Cancers

2024

Jump to: 2023, 2022, 2021

13 pages, 1858 KiB

Open AccessArticle

Longitudinal FDG-PET Radiomics for Early Prediction of Treatment Response to Chemoradiation in Locally Advanced Cervical Cancer: A Pilot Study

by Alejandro Cepero, Yidong Yang, Lori Young, Jianfeng Huang, Xuemei Ji and Fei Yang

Cancers 2024, 16(22), 3813; https://doi.org/10.3390/cancers16223813 - 13 Nov 2024

Viewed by 601

Abstract

Objectives: This study aimed to assess the capacity of longitudinal FDG-PET radiomics for early distinguishing between locally advanced cervical cancer (LACC) patients who responded to treatment and those who did not. Methods: FDG-PET scans were obtained before and midway through concurrent [...] Read more.

Objectives: This study aimed to assess the capacity of longitudinal FDG-PET radiomics for early distinguishing between locally advanced cervical cancer (LACC) patients who responded to treatment and those who did not. Methods: FDG-PET scans were obtained before and midway through concurrent chemoradiation for a study cohort of patients with LACC. Radiomics features related to image textures were extracted from the primary tumor volumes and stratified for relevance to treatment response status with the aid of random forest recursive feature elimination. Predictive models based on the k-nearest neighbors time series classifier were developed using the top-selected features to differentiate between responders and non-responders. The performance of the developed models was evaluated using receiver operating characteristic (ROC) curve analysis and n-fold cross-validation. Results: The top radiomics features extracted from scans taken midway through treatment showed significant differences between the two responder groups (p-values < 0.0005). In contrast, those from pretreatment scans did not exhibit significant differences. The AUC of the mean ROC curve for the predictive model based on the top features from pretreatment scans was 0.8529, while it reached 0.9420 for those derived midway through treatment scans. Conclusions: The study highlights the potential of longitudinal FDG-PET radiomics extracted midway through treatment for predicting response to chemoradiation in LACC patients and emphasizes that interim PET scans could be crucial in personalized medicine, ultimately enhancing therapeutic outcomes for LACC. Full article

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13 pages, 851 KiB

Open AccessArticle

AI Survival Prediction Modeling: The Importance of Considering Treatments and Changes in Health Status over Time

by Nabil Adam and Robert Wieder

Cancers 2024, 16(20), 3527; https://doi.org/10.3390/cancers16203527 - 18 Oct 2024

Viewed by 768

Abstract

Background and objectives: Deep learning (DL)-based models for predicting the survival of patients with local stages of breast cancer only use time-fixed covariates, i.e., patient and cancer data at the time of diagnosis. These predictions are inherently error-prone because they do not consider [...] Read more.

Background and objectives: Deep learning (DL)-based models for predicting the survival of patients with local stages of breast cancer only use time-fixed covariates, i.e., patient and cancer data at the time of diagnosis. These predictions are inherently error-prone because they do not consider time-varying events that occur after initial diagnosis. Our objective is to improve the predictive modeling of survival of patients with localized breast cancer to consider both time-fixed and time-varying events; thus, we take into account the progression of a patient’s health status over time. Methods: We extended four DL-based predictive survival models (DeepSurv, DeepHit, Nnet-survival, and Cox-Time) that deal with right-censored time-to-event data to consider not only a patient’s time-fixed covariates (patient and cancer data at diagnosis) but also a patient’s time-varying covariates (e.g., treatments, comorbidities, progressive age, frailty index, adverse events from treatment). We utilized, as our study data, the SEER-Medicare linked dataset from 1991 to 2016 to study a population of women diagnosed with stage I–III breast cancer (BC) enrolled in Medicare at 65 years or older as qualified by age. We delineated time-fixed variables recorded at the time of diagnosis, including age, race, marital status, breast cancer stage, tumor grade, laterality, estrogen receptor (ER), progesterone receptor (PR), and human epidermal receptor 2 (HER2) status, and comorbidity index. We analyzed six distinct prognostic categories, cancer stages I–III BC, and each stage’s ER/PR+ or ER/PR− status. At each visit, we delineated the time-varying covariates of administered treatments, induced adverse events, comorbidity index, and age. We predicted the survival of three hypothetical patients to demonstrate the model’s utility. Main Outcomes and Measures: The primary outcomes of the modeling were the measures of the model’s prediction error, as measured by the concordance index, the most commonly applied evaluation metric in survival analysis, and the integrated Brier score, a metric of the model’s discrimination and calibration. Results: The proposed extended patients’ covariates that include both time-fixed and time-varying covariates significantly improved the deep learning models’ prediction error and the discrimination and calibration of a model’s estimates. The prediction of the four DL models using time-fixed covariates in six different prognostic categories all resulted in approximately a 30% error in all six categories. When applying the proposed extension to include time-varying covariates, the accuracy of all four predictive models improved significantly, with the error decreasing to approximately 10%. The models’ predictive accuracy was independent of the differing published survival predictions from time-fixed covariates in the six prognostic categories. We demonstrate the utility of the model in three hypothetical patients with unique patient, cancer, and treatment variables. The model predicted survival based on the patient’s individual time-fixed and time-varying features, which varied considerably from Social Security age-based, and stage and race-based breast cancer survival predictions. Conclusions: The predictive modeling of the survival of patients with early-stage breast cancer using DL models has a prediction error of around 30% when considering only time-fixed covariates at the time of diagnosis and decreases to values under 10% when time-varying covariates are added as input to the models, regardless of the prognostic category of the patient groups. These models can be used to predict individual patients’ survival probabilities based on their unique repertoire of time-fixed and time-varying features. They will provide guidance for patients and their caregivers to assist in decision making. Full article

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22 pages, 2401 KiB

Open AccessArticle

Early 2-Factor Transcription Factors Associated with Progression and Recurrence in Bevacizumab-Responsive Subtypes of Glioblastoma

by Jian Shi

Cancers 2024, 16(14), 2536; https://doi.org/10.3390/cancers16142536 - 14 Jul 2024

Cited by 1 | Viewed by 1012

Abstract

The early 2-factor (E2F) family of transcription factors, including E2F1 through 8, plays a critical role in apoptosis, metabolism, proliferation, and angiogenesis within glioblastoma (GBM). However, the specific functions of E2F transcription factors (E2Fs) and their impact on the malignancy of Bevacizumab (BVZ)-responsive [...] Read more.

The early 2-factor (E2F) family of transcription factors, including E2F1 through 8, plays a critical role in apoptosis, metabolism, proliferation, and angiogenesis within glioblastoma (GBM). However, the specific functions of E2F transcription factors (E2Fs) and their impact on the malignancy of Bevacizumab (BVZ)-responsive GBM subtypes remain unclear. This study used data from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), European Molecular Biology Laboratory’s European Bioinformatics Institute (EMBL-EBI), and Gene Expression Omnibus (GEO) to explore the impact of eight E2F family members on the clinical characteristics of BVZ-responsive GBM subtypes and possible mechanisms of recurrence after BVZ treatment. Using machine learning algorithms, including TreeBagger and deep neural networks, we systematically predicted and validated GBM patient survival terms based on the expression profiles of E2Fs across BVZ-responsive GBM subtypes. Our bioinformatics analyses suggested that a significant increase in E2F8 post-BVZ treatment may enhance the function of angiogenesis and stem cell proliferation, implicating this factor as a candidate mechanism of GBM recurrence after treatment. In addition, BVZ treatment in unresponsive GBM patients may potentially worsen disease progression. These insights underscore that E2F family members play important roles in GBM malignancy and BVZ treatment response, highlighting their potential as prognostic biomarkers, therapeutic targets, and recommending precision BVZ treatment to individual GBM patients. Full article

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13 pages, 2137 KiB

Open AccessArticle

Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology

by Aadhi Aadhavan Balasubramanian, Salah Mohammed Awad Al-Heejawi, Akarsh Singh, Anne Breggia, Bilal Ahmad, Robert Christman, Stephen T. Ryan and Saeed Amal

Cancers 2024, 16(12), 2222; https://doi.org/10.3390/cancers16122222 - 14 Jun 2024

Cited by 3 | Viewed by 3867

Abstract

Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, a comprehensive approach is presented utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets were based on two widely employed datasets from different [...] Read more.

Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, a comprehensive approach is presented utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets were based on two widely employed datasets from different centers for two different tasks: BACH and BreakHis. Within the BACH dataset, a proposed ensemble strategy was employed, incorporating VGG16 and ResNet50 architectures to achieve precise classification of breast cancer histopathology images. Introducing a novel image patching technique to preprocess a high-resolution image facilitated a focused analysis of localized regions of interest. The annotated BACH dataset encompassed 400 WSIs across four distinct classes: Normal, Benign, In Situ Carcinoma, and Invasive Carcinoma. In addition, the proposed ensemble was used on the BreakHis dataset, utilizing VGG16, ResNet34, and ResNet50 models to classify microscopic images into eight distinct categories (four benign and four malignant). For both datasets, a five-fold cross-validation approach was employed for rigorous training and testing. Preliminary experimental results indicated a patch classification accuracy of 95.31% (for the BACH dataset) and WSI image classification accuracy of 98.43% (BreakHis). This research significantly contributes to ongoing endeavors in harnessing artificial intelligence to advance breast cancer diagnosis, potentially fostering improved patient outcomes and alleviating healthcare burdens. Full article

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14 pages, 2546 KiB

Open AccessArticle

Radiotherapy Plan Quality Assurance in NRG Oncology Trials for Brain and Head/Neck Cancers: An AI-Enhanced Knowledge-Based Approach

by Du Wang, Huaizhi Geng, Vinai Gondi, Nancy Y. Lee, Christina I. Tsien, Ping Xia, Thomas L. Chenevert, Jeff M. Michalski, Mark R. Gilbert, Quynh-Thu Le, Antonio M. Omuro, Kuo Men, Kenneth D. Aldape, Yue Cao, Ashok Srinivasan, Igor J. Barani, Sean Sachdev, Jiayi Huang, Serah Choi, Wenyin Shi, James D. Battiste, Zabi Wardak, Michael D. Chan, Minesh P. Mehta and Ying Xiao Show full author list Hide full author list

Cancers 2024, 16(11), 2007; https://doi.org/10.3390/cancers16112007 - 25 May 2024

Viewed by 1361

Abstract

The quality of radiation therapy (RT) treatment plans directly affects the outcomes of clinical trials. KBP solutions have been utilized in RT plan quality assurance (QA). In this study, we evaluated the quality of RT plans for brain and head/neck cancers enrolled in [...] Read more.

The quality of radiation therapy (RT) treatment plans directly affects the outcomes of clinical trials. KBP solutions have been utilized in RT plan quality assurance (QA). In this study, we evaluated the quality of RT plans for brain and head/neck cancers enrolled in multi-institutional clinical trials utilizing a KBP approach. The evaluation was conducted on 203 glioblastoma (GBM) patients enrolled in NRG-BN001 and 70 nasopharyngeal carcinoma (NPC) patients enrolled in NRG-HN001. For each trial, fifty high-quality photon plans were utilized to build a KBP photon model. A KBP proton model was generated using intensity-modulated proton therapy (IMPT) plans generated on 50 patients originally treated with photon RT. These models were then applied to generate KBP plans for the remaining patients, which were compared against the submitted plans for quality evaluation, including in terms of protocol compliance, target coverage, and organ-at-risk (OAR) doses. RT plans generated by the KBP models were demonstrated to have superior quality compared to the submitted plans. KBP IMPT plans can decrease the variation of proton plan quality and could possibly be used as a tool for developing improved plans in the future. Additionally, the KBP tool proved to be an effective instrument for RT plan QA in multi-center clinical trials. Full article

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8 pages, 583 KiB

Open AccessArticle

CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology

by Dania Daye, Regina Parker, Satvik Tripathi, Meredith Cox, Sebastian Brito Orama, Leonardo Valentin, Christopher P. Bridge and Raul N. Uppot

Cancers 2024, 16(11), 1975; https://doi.org/10.3390/cancers16111975 - 23 May 2024

Viewed by 1001

Abstract

This study addresses the potential of machine learning in predicting treatment recommendations for patients with hepatocellular carcinoma (HCC). Using an IRB-approved retrospective study of patients discussed at a multidisciplinary tumor board, clinical and imaging variables were extracted and used in a gradient-boosting machine [...] Read more.

This study addresses the potential of machine learning in predicting treatment recommendations for patients with hepatocellular carcinoma (HCC). Using an IRB-approved retrospective study of patients discussed at a multidisciplinary tumor board, clinical and imaging variables were extracted and used in a gradient-boosting machine learning algorithm, XGBoost. The algorithm’s performance was assessed using confusion matrix metrics and the area under the Receiver Operating Characteristics (ROC) curve. The study included 140 patients (mean age 67.7 ± 8.9 years), and the algorithm was found to be predictive of all eight treatment recommendations made by the board. The model’s predictions were more accurate than those based on published therapeutic guidelines by ESMO and NCCN. The study concludes that a machine learning model incorporating clinical and imaging variables can predict treatment recommendations made by an expert multidisciplinary tumor board, potentially aiding clinical decision-making in settings lacking subspecialty expertise. Full article

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17 pages, 2502 KiB

Open AccessArticle

The Prediction of Stress in Radiation Therapy: Integrating Artificial Intelligence with Biological Signals

by Sangwoon Jeong, Hongryull Pyo, Won Park and Youngyih Han

Cancers 2024, 16(11), 1964; https://doi.org/10.3390/cancers16111964 - 22 May 2024

Viewed by 985

Abstract

This study aimed to predict stress in patients using artificial intelligence (AI) from biological signals and verify the effect of stress on respiratory irregularity. We measured 123 cases in 41 patients and calculated stress scores with seven stress-related features derived from heart-rate variability. [...] Read more.

This study aimed to predict stress in patients using artificial intelligence (AI) from biological signals and verify the effect of stress on respiratory irregularity. We measured 123 cases in 41 patients and calculated stress scores with seven stress-related features derived from heart-rate variability. The distribution and trends of stress scores across the treatment period were analyzed. Before-treatment information was used to predict the stress features during treatment. AI models included both non-pretrained (decision tree, random forest, support vector machine, long short-term memory (LSTM), and transformer) and pretrained (ChatGPT) models. Performance was evaluated using 10-fold cross-validation, exact match ratio, accuracy, recall, precision, and F1 score. Respiratory irregularities were calculated in phase and amplitude and analyzed for correlation with stress score. Over 90% of the patients experienced stress during radiation therapy. LSTM and prompt engineering GPT4.0 had the highest accuracy (feature classification, LSTM: 0.703, GPT4.0: 0.659; stress classification, LSTM: 0.846, GPT4.0: 0.769). A 10% increase in stress score was associated with a 0.286 higher phase irregularity (p < 0.025). Our research pioneers the use of AI and biological signals for stress prediction in patients undergoing radiation therapy, potentially identifying those needing psychological support and suggesting methods to improve radiotherapy effectiveness through stress management. Full article

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18 pages, 3487 KiB

Open AccessSystematic Review

Performance of Commercial Dermatoscopic Systems That Incorporate Artificial Intelligence for the Identification of Melanoma in General Practice: A Systematic Review

by Ian Miller, Nedeljka Rosic, Michael Stapelberg, Jeremy Hudson, Paul Coxon, James Furness, Joe Walsh and Mike Climstein

Cancers 2024, 16(7), 1443; https://doi.org/10.3390/cancers16071443 - 8 Apr 2024

Viewed by 2312

Abstract

Background: Cutaneous melanoma remains an increasing global public health burden, particularly in fair-skinned populations. Advancing technologies, particularly artificial intelligence (AI), may provide an additional tool for clinicians to help detect malignancies with a more accurate success rate. This systematic review aimed to report [...] Read more.

Background: Cutaneous melanoma remains an increasing global public health burden, particularly in fair-skinned populations. Advancing technologies, particularly artificial intelligence (AI), may provide an additional tool for clinicians to help detect malignancies with a more accurate success rate. This systematic review aimed to report the performance metrics of commercially available convolutional neural networks (CNNs) tasked with detecting MM. Methods: A systematic literature search was performed using CINAHL, Medline, Scopus, ScienceDirect and Web of Science databases. Results: A total of 16 articles reporting MM were included in this review. The combined number of melanomas detected was 1160, and non-melanoma lesions were 33,010. The performance of market-approved technology and clinician performance for classifying melanoma was highly heterogeneous, with sensitivity ranging from 16.4 to 100.0%, specificity between 40.0 and 98.3% and accuracy between 44.0 and 92.0%. Less heterogeneity was observed when clinicians worked in unison with AI, with sensitivity ranging between 83.3 and 100.0%, specificity between 83.7 and 87.3%, and accuracy between 86.4 and 86.9%. Conclusion: Instead of focusing on the performance of AI versus clinicians for classifying melanoma, more consistent performance has been obtained when clinicians’ work is supported by AI, facilitating management decisions and improving health outcomes. Full article

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17 pages, 4021 KiB

Open AccessArticle

Deep-Learning-Based Predictive Imaging Biomarker Model for EGFR Mutation Status in Non-Small Cell Lung Cancer from CT Imaging

by Abhishek Mahajan, Vatsal Kania, Ujjwal Agarwal, Renuka Ashtekar, Shreya Shukla, Vijay Maruti Patil, Vanita Noronha, Amit Joshi, Nandini Menon, Rajiv Kumar Kaushal, Swapnil Rane, Anuradha Chougule, Suthirth Vaidya, Krishna Kaluva and Kumar Prabhash

Cancers 2024, 16(6), 1130; https://doi.org/10.3390/cancers16061130 - 12 Mar 2024

Cited by 4 | Viewed by 2241

Abstract

Purpose: The authors aimed to develop and validate deep-learning-based radiogenomic (DLR) models and radiomic signatures to predict the EGFR mutation in patients with NSCLC, and to assess the semantic and clinical features that can contribute to detecting EGFR mutations. Methods: Using 990 patients [...] Read more.

Purpose: The authors aimed to develop and validate deep-learning-based radiogenomic (DLR) models and radiomic signatures to predict the EGFR mutation in patients with NSCLC, and to assess the semantic and clinical features that can contribute to detecting EGFR mutations. Methods: Using 990 patients from two NSCLC trials, we employed an end-to-end pipeline analyzing CT images without precise segmentation. Two 3D convolutional neural networks segmented lung masses and nodules. Results: The combined radiomics and DLR model achieved an AUC of 0.88 ± 0.03 in predicting EGFR mutation status, outperforming individual models. Semantic features further improved the model’s accuracy, with an AUC of 0.88 ± 0.05. CT semantic features that were found to be significantly associated with EGFR mutations were pure solid tumours with no associated ground glass component (p < 0.03), the absence of peripheral emphysema (p < 0.03), the presence of pleural retraction (p = 0.004), the presence of fissure attachment (p = 0.001), the presence of metastatic nodules in both the tumour-containing lobe (p = 0.001) and the non-tumour-containing lobe (p = 0.001), the presence of ipsilateral pleural effusion (p = 0.04), and average enhancement of the tumour mass above 54 HU (p < 0.001). Conclusions: This AI-based radiomics and DLR model demonstrated high accuracy in predicting EGFR mutation, serving as a non-invasive and user-friendly imaging biomarker for EGFR mutation status prediction. Full article

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23 pages, 2274 KiB

Open AccessReview

Integrating Artificial Intelligence for Advancing Multiple-Cancer Early Detection via Serum Biomarkers: A Narrative Review

by Hsin-Yao Wang, Wan-Ying Lin, Chenfei Zhou, Zih-Ang Yang, Sriram Kalpana and Michael S. Lebowitz

Cancers 2024, 16(5), 862; https://doi.org/10.3390/cancers16050862 - 21 Feb 2024

Cited by 4 | Viewed by 3424

Abstract

The concept and policies of multicancer early detection (MCED) have gained significant attention from governments worldwide in recent years. In the era of burgeoning artificial intelligence (AI) technology, the integration of MCED with AI has become a prevailing trend, giving rise to a [...] Read more.

The concept and policies of multicancer early detection (MCED) have gained significant attention from governments worldwide in recent years. In the era of burgeoning artificial intelligence (AI) technology, the integration of MCED with AI has become a prevailing trend, giving rise to a plethora of MCED AI products. However, due to the heterogeneity of both the detection targets and the AI technologies, the overall diversity of MCED AI products remains considerable. The types of detection targets encompass protein biomarkers, cell-free DNA, or combinations of these biomarkers. In the development of AI models, different model training approaches are employed, including datasets of case-control studies or real-world cancer screening datasets. Various validation techniques, such as cross-validation, location-wise validation, and time-wise validation, are used. All of the factors show significant impacts on the predictive efficacy of MCED AIs. After the completion of AI model development, deploying the MCED AIs in clinical practice presents numerous challenges, including presenting the predictive reports, identifying the potential locations and types of tumors, and addressing cancer-related information, such as clinical follow-up and treatment. This study reviews several mature MCED AI products currently available in the market, detecting their composing factors from serum biomarker detection, MCED AI training/validation, and the clinical application. This review illuminates the challenges encountered by existing MCED AI products across these stages, offering insights into the continued development and obstacles within the field of MCED AI. Full article

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16 pages, 2070 KiB

Open AccessReview

The Rise of Hypothesis-Driven Artificial Intelligence in Oncology

by Zilin Xianyu, Cristina Correia, Choong Yong Ung, Shizhen Zhu, Daniel D. Billadeau and Hu Li

Cancers 2024, 16(4), 822; https://doi.org/10.3390/cancers16040822 - 18 Feb 2024

Cited by 2 | Viewed by 5260

Abstract

Cancer is a complex disease involving the deregulation of intricate cellular systems beyond genetic aberrations and, as such, requires sophisticated computational approaches and high-dimensional data for optimal interpretation. While conventional artificial intelligence (AI) models excel in many prediction tasks, they often lack interpretability [...] Read more.

Cancer is a complex disease involving the deregulation of intricate cellular systems beyond genetic aberrations and, as such, requires sophisticated computational approaches and high-dimensional data for optimal interpretation. While conventional artificial intelligence (AI) models excel in many prediction tasks, they often lack interpretability and are blind to the scientific hypotheses generated by researchers to enable cancer discoveries. Here we propose that hypothesis-driven AI, a new emerging class of AI algorithm, is an innovative approach to uncovering the complex etiology of cancer from big omics data. This review exemplifies how hypothesis-driven AI is different from conventional AI by citing its application in various areas of oncology including tumor classification, patient stratification, cancer gene discovery, drug response prediction, and tumor spatial organization. Our aim is to stress the feasibility of incorporating domain knowledge and scientific hypotheses to craft the design of new AI algorithms. We showcase the power of hypothesis-driven AI in making novel cancer discoveries that can be overlooked by conventional AI methods. Since hypothesis-driven AI is still in its infancy, open questions such as how to better incorporate new knowledge and biological perspectives to ameliorate bias and improve interpretability in the design of AI algorithms still need to be addressed. In conclusion, hypothesis-driven AI holds great promise in the discovery of new mechanistic and functional insights that explain the complexity of cancer etiology and potentially chart a new roadmap to improve treatment regimens for individual patients. Full article

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2023

Jump to: 2024, 2022, 2021

14 pages, 1060 KiB

Open AccessReview

Breaking Barriers: AI’s Influence on Pathology and Oncology in Resource-Scarce Medical Systems

by Alon Vigdorovits, Maria Magdalena Köteles, Gheorghe-Emilian Olteanu and Ovidiu Pop

Cancers 2023, 15(23), 5692; https://doi.org/10.3390/cancers15235692 - 2 Dec 2023

Viewed by 2277

Abstract

The application of artificial intelligence to improve the access of cancer patients to high-quality medical care is one of the goals of modern medicine. Pathology constitutes the foundation of modern oncologic treatment, and its role has expanded far beyond diagnosis into predicting treatment [...] Read more.

The application of artificial intelligence to improve the access of cancer patients to high-quality medical care is one of the goals of modern medicine. Pathology constitutes the foundation of modern oncologic treatment, and its role has expanded far beyond diagnosis into predicting treatment response and overall survival. However, the funding of pathology is often an afterthought in resource-scarce medical systems. The increased digitalization of pathology has paved the way towards the potential use of artificial intelligence tools for improving pathologist efficiency and extracting more information from tissues. In this review, we provide an overview of the main research directions intersecting with artificial intelligence and pathology in relation to oncology, such as tumor classification, the prediction of molecular alterations, and biomarker quantification. We then discuss examples of tools that have matured into clinical products and gained regulatory approval for clinical use. Finally, we highlight the main hurdles that stand in the way of the digitalization of pathology and the application of artificial intelligence in pathology while also discussing possible solutions. Full article

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13 pages, 1764 KiB

Open AccessArticle

Radiomics and Clinicopathological Characteristics for Predicting Lymph Node Metastasis in Testicular Cancer

by Catharina Silvia Lisson, Sabitha Manoj, Daniel Wolf, Christoph Gerhard Lisson, Stefan A. Schmidt, Meinrad Beer, Wolfgang Thaiss, Christian Bolenz, Friedemann Zengerling and Michael Goetz

Cancers 2023, 15(23), 5630; https://doi.org/10.3390/cancers15235630 - 29 Nov 2023

Cited by 5 | Viewed by 1590

Abstract

Accurate prediction of lymph node metastasis (LNM) in patients with testicular cancer is highly relevant for treatment decision-making and prognostic evaluation. Our study aimed to develop and validate clinical radiomics models for individual preoperative prediction of LNM in patients with testicular cancer. We [...] Read more.

Accurate prediction of lymph node metastasis (LNM) in patients with testicular cancer is highly relevant for treatment decision-making and prognostic evaluation. Our study aimed to develop and validate clinical radiomics models for individual preoperative prediction of LNM in patients with testicular cancer. We enrolled 91 patients with clinicopathologically confirmed early-stage testicular cancer, with disease confined to the testes. We included five significant clinical risk factors (age, preoperative serum tumour markers AFP and B-HCG, histotype and BMI) to build the clinical model. After segmenting 273 retroperitoneal lymph nodes, we then combined the clinical risk factors and lymph node radiomics features to establish combined predictive models using Random Forest (RF), Light Gradient Boosting Machine (LGBM), Support Vector Machine Classifier (SVC), and K-Nearest Neighbours (KNN). Model performance was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). Finally, the decision curve analysis (DCA) was used to evaluate the clinical usefulness. The Random Forest combined clinical lymph node radiomics model with the highest AUC of 0.95 (±0.03 SD; 95% CI) was considered the candidate model with decision curve analysis, demonstrating its usefulness for preoperative prediction in the clinical setting. Our study has identified reliable and predictive machine learning techniques for predicting lymph node metastasis in early-stage testicular cancer. Identifying the most effective machine learning approaches for predictive analysis based on radiomics integrating clinical risk factors can expand the applicability of radiomics in precision oncology and cancer treatment. Full article

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15 pages, 6752 KiB

Open AccessArticle

An Interpretable Radiomics Model Based on Two-Dimensional Shear Wave Elastography for Predicting Symptomatic Post-Hepatectomy Liver Failure in Patients with Hepatocellular Carcinoma

by Xian Zhong, Zohaib Salahuddin, Yi Chen, Henry C. Woodruff, Haiyi Long, Jianyun Peng, Xiaoyan Xie, Manxia Lin and Philippe Lambin

Cancers 2023, 15(21), 5303; https://doi.org/10.3390/cancers15215303 - 6 Nov 2023

Viewed by 1555

Abstract

Objective: The aim of this study was to develop and validate an interpretable radiomics model based on two-dimensional shear wave elastography (2D-SWE) for symptomatic post-hepatectomy liver failure (PHLF) prediction in patients undergoing liver resection for hepatocellular carcinoma (HCC). Methods: A total of 345 [...] Read more.

Objective: The aim of this study was to develop and validate an interpretable radiomics model based on two-dimensional shear wave elastography (2D-SWE) for symptomatic post-hepatectomy liver failure (PHLF) prediction in patients undergoing liver resection for hepatocellular carcinoma (HCC). Methods: A total of 345 consecutive patients were enrolled. A five-fold cross-validation was performed during training, and the models were evaluated in the independent test cohort. A multi-patch radiomics model was established based on the 2D-SWE images for predicting symptomatic PHLF. Clinical features were incorporated into the models to train the clinical–radiomics model. The radiomics model and the clinical–radiomics model were compared with the clinical model comprising clinical variables and other clinical predictive indices, including the model for end-stage liver disease (MELD) score and albumin–bilirubin (ALBI) score. Shapley Additive exPlanations (SHAP) was used for post hoc interpretability of the radiomics model. Results: The clinical–radiomics model achieved an AUC of 0.867 (95% CI 0.787–0.947) in the five-fold cross-validation, and this score was higher than that of the clinical model (AUC: 0.809; 95% CI: 0.715–0.902) and the radiomics model (AUC: 0.746; 95% CI: 0.681–0.811). The clinical–radiomics model showed an AUC of 0.822 in the test cohort, higher than that of the clinical model (AUC: 0.684, p = 0.007), radiomics model (AUC: 0.784, p = 0.415), MELD score (AUC: 0.529, p < 0.001), and ALBI score (AUC: 0.644, p = 0.016). The SHAP analysis showed that the first-order radiomics features, including first-order maximum 64 × 64, first-order 90th percentile 64 × 64, and first-order 10th percentile 32 × 32, were the most important features for PHLF prediction. Conclusion: An interpretable clinical–radiomics model based on 2D-SWE and clinical variables can help in predicting symptomatic PHLF in HCC. Full article

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16 pages, 1218 KiB

Open AccessReview

Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes

by Zainab Gandhi, Priyatham Gurram, Birendra Amgai, Sai Prasanna Lekkala, Alifya Lokhandwala, Suvidha Manne, Adil Mohammed, Hiren Koshiya, Nakeya Dewaswala, Rupak Desai, Huzaifa Bhopalwala, Shyam Ganti and Salim Surani

Cancers 2023, 15(21), 5236; https://doi.org/10.3390/cancers15215236 - 31 Oct 2023

Cited by 18 | Viewed by 9070

Abstract

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for improved diagnostic and treatment approaches. In recent years, the emergence of artificial intelligence (AI) has sparked considerable interest in its potential role in lung cancer. This review [...] Read more.

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for improved diagnostic and treatment approaches. In recent years, the emergence of artificial intelligence (AI) has sparked considerable interest in its potential role in lung cancer. This review aims to provide an overview of the current state of AI applications in lung cancer screening, diagnosis, and treatment. AI algorithms like machine learning, deep learning, and radiomics have shown remarkable capabilities in the detection and characterization of lung nodules, thereby aiding in accurate lung cancer screening and diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, and even chest radiographs, accurately identifying suspicious nodules and facilitating timely intervention. AI models have exhibited promise in utilizing biomarkers and tumor markers as supplementary screening tools, effectively enhancing the specificity and accuracy of early detection. These models can accurately distinguish between benign and malignant lung nodules, assisting radiologists in making more accurate and informed diagnostic decisions. Additionally, AI algorithms hold the potential to integrate multiple imaging modalities and clinical data, providing a more comprehensive diagnostic assessment. By utilizing high-quality data, including patient demographics, clinical history, and genetic profiles, AI models can predict treatment responses and guide the selection of optimal therapies. Notably, these models have shown considerable success in predicting the likelihood of response and recurrence following targeted therapies and optimizing radiation therapy for lung cancer patients. Implementing these AI tools in clinical practice can aid in the early diagnosis and timely management of lung cancer and potentially improve outcomes, including the mortality and morbidity of the patients. Full article

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13 pages, 1856 KiB

Open AccessArticle

Machine Learning-Based Prediction of Distant Recurrence in Invasive Breast Carcinoma Using Clinicopathological Data: A Cross-Institutional Study

by Shrey S. Sukhadia, Kristen E. Muller, Adrienne A. Workman and Shivashankar H. Nagaraj

Cancers 2023, 15(15), 3960; https://doi.org/10.3390/cancers15153960 - 3 Aug 2023

Cited by 1 | Viewed by 2218

Abstract

Breast cancer is the most common type of cancer worldwide. Alarmingly, approximately 30% of breast cancer cases result in disease recurrence at distant organs after treatment. Distant recurrence is more common in some subtypes such as invasive breast carcinoma (IBC). While clinicians have [...] Read more.

Breast cancer is the most common type of cancer worldwide. Alarmingly, approximately 30% of breast cancer cases result in disease recurrence at distant organs after treatment. Distant recurrence is more common in some subtypes such as invasive breast carcinoma (IBC). While clinicians have utilized several clinicopathological measurements to predict distant recurrences in IBC, no studies have predicted distant recurrences by combining clinicopathological evaluations of IBC tumors pre- and post-therapy with machine learning (ML) models. The goal of our study was to determine whether classification-based ML techniques could predict distant recurrences in IBC patients using key clinicopathological measurements, including pathological staging of the tumor and surrounding lymph nodes assessed both pre- and post-neoadjuvant therapy, response to therapy via standard-of-care imaging, and binary status of adjuvant therapy administered to patients. We trained and tested four clinicopathological ML models using a dataset (144 and 17 patients for training and testing, respectively) from Duke University and validated the best-performing model using an external dataset (8 patients) from Dartmouth Hitchcock Medical Center. The random forest model performed better than the C-support vector classifier, multilayer perceptron, and logistic regression models, yielding AUC values of 1.0 in the testing set and 0.75 in the validation set (p < 0.002) across both institutions, thereby demonstrating the cross-institutional portability and validity of ML models in the field of clinical research in cancer. The top-ranking clinicopathological measurement impacting the prediction of distant recurrences in IBC were identified to be tumor response to neoadjuvant therapy as evaluated via SOC imaging and pathology, which included tumor as well as node staging. Full article

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27 pages, 2379 KiB

Open AccessReview

Advancements in MRI-Based Radiomics and Artificial Intelligence for Prostate Cancer: A Comprehensive Review and Future Prospects

by Ahmad Chaddad, Guina Tan, Xiaojuan Liang, Lama Hassan, Saima Rathore, Christian Desrosiers, Yousef Katib and Tamim Niazi

Cancers 2023, 15(15), 3839; https://doi.org/10.3390/cancers15153839 - 28 Jul 2023

Cited by 18 | Viewed by 3930

Abstract

The use of multiparametric magnetic resonance imaging (mpMRI) has become a common technique used in guiding biopsy and developing treatment plans for prostate lesions. While this technique is effective, non-invasive methods such as radiomics have gained popularity for extracting imaging features to develop [...] Read more.

The use of multiparametric magnetic resonance imaging (mpMRI) has become a common technique used in guiding biopsy and developing treatment plans for prostate lesions. While this technique is effective, non-invasive methods such as radiomics have gained popularity for extracting imaging features to develop predictive models for clinical tasks. The aim is to minimize invasive processes for improved management of prostate cancer (PCa). This study reviews recent research progress in MRI-based radiomics for PCa, including the radiomics pipeline and potential factors affecting personalized diagnosis. The integration of artificial intelligence (AI) with medical imaging is also discussed, in line with the development trend of radiogenomics and multi-omics. The survey highlights the need for more data from multiple institutions to avoid bias and generalize the predictive model. The AI-based radiomics model is considered a promising clinical tool with good prospects for application. Full article

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13 pages, 4427 KiB

Open AccessArticle

Development and Validation of a Nomogram for Preoperative Prediction of Early Recurrence after Upfront Surgery in Pancreatic Ductal Adenocarcinoma by Integrating Deep Learning and Radiological Variables

by Fei Xiang, Xiang He, Xingyu Liu, Xinming Li, Xuchang Zhang, Yingfang Fan and Sheng Yan

Cancers 2023, 15(14), 3543; https://doi.org/10.3390/cancers15143543 - 8 Jul 2023

Cited by 3 | Viewed by 1381

Abstract

Around 80% of pancreatic ductal adenocarcinoma (PDAC) patients experience recurrence after curative resection. We aimed to develop a deep-learning model based on preoperative CT images to predict early recurrence (recurrence within 12 months) in PDAC patients. The retrospective study included 435 patients with [...] Read more.

Around 80% of pancreatic ductal adenocarcinoma (PDAC) patients experience recurrence after curative resection. We aimed to develop a deep-learning model based on preoperative CT images to predict early recurrence (recurrence within 12 months) in PDAC patients. The retrospective study included 435 patients with PDAC from two independent centers. A modified 3D-ResNet18 network was used for a deep learning model construction. A nomogram was constructed by incorporating deep learning model outputs and independent preoperative radiological predictors. The deep learning model provided the area under the receiver operating curve (AUC) values of 0.836, 0.736, and 0.720 in the development, internal, and external validation datasets for early recurrence prediction, respectively. Multivariate logistic analysis revealed that higher deep learning model outputs (odds ratio [OR]: 1.675; 95% CI: 1.467, 1.950; p < 0.001), cN1/2 stage (OR: 1.964; 95% CI: 1.036, 3.774; p = 0.040), and arterial involvement (OR: 2.207; 95% CI: 1.043, 4.873; p = 0.043) were independent risk factors associated with early recurrence and were used to build an integrated nomogram. The nomogram yielded AUC values of 0.855, 0.752, and 0.741 in the development, internal, and external validation datasets. In conclusion, the proposed nomogram may help predict early recurrence in PDAC patients. Full article

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20 pages, 5482 KiB

Open AccessArticle

Controllability and Robustness of Functional and Structural Connectomic Networks in Glioma Patients

by Anke Meyer-Baese, Kerstin Jütten, Uwe Meyer-Baese, Ali Moradi Amani, Hagen Malberg, Andreas Stadlbauer, Thomas Kinfe and Chuh-Hyoun Na

Cancers 2023, 15(10), 2714; https://doi.org/10.3390/cancers15102714 - 11 May 2023

Cited by 3 | Viewed by 1748

Abstract

Previous studies suggest that the topological properties of structural and functional neural networks in glioma patients are altered beyond the tumor location. These alterations are due to the dynamic interactions with large-scale neural circuits. Understanding and describing these interactions may be an important [...] Read more.

Previous studies suggest that the topological properties of structural and functional neural networks in glioma patients are altered beyond the tumor location. These alterations are due to the dynamic interactions with large-scale neural circuits. Understanding and describing these interactions may be an important step towards deciphering glioma disease evolution. In this study, we analyze structural and functional brain networks in terms of determining the correlation between network robustness and topological features regarding the default-mode network (DMN), comparing prognostically differing patient groups to healthy controls. We determine the driver nodes of these networks, which are receptive to outside signals, and the critical nodes as the most important elements for controllability since their removal will dramatically affect network controllability. Our results suggest that network controllability and robustness of the DMN is decreased in glioma patients. We found losses of driver and critical nodes in patients, especially in the prognostically less favorable IDH wildtype (IDHwt) patients, which might reflect lesion-induced network disintegration. On the other hand, topological shifts of driver and critical nodes, and even increases in the number of critical nodes, were observed mainly in IDH mutated (IDHmut) patients, which might relate to varying degrees of network plasticity accompanying the chronic disease course in some of the patients, depending on tumor growth dynamics. We hereby implement a novel approach for further exploring disease evolution in brain cancer under the aspects of neural network controllability and robustness in glioma patients. Full article

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22 pages, 1811 KiB

Open AccessReview

Artificial Intelligence in CT and MR Imaging for Oncological Applications

by Ramesh Paudyal, Akash D. Shah, Oguz Akin, Richard K. G. Do, Amaresha Shridhar Konar, Vaios Hatzoglou, Usman Mahmood, Nancy Lee, Richard J. Wong, Suchandrima Banerjee, Jaemin Shin, Harini Veeraraghavan and Amita Shukla-Dave

Cancers 2023, 15(9), 2573; https://doi.org/10.3390/cancers15092573 - 30 Apr 2023

Cited by 22 | Viewed by 5243

Abstract

Cancer care increasingly relies on imaging for patient management. The two most common cross-sectional imaging modalities in oncology are computed tomography (CT) and magnetic resonance imaging (MRI), which provide high-resolution anatomic and physiological imaging. Herewith is a summary of recent applications of rapidly [...] Read more.

Cancer care increasingly relies on imaging for patient management. The two most common cross-sectional imaging modalities in oncology are computed tomography (CT) and magnetic resonance imaging (MRI), which provide high-resolution anatomic and physiological imaging. Herewith is a summary of recent applications of rapidly advancing artificial intelligence (AI) in CT and MRI oncological imaging that addresses the benefits and challenges of the resultant opportunities with examples. Major challenges remain, such as how best to integrate AI developments into clinical radiology practice, the vigorous assessment of quantitative CT and MR imaging data accuracy, and reliability for clinical utility and research integrity in oncology. Such challenges necessitate an evaluation of the robustness of imaging biomarkers to be included in AI developments, a culture of data sharing, and the cooperation of knowledgeable academics with vendor scientists and companies operating in radiology and oncology fields. Herein, we will illustrate a few challenges and solutions of these efforts using novel methods for synthesizing different contrast modality images, auto-segmentation, and image reconstruction with examples from lung CT as well as abdome, pelvis, and head and neck MRI. The imaging community must embrace the need for quantitative CT and MRI metrics beyond lesion size measurement. AI methods for the extraction and longitudinal tracking of imaging metrics from registered lesions and understanding the tumor environment will be invaluable for interpreting disease status and treatment efficacy. This is an exciting time to work together to move the imaging field forward with narrow AI-specific tasks. New AI developments using CT and MRI datasets will be used to improve the personalized management of cancer patients. Full article

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Graphical abstract

16 pages, 2557 KiB

Open AccessArticle

Melanoma Clinical Decision Support System: An Artificial Intelligence-Based Tool to Diagnose and Predict Disease Outcome in Early-Stage Melanoma Patients

by Jose Luis Diaz-Ramón, Jesus Gardeazabal, Rosa Maria Izu, Estibaliz Garrote, Javier Rasero, Aintzane Apraiz, Cristina Penas, Sandra Seijo, Cristina Lopez-Saratxaga, Pedro Maria De la Peña, Ana Sanchez-Diaz, Goikoane Cancho-Galan, Veronica Velasco, Arrate Sevilla, David Fernandez, Iciar Cuenca, Jesus María Cortes, Santos Alonso, Aintzane Asumendi and María Dolores Boyano

Cancers 2023, 15(7), 2174; https://doi.org/10.3390/cancers15072174 - 6 Apr 2023

Cited by 6 | Viewed by 3333

Abstract

This study set out to assess the performance of an artificial intelligence (AI) algorithm based on clinical data and dermatoscopic imaging for the early diagnosis of melanoma, and its capacity to define the metastatic progression of melanoma through serological and histopathological biomarkers, enabling [...] Read more.

This study set out to assess the performance of an artificial intelligence (AI) algorithm based on clinical data and dermatoscopic imaging for the early diagnosis of melanoma, and its capacity to define the metastatic progression of melanoma through serological and histopathological biomarkers, enabling dermatologists to make more informed decisions about patient management. Integrated analysis of demographic data, images of the skin lesions, and serum and histopathological markers were analyzed in a group of 196 patients with melanoma. The interleukins (ILs) IL-4, IL-6, IL-10, and IL-17A as well as IFNγ (interferon), GM-CSF (granulocyte and macrophage colony-stimulating factor), TGFβ (transforming growth factor), and the protein DCD (dermcidin) were quantified in the serum of melanoma patients at the time of diagnosis, and the expression of the RKIP, PIRIN, BCL2, BCL3, MITF, and ANXA5 proteins was detected by immunohistochemistry (IHC) in melanoma biopsies. An AI algorithm was used to improve the early diagnosis of melanoma and to predict the risk of metastasis and of disease-free survival. Two models were obtained to predict metastasis (including “all patients” or only patients “at early stages of melanoma”), and a series of attributes were seen to predict the progression of metastasis: Breslow thickness, infiltrating BCL-2 expressing lymphocytes, and IL-4 and IL-6 serum levels. Importantly, a decrease in serum GM-CSF seems to be a marker of poor prognosis in patients with early-stage melanomas. Full article

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18 pages, 5690 KiB

Open AccessArticle

Empirical Study of Overfitting in Deep Learning for Predicting Breast Cancer Metastasis

by Chuhan Xu, Pablo Coen-Pirani and Xia Jiang

Cancers 2023, 15(7), 1969; https://doi.org/10.3390/cancers15071969 - 25 Mar 2023

Cited by 11 | Viewed by 2587

Abstract

Overfitting may affect the accuracy of predicting future data because of weakened generalization. In this research, we used an electronic health records (EHR) dataset concerning breast cancer metastasis to study the overfitting of deep feedforward neural networks (FNNs) prediction models. We studied how [...] Read more.

Overfitting may affect the accuracy of predicting future data because of weakened generalization. In this research, we used an electronic health records (EHR) dataset concerning breast cancer metastasis to study the overfitting of deep feedforward neural networks (FNNs) prediction models. We studied how each hyperparameter and some of the interesting pairs of hyperparameters were interacting to influence the model performance and overfitting. The 11 hyperparameters we studied were activate function, weight initializer, number of hidden layers, learning rate, momentum, decay, dropout rate, batch size, epochs, L1, and L2. Our results show that most of the single hyperparameters are either negatively or positively corrected with model prediction performance and overfitting. In particular, we found that overfitting overall tends to negatively correlate with learning rate, decay, batch size, and L2, but tends to positively correlate with momentum, epochs, and L1. According to our results, learning rate, decay, and batch size may have a more significant impact on both overfitting and prediction performance than most of the other hyperparameters, including L1, L2, and dropout rate, which were designed for minimizing overfitting. We also find some interesting interacting pairs of hyperparameters such as learning rate and momentum, learning rate and decay, and batch size and epochs. Full article

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15 pages, 2315 KiB

Open AccessArticle

Replication of Real-World Evidence in Oncology Using Electronic Health Record Data Extracted by Machine Learning

by Corey M. Benedum, Arjun Sondhi, Erin Fidyk, Aaron B. Cohen, Sheila Nemeth, Blythe Adamson, Melissa Estévez and Selen Bozkurt

Cancers 2023, 15(6), 1853; https://doi.org/10.3390/cancers15061853 - 20 Mar 2023

Cited by 10 | Viewed by 4038

Abstract

Meaningful real-world evidence (RWE) generation requires unstructured data found in electronic health records (EHRs) which are often missing from administrative claims; however, obtaining relevant data from unstructured EHR sources is resource-intensive. In response, researchers are using natural language processing (NLP) with machine learning [...] Read more.

Meaningful real-world evidence (RWE) generation requires unstructured data found in electronic health records (EHRs) which are often missing from administrative claims; however, obtaining relevant data from unstructured EHR sources is resource-intensive. In response, researchers are using natural language processing (NLP) with machine learning (ML) techniques (i.e., ML extraction) to extract real-world data (RWD) at scale. This study assessed the quality and fitness-for-use of EHR-derived oncology data curated using NLP with ML as compared to the reference standard of expert abstraction. Using a sample of 186,313 patients with lung cancer from a nationwide EHR-derived de-identified database, we performed a series of replication analyses demonstrating some common analyses conducted in retrospective observational research with complex EHR-derived data to generate evidence. Eligible patients were selected into biomarker- and treatment-defined cohorts, first with expert-abstracted then with ML-extracted data. We utilized the biomarker- and treatment-defined cohorts to perform analyses related to biomarker-associated survival and treatment comparative effectiveness, respectively. Across all analyses, the results differed by less than 8% between the data curation methods, and similar conclusions were reached. These results highlight that high-performance ML-extracted variables trained on expert-abstracted data can achieve similar results as when using abstracted data, unlocking the ability to perform oncology research at scale. Full article

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12 pages, 1975 KiB

Open AccessArticle

A Neural Network Model Combining [-2]proPSA, freePSA, Total PSA, Cathepsin D, and Thrombospondin-1 Showed Increased Accuracy in the Identification of Clinically Significant Prostate Cancer

by Francesco Gentile, Evelina La Civita, Bartolomeo Della Ventura, Matteo Ferro, Dario Bruzzese, Felice Crocetto, Pierre Tennstedt, Thomas Steuber, Raffaele Velotta and Daniela Terracciano

Cancers 2023, 15(5), 1355; https://doi.org/10.3390/cancers15051355 - 21 Feb 2023

Cited by 6 | Viewed by 2641

Abstract

Background: The Prostate Health Index (PHI) and Proclarix (PCLX) have been proposed as blood-based tests for prostate cancer (PCa). In this study, we evaluated the feasibility of an artificial neural network (ANN)-based approach to develop a combinatorial model including PHI and PCLX biomarkers [...] Read more.

Background: The Prostate Health Index (PHI) and Proclarix (PCLX) have been proposed as blood-based tests for prostate cancer (PCa). In this study, we evaluated the feasibility of an artificial neural network (ANN)-based approach to develop a combinatorial model including PHI and PCLX biomarkers to recognize clinically significant PCa (csPCa) at initial diagnosis. Methods: To this aim, we prospectively enrolled 344 men from two different centres. All patients underwent radical prostatectomy (RP). All men had a prostate-specific antigen (PSA) between 2 and 10 ng/mL. We used an artificial neural network to develop models that can identify csPCa efficiently. As inputs, the model uses [-2]proPSA, freePSA, total PSA, cathepsin D, thrombospondin, and age. Results: The output of the model is an estimate of the presence of a low or high Gleason score PCa defined at RP. After training on a dataset of up to 220 samples and optimization of the variables, the model achieved values as high as 78% for sensitivity and 62% for specificity for all-cancer detection compared with those of PHI and PCLX alone. For csPCa detection, the model showed 66% (95% CI 66–68%) for sensitivity and 68% (95% CI 66–68%) for specificity. These values were significantly different compared with those of PHI (p < 0.0001 and 0.0001, respectively) and PCLX (p = 0.0003 and 0.0006, respectively) alone. Conclusions: Our preliminary study suggests that combining PHI and PCLX biomarkers may help to estimate, with higher accuracy, the presence of csPCa at initial diagnosis, allowing a personalized treatment approach. Further studies training the model on larger datasets are strongly encouraged to support the efficiency of this approach. Full article

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7 pages, 210 KiB

Open AccessEditorial

Artificial Intelligence in Oncology: A Topical Collection in 2022

by Andreas Stadlbauer and Anke Meyer-Bäse

Cancers 2023, 15(4), 1065; https://doi.org/10.3390/cancers15041065 - 7 Feb 2023

Cited by 2 | Viewed by 1612

Abstract

Artificial intelligence (AI) is considered one of the core technologies of the Fourth Industrial Revolution that is currently taking place [...] Full article

11 pages, 2025 KiB

Open AccessCommunication

Implementation of Machine Learning Models to Ensure Radiotherapy Quality for Multicenter Clinical Trials: Report from a Phase III Lung Cancer Study

by Huaizhi Geng, Zhongxing Liao, Quynh-Nhu Nguyen, Abigail T. Berman, Clifford Robinson, Abraham Wu, Romaine Charles Nichols Jr, Henning Willers, Nasiruddin Mohammed, Pranshu Mohindra and Ying Xiao

Cancers 2023, 15(4), 1014; https://doi.org/10.3390/cancers15041014 - 5 Feb 2023

Cited by 1 | Viewed by 2320

Abstract

The outcome of the patient and the success of clinical trials involving RT is dependent on the quality assurance of the RT plans. Knowledge-based Planning (KBP) models using data from a library of high-quality plans have been utilized in radiotherapy to guide treatment. [...] Read more.

The outcome of the patient and the success of clinical trials involving RT is dependent on the quality assurance of the RT plans. Knowledge-based Planning (KBP) models using data from a library of high-quality plans have been utilized in radiotherapy to guide treatment. In this study, we report on the use of these machine learning tools to guide the quality assurance of multicenter clinical trial plans. The data from 130 patients submitted to RTOG1308 were included in this study. Fifty patient cases were used to train separate photon and proton models on a commercially available platform based on principal component analysis. Models evaluated 80 patient cases. Statistical comparisons were made between the KBP plans and the original plans submitted for quality evaluation. Both photon and proton KBP plans demonstrate a statistically significant improvement of quality in terms of organ-at-risk (OAR) sparing. Proton KBP plans, a relatively emerging technique, show more improvements compared with photon plans. The KBP proton model is a useful tool for creating proton plans that adhere to protocol requirements. The KBP tool was also shown to be a useful tool for evaluating the quality of RT plans in the multicenter clinical trial setting. Full article

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20 pages, 3637 KiB

Open AccessArticle

Development of a Novel Intra-Operative Score to Record Diseases’ Anatomic Fingerprints (ANAFI Score) for the Prediction of Complete Cytoreduction in Advanced-Stage Ovarian Cancer by Using Machine Learning and Explainable Artificial Intelligence

by Alexandros Laios, Evangelos Kalampokis, Racheal Johnson, Sarika Munot, Amudha Thangavelu, Richard Hutson, Tim Broadhead, Georgios Theophilou, David Nugent and Diederick De Jong

Cancers 2023, 15(3), 966; https://doi.org/10.3390/cancers15030966 - 3 Feb 2023

Cited by 8 | Viewed by 2764

Abstract

Background: The Peritoneal Carcinomatosis Index (PCI) and the Intra-operative Mapping for Ovarian Cancer (IMO), to a lesser extent, have been universally validated in advanced-stage epithelial ovarian cancer (EOC) to describe the extent of peritoneal dissemination and are proven to be powerful predictors of [...] Read more.

Background: The Peritoneal Carcinomatosis Index (PCI) and the Intra-operative Mapping for Ovarian Cancer (IMO), to a lesser extent, have been universally validated in advanced-stage epithelial ovarian cancer (EOC) to describe the extent of peritoneal dissemination and are proven to be powerful predictors of the surgical outcome with an added sensitivity of assessment at laparotomy of around 70%. This leaves room for improvement because the two-dimensional anatomic scoring model fails to reflect the patient’s real anatomy, as seen by a surgeon. We hypothesized that tumor dissemination in specific anatomic locations can be more predictive of complete cytoreduction (CC0) and survival than PCI and IMO tools in EOC patients. (2) Methods: We analyzed prospectively data collected from 508 patients with FIGO-stage IIIB-IVB EOC who underwent cytoreductive surgery between January 2014 and December 2019 at a UK tertiary center. We adapted the structured ESGO ovarian cancer report to provide detailed information on the patterns of tumor dissemination (cancer anatomic fingerprints). We employed the extreme gradient boost (XGBoost) to model only the variables referring to the EOC disseminated patterns, to create an intra-operative score and judge the predictive power of the score alone for complete cytoreduction (CC0). Receiver operating characteristic (ROC) curves were then used for performance comparison between the new score and the existing PCI and IMO tools. We applied the Shapley additive explanations (SHAP) framework to support the feature selection of the narrated cancer fingerprints and provide global and local explainability. Survival analysis was performed using Kaplan–Meier curves and Cox regression. (3) Results: An intra-operative disease score was developed based on specific weights assigned to the cancer anatomic fingerprints. The scores range from 0 to 24. The XGBoost predicted CC0 resection (area under curve (AUC) = 0.88 CI = 0.854–0.913) with high accuracy. Organ-specific dissemination on the small bowel mesentery, large bowel serosa, and diaphragmatic peritoneum were the most crucial features globally. When added to the composite model, the novel score slightly enhanced its predictive value (AUC = 0.91, CI = 0.849–0.963). We identified a “turning point”, ≤5, that increased the probability of CC0. Using conventional logistic regression, the new score was superior to the PCI and IMO scores for the prediction of CC0 (AUC = 0.81 vs. 0.73 and 0.67, respectively). In multivariate Cox analysis, a 1-point increase in the new intra-operative score was associated with poorer progression-free (HR: 1.06; 95% CI: 1.03–1.09, p < 0.005) and overall survival (HR: 1.04; 95% CI: 1.01–1.07), by 4% and 6%, respectively. (4) Conclusions: The presence of cancer disseminated in specific anatomical sites, including small bowel mesentery, large bowel serosa, and diaphragmatic peritoneum, can be more predictive of CC0 and survival than the entire PCI and IMO scores. Early intra-operative assessment of these areas only may reveal whether CC0 is achievable. In contrast to the PCI and IMO scores, the novel score remains predictive of adverse survival outcomes. Full article

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18 pages, 2448 KiB

Open AccessArticle

Radiomics-Based Inter-Lesion Relation Network to Describe [¹⁸F]FMCH PET/CT Imaging Phenotypes in Prostate Cancer

by Lara Cavinato, Martina Sollini, Alessandra Ragni, Francesco Bartoli, Roberta Zanca, Francesco Pasqualetti, Andrea Marciano, Francesca Ieva and Paola Anna Erba

Cancers 2023, 15(3), 823; https://doi.org/10.3390/cancers15030823 - 29 Jan 2023

Cited by 3 | Viewed by 2391

Abstract

Advanced image analysis, including radiomics, has recently acquired recognition as a source of biomarkers, although there are some technical and methodological challenges to face for its application in the clinic. Among others, proper phenotyping of metastatic or systemic disease where multiple lesions coexist [...] Read more.

Advanced image analysis, including radiomics, has recently acquired recognition as a source of biomarkers, although there are some technical and methodological challenges to face for its application in the clinic. Among others, proper phenotyping of metastatic or systemic disease where multiple lesions coexist is an issue, since each lesion contributes to characterization of the disease. Therefore, the radiomic profile of each lesion should be modeled into a more complex architecture able to reproduce each “unit” (lesion) as a part of the “entire” (patient). This work aimed to characterize intra-tumor heterogeneity underpinning metastatic prostate cancer using an exhaustive innovative approach which consist of a i) feature transformation method to build an agnostic (i.e., irrespective of pre-existence knowledge, experience, and expertise) radiomic profile of lesions extracted from [¹⁸F]FMCH PET/CT, ii) qualitative assessment of intra-tumor heterogeneity of patients, iii) quantitative representation of the intra-tumor heterogeneity of patients in terms of the relationship between their lesions’ profiles, to be associated with prognostic factors. We confirmed that metastatic prostate cancer patients encompassed lesions with different radiomic profiles that exhibited intra-tumor radiomic heterogeneity and that the presence of many radiomic profiles within the same patient impacted the outcome. Full article

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2022

Jump to: 2024, 2023, 2021

10 pages, 1229 KiB

Open AccessArticle

Diagnosis of Depth of Submucosal Invasion in Colorectal Cancer with AI Using Deep Learning

by Soichiro Minami, Kazuhiro Saso, Norikatsu Miyoshi, Shiki Fujino, Shinya Kato, Yuki Sekido, Tsuyoshi Hata, Takayuki Ogino, Hidekazu Takahashi, Mamoru Uemura, Hirofumi Yamamoto, Yuichiro Doki and Hidetoshi Eguchi

Cancers 2022, 14(21), 5361; https://doi.org/10.3390/cancers14215361 - 31 Oct 2022

Cited by 8 | Viewed by 2719

Abstract

The submucosal invasion depth predicts prognosis in early colorectal cancer. Although colorectal cancer with shallow submucosal invasion can be treated via endoscopic resection, colorectal cancer with deep submucosal invasion requires surgical colectomy. However, accurately diagnosing the depth of submucosal invasion via endoscopy is [...] Read more.

The submucosal invasion depth predicts prognosis in early colorectal cancer. Although colorectal cancer with shallow submucosal invasion can be treated via endoscopic resection, colorectal cancer with deep submucosal invasion requires surgical colectomy. However, accurately diagnosing the depth of submucosal invasion via endoscopy is difficult. We developed a tool to diagnose the depth of submucosal invasion in early colorectal cancer using artificial intelligence. We reviewed data from 196 patients who had undergone a preoperative colonoscopy at the Osaka University Hospital and Osaka International Cancer Institute between 2011 and 2018 and were diagnosed pathologically as having shallow submucosal invasion or deep submucosal invasion colorectal cancer. A convolutional neural network for predicting invasion depth was constructed using 706 images from 91 patients between 2011 and 2015 as the training dataset. The diagnostic accuracy of the constructed convolutional neural network was evaluated using 394 images from 49 patients between 2016 and 2017 as the validation dataset. We also prospectively tested the tool from 56 patients in 2018 with suspected early-stage colorectal cancer. The sensitivity, specificity, accuracy, and area under the curve of the convolutional neural network for diagnosing deep submucosal invasion colorectal cancer were 87.2% (258/296), 35.7% (35/98), 74.4% (293/394), and 0.758, respectively. The positive predictive value was 84.4% (356/422) and the sensitivity was 75.7% (356/470) in the test set. The diagnostic accuracy of the constructed convolutional neural network seemed to be as high as that of a skilled endoscopist. Thus, endoscopic image recognition by deep learning may be able to predict the submucosal invasion depth in early-stage colorectal cancer in clinical practice. Full article

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14 pages, 1702 KiB

Open AccessArticle

Impact of Tumour Segmentation Accuracy on Efficacy of Quantitative MRI Biomarkers of Radiotherapy Outcome in Brain Metastasis

by Seyed Ali Jalalifar, Hany Soliman, Arjun Sahgal and Ali Sadeghi-Naini

Cancers 2022, 14(20), 5133; https://doi.org/10.3390/cancers14205133 - 20 Oct 2022

Cited by 3 | Viewed by 2291

Abstract

Significantly affecting patients’ clinical course and quality of life, a growing number of cancer cases are diagnosed with brain metastasis (BM) annually. Stereotactic radiotherapy is now a major treatment option for patients with BM. However, it may take months before the local response [...] Read more.

Significantly affecting patients’ clinical course and quality of life, a growing number of cancer cases are diagnosed with brain metastasis (BM) annually. Stereotactic radiotherapy is now a major treatment option for patients with BM. However, it may take months before the local response of BM to stereotactic radiation treatment is apparent on standard follow-up imaging. While machine learning in conjunction with radiomics has shown great promise in predicting the local response of BM before or early after radiotherapy, further development and widespread application of such techniques has been hindered by their dependency on manual tumour delineation. In this study, we explored the impact of using less-accurate automatically generated segmentation masks on the efficacy of radiomic features for radiotherapy outcome prediction in BM. The findings of this study demonstrate that while the effect of tumour delineation accuracy is substantial for segmentation models with lower dice scores (dice score ≤ 0.85), radiomic features and prediction models are rather resilient to imperfections in the produced tumour masks. Specifically, the selected radiomic features (six shared features out of seven) and performance of the prediction model (accuracy of 80% versus 80%, AUC of 0.81 versus 0.78) were fairly similar for the ground-truth and automatically generated segmentation masks, with dice scores close to 0.90. The positive outcome of this work paves the way for adopting high-throughput automatically generated tumour masks for discovering diagnostic and prognostic imaging biomarkers in BM without sacrificing accuracy. Full article

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17 pages, 23648 KiB

Open AccessArticle

Transfer Learning for Adenocarcinoma Classifications in the Transurethral Resection of Prostate Whole-Slide Images

by Masayuki Tsuneki, Makoto Abe and Fahdi Kanavati

Cancers 2022, 14(19), 4744; https://doi.org/10.3390/cancers14194744 - 28 Sep 2022

Cited by 5 | Viewed by 2778

Abstract

The transurethral resection of the prostate (TUR-P) is an option for benign prostatic diseases, especially nodular hyperplasia patients who have moderate to severe urinary problems that have not responded to medication. Importantly, incidental prostate cancer is diagnosed at the time of TUR-P for [...] Read more.

The transurethral resection of the prostate (TUR-P) is an option for benign prostatic diseases, especially nodular hyperplasia patients who have moderate to severe urinary problems that have not responded to medication. Importantly, incidental prostate cancer is diagnosed at the time of TUR-P for benign prostatic disease. TUR-P specimens contain a large number of fragmented prostate tissues; this makes them time consuming to examine for pathologists as they have to check each fragment one by one. In this study, we trained deep learning models to classify TUR-P WSIs into prostate adenocarcinoma and benign (non-neoplastic) lesions using transfer and weakly supervised learning. We evaluated the models on TUR-P, needle biopsy, and The Cancer Genome Atlas (TCGA) public dataset test sets, achieving an ROC-AUC up to 0.984 in TUR-P test sets for adenocarcinoma. The results demonstrate the promising potential of deployment in a practical TUR-P histopathological diagnostic workflow system to improve the efficiency of pathologists. Full article

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27 pages, 26717 KiB

Open AccessArticle

Automated Assessment of Breast Positioning Quality in Screening Mammography

by Mouna Brahim, Kai Westerkamp, Louisa Hempel, Reiner Lehmann, Dirk Hempel and Patrick Philipp

Cancers 2022, 14(19), 4704; https://doi.org/10.3390/cancers14194704 - 27 Sep 2022

Cited by 11 | Viewed by 5322

Abstract

Screening mammography is a widely used approach for early breast cancer detection, effectively increasing the survival rate of affected patients. According to the Food and Drug Administration’s Mammography Quality Standards Act and Program statistics, approximately 39 million mammography procedures are performed in the [...] Read more.

Screening mammography is a widely used approach for early breast cancer detection, effectively increasing the survival rate of affected patients. According to the Food and Drug Administration’s Mammography Quality Standards Act and Program statistics, approximately 39 million mammography procedures are performed in the United States each year. Therefore, breast cancer screening is among the most common radiological tasks. Interpretation of screening mammograms by a specialist radiologist includes primarily the review of breast positioning quality, which is a key factor affecting the sensitivity of mammography and thus the diagnostic performance. Each mammogram with inadequate positioning may lead to a missed cancer or, in case of false positive signal interpretation, to follow-up activities, increased emotional burden and potential over-therapy and must be repeated, requiring the return of the patient. In this study, we have developed deep convolutional neuronal networks to differentiate mammograms with inadequate breast positioning from the adequate ones. The aim of the proposed automated positioning quality evaluation is to assist radiology technologists in detecting poorly positioned mammograms during patient visits, improve mammography performance, and decrease the recall rate. The implemented models have achieved 96.5% accuracy in cranio-caudal view classification and 93.3% accuracy in mediolateral oblique view regarding breast positioning quality. In addition to these results, we developed a software module that allows the study to be applied in practice by presenting the implemented model predictions and informing the technologist about the missing quality criteria. Full article

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19 pages, 2089 KiB

Open AccessReview

DNA Karyometry for Automated Detection of Cancer Cells

by Alfred Böcking, David Friedrich, Martin Schramm, Branko Palcic and Gregor Erbeznik

Cancers 2022, 14(17), 4210; https://doi.org/10.3390/cancers14174210 - 30 Aug 2022

Cited by 6 | Viewed by 2350

Abstract

Background: Microscopical screening of cytological samples for the presence of cancer cells at high throughput with sufficient diagnostic accuracy requires highly specialized personnel which is not available in most countries. Methods: Using commercially available automated microscope-based screeners (MotiCyte and EasyScan), software was developed [...] Read more.

Background: Microscopical screening of cytological samples for the presence of cancer cells at high throughput with sufficient diagnostic accuracy requires highly specialized personnel which is not available in most countries. Methods: Using commercially available automated microscope-based screeners (MotiCyte and EasyScan), software was developed which is able to classify Feulgen-stained nuclei into eight diagnostically relevant types, using supervised machine learning. the nuclei belonging to normal cells were used for internal calibration of the nuclear DNA content while nuclei belonging to those suspicious of being malignant were specifically identified. The percentage of morphologically abnormal nuclei was used to identify samples suspected of malignancy, and the proof of DNA-aneuploidy was used to definitely determine the state malignancy. A blinded study was performed using oral smears from 92 patients with Fanconi anemia, revealing oral leukoplakias or erythroplakias. In an earlier study, we compared diagnostic accuracies on 121 serous effusion specimens. In addition, using a blinded study employing 80 patients with prostate cancer who were under active surveillance, we aimed to identify those whose cancers would not advance within 4 years. Results: Applying a threshold of the presence of >4% of morphologically abnormal nuclei from oral squamous cells and DNA single-cell or stemline aneuploidy to identify samples suspected of malignancy, an overall diagnostic accuracy of 91.3% was found as compared with 75.0% accuracy determined by conventional subjective cytological assessment using the same slides. Accuracy of automated screening effusions was 84.3% as compared to 95.9% of conventional cytology. No prostate cancer patients under active surveillance, revealing DNA-grade 1, showed progress of their disease within 4.1 years. Conclusions: An automated microscope-based screener was developed which is able to identify malignant cells in different types of human specimens with a diagnostic accuracy comparable with subjective cytological assessment. Early prostate cancers which do not progress despite applying any therapy could be identified using this automated approach. Full article

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10 pages, 1908 KiB

Open AccessArticle

An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study

by María Torrente, Pedro A. Sousa, Roberto Hernández, Mariola Blanco, Virginia Calvo, Ana Collazo, Gracinda R. Guerreiro, Beatriz Núñez, Joao Pimentao, Juan Cristóbal Sánchez, Manuel Campos, Luca Costabello, Vit Novacek, Ernestina Menasalvas, María Esther Vidal and Mariano Provencio

Cancers 2022, 14(16), 4041; https://doi.org/10.3390/cancers14164041 - 22 Aug 2022

Cited by 17 | Viewed by 4167

Abstract

Background: Artificial intelligence (AI) has contributed substantially in recent years to the resolution of different biomedical problems, including cancer. However, AI tools with significant and widespread impact in oncology remain scarce. The goal of this study is to present an AI-based solution tool [...] Read more.

Background: Artificial intelligence (AI) has contributed substantially in recent years to the resolution of different biomedical problems, including cancer. However, AI tools with significant and widespread impact in oncology remain scarce. The goal of this study is to present an AI-based solution tool for cancer patients data analysis that assists clinicians in identifying the clinical factors associated with poor prognosis, relapse and survival, and to develop a prognostic model that stratifies patients by risk. Materials and Methods: We used clinical data from 5275 patients diagnosed with non-small cell lung cancer, breast cancer, and non-Hodgkin lymphoma at Hospital Universitario Puerta de Hierro-Majadahonda. Accessible clinical parameters measured with a wearable device and quality of life questionnaires data were also collected. Results: Using an AI-tool, data from 5275 cancer patients were analyzed, integrating clinical data, questionnaires data, and data collected from wearable devices. Descriptive analyses were performed in order to explore the patients’ characteristics, survival probabilities were calculated, and a prognostic model identified low and high-risk profile patients. Conclusion: Overall, the reconstruction of the population’s risk profile for the cancer-specific predictive model was achieved and proved useful in clinical practice using artificial intelligence. It has potential application in clinical settings to improve risk stratification, early detection, and surveillance management of cancer patients. Full article

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15 pages, 2264 KiB

Open AccessArticle

Radiomics-Based Deep Learning Prediction of Overall Survival in Non-Small-Cell Lung Cancer Using Contrast-Enhanced Computed Tomography

by Kuei-Yuan Hou, Jyun-Ru Chen, Yung-Chen Wang, Ming-Huang Chiu, Sen-Ping Lin, Yuan-Heng Mo, Shih-Chieh Peng and Chia-Feng Lu

Cancers 2022, 14(15), 3798; https://doi.org/10.3390/cancers14153798 - 4 Aug 2022

Cited by 21 | Viewed by 4356

Abstract

Patient outcomes of non-small-cell lung cancer (NSCLC) vary because of tumor heterogeneity and treatment strategies. This study aimed to construct a deep learning model combining both radiomic and clinical features to predict the overall survival of patients with NSCLC. To improve the reliability [...] Read more.

Patient outcomes of non-small-cell lung cancer (NSCLC) vary because of tumor heterogeneity and treatment strategies. This study aimed to construct a deep learning model combining both radiomic and clinical features to predict the overall survival of patients with NSCLC. To improve the reliability of the proposed model, radiomic analysis complying with the Image Biomarker Standardization Initiative and the compensation approach to integrate multicenter datasets were performed on contrast-enhanced computed tomography (CECT) images. Pretreatment CECT images and the clinical data of 492 patients with NSCLC from two hospitals were collected. The deep neural network architecture, DeepSurv, with the input of radiomic and clinical features was employed. The performance of survival prediction model was assessed using the C-index and area under the curve (AUC) 8, 12, and 24 months after diagnosis. The performance of survival prediction that combined eight radiomic features and five clinical features outperformed that solely based on radiomic or clinical features. The C-index values of the combined model achieved 0.74, 0.75, and 0.75, respectively, and AUC values of 0.76, 0.74, and 0.73, respectively, 8, 12, and 24 months after diagnosis. In conclusion, combining the traits of pretreatment CECT images, lesion characteristics, and treatment strategies could effectively predict the survival of patients with NSCLC using a deep learning model. Full article

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28 pages, 5421 KiB

Open AccessReview

Current Developments of Artificial Intelligence in Digital Pathology and Its Future Clinical Applications in Gastrointestinal Cancers

by Alex Ngai Nick Wong, Zebang He, Ka Long Leung, Curtis Chun Kit To, Chun Yin Wong, Sze Chuen Cesar Wong, Jung Sun Yoo, Cheong Kin Ronald Chan, Angela Zaneta Chan, Maribel D. Lacambra and Martin Ho Yin Yeung

Cancers 2022, 14(15), 3780; https://doi.org/10.3390/cancers14153780 - 3 Aug 2022

Cited by 22 | Viewed by 9303

Abstract

The implementation of DP will revolutionize current practice by providing pathologists with additional tools and algorithms to improve workflow. Furthermore, DP will open up opportunities for development of AI-based tools for more precise and reproducible diagnosis through computational pathology. One of the key [...] Read more.

The implementation of DP will revolutionize current practice by providing pathologists with additional tools and algorithms to improve workflow. Furthermore, DP will open up opportunities for development of AI-based tools for more precise and reproducible diagnosis through computational pathology. One of the key features of AI is its capability to generate perceptions and recognize patterns beyond the human senses. Thus, the incorporation of AI into DP can reveal additional morphological features and information. At the current rate of AI development and adoption of DP, the interest in computational pathology is expected to rise in tandem. There have already been promising developments related to AI-based solutions in prostate cancer detection; however, in the GI tract, development of more sophisticated algorithms is required to facilitate histological assessment of GI specimens for early and accurate diagnosis. In this review, we aim to provide an overview of the current histological practices in AP laboratories with respect to challenges faced in image preprocessing, present the existing AI-based algorithms, discuss their limitations and present clinical insight with respect to the application of AI in early detection and diagnosis of GI cancer. Full article

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12 pages, 5715 KiB

Open AccessArticle

Efficient Prediction of Ki-67 Proliferation Index in Meningiomas on MRI: From Traditional Radiological Findings to a Machine Learning Approach

by Yanjie Zhao, Jianfeng Xu, Boran Chen, Le Cao and Chaoyue Chen

Cancers 2022, 14(15), 3637; https://doi.org/10.3390/cancers14153637 - 26 Jul 2022

Cited by 12 | Viewed by 4274

Abstract

Background/aim This study aimed to explore the value of radiological and radiomic features retrieved from magnetic resonance imaging in the prediction of a Ki-67 proliferative index in meningioma patients using a machine learning model. Methods This multicenter, retrospective study included 371 patients collected [...] Read more.

Background/aim This study aimed to explore the value of radiological and radiomic features retrieved from magnetic resonance imaging in the prediction of a Ki-67 proliferative index in meningioma patients using a machine learning model. Methods This multicenter, retrospective study included 371 patients collected from two centers. The Ki-67 expression was classified into low-expressed and high-expressed groups with a threshold of 5%. Clinical features and radiological features were collected and analyzed by using univariate and multivariate statistical analyses. Radiomic features were extracted from contrast-enhanced images, followed by three independent feature selections. Six predictive models were constructed with different combinations of features by using linear discriminant analysis (LDA) classifier. Results The multivariate analysis suggested that the presence of intratumoral necrosis (p = 0.032) and maximum diameter (p < 0.001) were independently correlated with a high Ki-67 status. The predictive models showed good performance with AUC of 0.837, accuracy of 0.810, sensitivity of 0.857, and specificity of 0.771 in the internal test and with AUC of 0.700, accuracy of 0.557, sensitivity of 0.314, and specificity of 0.885 in the external test. Conclusion The results of this study suggest that the predictive model can efficiently predict the Ki-67 index of meningioma patients to facilitate the therapeutic management. Full article

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19 pages, 4854 KiB

Open AccessArticle

How Many Private Data Are Needed for Deep Learning in Lung Nodule Detection on CT Scans? A Retrospective Multicenter Study

by Jeong Woo Son, Ji Young Hong, Yoon Kim, Woo Jin Kim, Dae-Yong Shin, Hyun-Soo Choi, So Hyeon Bak and Kyoung Min Moon

Cancers 2022, 14(13), 3174; https://doi.org/10.3390/cancers14133174 - 28 Jun 2022

Cited by 6 | Viewed by 3021

Abstract

Early detection of lung nodules is essential for preventing lung cancer. However, the number of radiologists who can diagnose lung nodules is limited, and considerable effort and time are required. To address this problem, researchers are investigating the automation of deep-learning-based lung nodule [...] Read more.

Early detection of lung nodules is essential for preventing lung cancer. However, the number of radiologists who can diagnose lung nodules is limited, and considerable effort and time are required. To address this problem, researchers are investigating the automation of deep-learning-based lung nodule detection. However, deep learning requires large amounts of data, which can be difficult to collect. Therefore, data collection should be optimized to facilitate experiments at the beginning of lung nodule detection studies. We collected chest computed tomography scans from 515 patients with lung nodules from three hospitals and high-quality lung nodule annotations reviewed by radiologists. We conducted several experiments using the collected datasets and publicly available data from LUNA16. The object detection model, YOLOX was used in the lung nodule detection experiment. Similar or better performance was obtained when training the model with the collected data rather than LUNA16 with large amounts of data. We also show that weight transfer learning from pre-trained open data is very useful when it is difficult to collect large amounts of data. Good performance can otherwise be expected when reaching more than 100 patients. This study offers valuable insights for guiding data collection in lung nodules studies in the future. Full article

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12 pages, 1300 KiB

Open AccessReview

Considerations for the Use of Machine Learning Extracted Real-World Data to Support Evidence Generation: A Research-Centric Evaluation Framework

by Melissa Estevez, Corey M. Benedum, Chengsheng Jiang, Aaron B. Cohen, Sharang Phadke, Somnath Sarkar and Selen Bozkurt

Cancers 2022, 14(13), 3063; https://doi.org/10.3390/cancers14133063 - 22 Jun 2022

Cited by 11 | Viewed by 6655

Abstract

A vast amount of real-world data, such as pathology reports and clinical notes, are captured as unstructured text in electronic health records (EHRs). However, this information is both difficult and costly to extract through human abstraction, especially when scaling to large datasets is [...] Read more.

A vast amount of real-world data, such as pathology reports and clinical notes, are captured as unstructured text in electronic health records (EHRs). However, this information is both difficult and costly to extract through human abstraction, especially when scaling to large datasets is needed. Fortunately, Natural Language Processing (NLP) and Machine Learning (ML) techniques provide promising solutions for a variety of information extraction tasks such as identifying a group of patients who have a specific diagnosis, share common characteristics, or show progression of a disease. However, using these ML-extracted data for research still introduces unique challenges in assessing validity and generalizability to different cohorts of interest. In order to enable effective and accurate use of ML-extracted real-world data (RWD) to support research and real-world evidence generation, we propose a research-centric evaluation framework for model developers, ML-extracted data users and other RWD stakeholders. This framework covers the fundamentals of evaluating RWD produced using ML methods to maximize the use of EHR data for research purposes. Full article

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20 pages, 3638 KiB

Open AccessArticle

Synthetic 18F-FDG PET Image Generation Using a Combination of Biomathematical Modeling and Machine Learning

by Mohammad Amin Abazari, Madjid Soltani, Farshad Moradi Kashkooli and Kaamran Raahemifar

Cancers 2022, 14(11), 2786; https://doi.org/10.3390/cancers14112786 - 3 Jun 2022

Cited by 13 | Viewed by 3408

Abstract

No previous works have attempted to combine generative adversarial network (GAN) architectures and the biomathematical modeling of positron emission tomography (PET) radiotracer uptake in tumors to generate extra training samples. Here, we developed a novel computational model to produce synthetic 18F-fluorodeoxyglucose (18F-FDG) PET [...] Read more.

No previous works have attempted to combine generative adversarial network (GAN) architectures and the biomathematical modeling of positron emission tomography (PET) radiotracer uptake in tumors to generate extra training samples. Here, we developed a novel computational model to produce synthetic 18F-fluorodeoxyglucose (18F-FDG) PET images of solid tumors in different stages of progression and angiogenesis. First, a comprehensive biomathematical model is employed for creating tumor-induced angiogenesis, intravascular and extravascular fluid flow, as well as modeling of the transport phenomena and reaction processes of 18F-FDG in a tumor microenvironment. Then, a deep convolutional GAN (DCGAN) model is employed for producing synthetic PET images using 170 input images of 18F-FDG uptake in each of 10 different tumor microvascular networks. The interstitial fluid parameters and spatiotemporal distribution of 18F-FDG uptake in tumor and healthy tissues have been compared against previously published numerical and experimental studies, indicating the accuracy of the model. The structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) of the generated PET sample and the experimental one are 0.72 and 28.53, respectively. Our results demonstrate that a combination of biomathematical modeling and GAN-based augmentation models provides a robust framework for the non-invasive and accurate generation of synthetic PET images of solid tumors in different stages. Full article

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23 pages, 13257 KiB

Open AccessArticle

Radiophysiomics: Brain Tumors Classification by Machine Learning and Physiological MRI Data

by Andreas Stadlbauer, Franz Marhold, Stefan Oberndorfer, Gertraud Heinz, Michael Buchfelder, Thomas M. Kinfe and Anke Meyer-Bäse

Cancers 2022, 14(10), 2363; https://doi.org/10.3390/cancers14102363 - 10 May 2022

Cited by 27 | Viewed by 5481

Abstract

The precise initial characterization of contrast-enhancing brain tumors has significant consequences for clinical outcomes. Various novel neuroimaging methods have been developed to increase the specificity of conventional magnetic resonance imaging (cMRI) but also the increased complexity of data analysis. Artificial intelligence offers new [...] Read more.

The precise initial characterization of contrast-enhancing brain tumors has significant consequences for clinical outcomes. Various novel neuroimaging methods have been developed to increase the specificity of conventional magnetic resonance imaging (cMRI) but also the increased complexity of data analysis. Artificial intelligence offers new options to manage this challenge in clinical settings. Here, we investigated whether multiclass machine learning (ML) algorithms applied to a high-dimensional panel of radiomic features from advanced MRI (advMRI) and physiological MRI (phyMRI; thus, radiophysiomics) could reliably classify contrast-enhancing brain tumors. The recently developed phyMRI technique enables the quantitative assessment of microvascular architecture, neovascularization, oxygen metabolism, and tissue hypoxia. A training cohort of 167 patients suffering from one of the five most common brain tumor entities (glioblastoma, anaplastic glioma, meningioma, primary CNS lymphoma, or brain metastasis), combined with nine common ML algorithms, was used to develop overall 135 classifiers. Multiclass classification performance was investigated using tenfold cross-validation and an independent test cohort. Adaptive boosting and random forest in combination with advMRI and phyMRI data were superior to human reading in accuracy (0.875 vs. 0.850), precision (0.862 vs. 0.798), F-score (0.774 vs. 0.740), AUROC (0.886 vs. 0.813), and classification error (5 vs. 6). The radiologists, however, showed a higher sensitivity (0.767 vs. 0.750) and specificity (0.925 vs. 0.902). We demonstrated that ML-based radiophysiomics could be helpful in the clinical routine diagnosis of contrast-enhancing brain tumors; however, a high expenditure of time and work for data preprocessing requires the inclusion of deep neural networks. Full article

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13 pages, 982 KiB

Open AccessArticle

Multiparametric ¹⁸F-FDG PET/MRI-Based Radiomics for Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer

by Lale Umutlu, Julian Kirchner, Nils-Martin Bruckmann, Janna Morawitz, Gerald Antoch, Saskia Ting, Ann-Kathrin Bittner, Oliver Hoffmann, Lena Häberle, Eugen Ruckhäberle, Onofrio Antonio Catalano, Michal Chodyla, Johannes Grueneisen, Harald H. Quick, Wolfgang P. Fendler, Christoph Rischpler, Ken Herrmann, Peter Gibbs and Katja Pinker

Cancers 2022, 14(7), 1727; https://doi.org/10.3390/cancers14071727 - 29 Mar 2022

Cited by 25 | Viewed by 2865

Abstract

Background: The aim of this study was to assess whether multiparametric ¹⁸F-FDG PET/MRI-based radiomics analysis is able to predict pathological complete response in breast cancer patients and hence potentially enhance pretherapeutic patient stratification. Methods: A total of 73 female patients (mean age [...] Read more.

Background: The aim of this study was to assess whether multiparametric ¹⁸F-FDG PET/MRI-based radiomics analysis is able to predict pathological complete response in breast cancer patients and hence potentially enhance pretherapeutic patient stratification. Methods: A total of 73 female patients (mean age 49 years; range 27–77 years) with newly diagnosed, therapy-naive breast cancer underwent simultaneous ¹⁸F-FDG PET/MRI and were included in this retrospective study. All PET/MRI datasets were imported to dedicated software (ITK-SNAP v. 3.6.0) for lesion annotation using a semi-automated method. Pretreatment biopsy specimens were used to determine tumor histology, tumor and nuclear grades, and immunohistochemical status. Histopathological results from surgical tumor specimens were used as the reference standard to distinguish between complete pathological response (pCR) and noncomplete pathological response. An elastic net was employed to select the most important radiomic features prior to model development. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated for each model. Results: The best results in terms of AUCs and NPV for predicting complete pathological response in the entire cohort were obtained by the combination of all MR sequences and PET (0.8 and 79.5%, respectively), and no significant differences from the other models were observed. In further subgroup analyses, combining all MR and PET data, the best AUC (0.94) for predicting complete pathologic response was obtained in the HR+/HER2− group. No difference between results with/without the inclusion of PET characteristics was observed in the TN/HER2+ group, each leading to an AUC of 0.92 for all MR and all MR + PET datasets. Conclusion: ¹⁸F-FDG PET/MRI enables comprehensive high-quality radiomics analysis for the prediction of pCR in breast cancer patients, especially in those with HR+/HER2− receptor status. Full article

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13 pages, 2675 KiB

Open AccessArticle

Hyperspectral Imaging for Tissue Classification after Advanced Stage Ovarian Cancer Surgery—A Pilot Study

by Sharline M. van Vliet-Pérez, Nick J. van de Berg, Francesca Manni, Marco Lai, Lucia Rijstenberg, Benno H. W. Hendriks, Jenny Dankelman, Patricia C. Ewing-Graham, Gatske M. Nieuwenhuyzen-de Boer and Heleen J. van Beekhuizen

Cancers 2022, 14(6), 1422; https://doi.org/10.3390/cancers14061422 - 10 Mar 2022

Cited by 7 | Viewed by 3153

Abstract

The most important prognostic factor for the survival of advanced-stage epithelial ovarian cancer (EOC) is the completeness of cytoreductive surgery (CRS). Therefore, an intraoperative technique to detect microscopic tumors would be of great value. The aim of this pilot study is to assess [...] Read more.

The most important prognostic factor for the survival of advanced-stage epithelial ovarian cancer (EOC) is the completeness of cytoreductive surgery (CRS). Therefore, an intraoperative technique to detect microscopic tumors would be of great value. The aim of this pilot study is to assess the feasibility of near-infrared hyperspectral imaging (HSI) for EOC detection in ex vivo tissue samples. Images were collected during CRS in 11 patients in the wavelength range of 665–975 nm, and processed by calibration, normalization, and noise filtering. A linear support vector machine (SVM) was employed to classify healthy and tumorous tissue (defined as >50% tumor cells). Classifier performance was evaluated using leave-one-out cross-validation. Images of 26 tissue samples from 10 patients were included, containing 26,446 data points that were matched to histopathology. Tumorous tissue could be classified with an area under the curve of 0.83, a sensitivity of 0.81, a specificity of 0.70, and Matthew’s correlation coefficient of 0.41. This study paves the way to in vivo and intraoperative use of HSI during CRS. Hyperspectral imaging can scan a whole tissue surface in a fast and non-contact way. Our pilot study demonstrates that HSI and SVM learning can be used to discriminate EOC from surrounding tissue. Full article

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20 pages, 3652 KiB

Open AccessReview

Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications

by Yawen Wu, Michael Cheng, Shuo Huang, Zongxiang Pei, Yingli Zuo, Jianxin Liu, Kai Yang, Qi Zhu, Jie Zhang, Honghai Hong, Daoqiang Zhang, Kun Huang, Liang Cheng and Wei Shao

Cancers 2022, 14(5), 1199; https://doi.org/10.3390/cancers14051199 - 25 Feb 2022

Cited by 53 | Viewed by 9307

Abstract

With the remarkable success of digital histopathology, we have witnessed a rapid expansion of the use of computational methods for the analysis of digital pathology and biopsy image patches. However, the unprecedented scale and heterogeneous patterns of histopathological images have presented critical computational [...] Read more.

With the remarkable success of digital histopathology, we have witnessed a rapid expansion of the use of computational methods for the analysis of digital pathology and biopsy image patches. However, the unprecedented scale and heterogeneous patterns of histopathological images have presented critical computational bottlenecks requiring new computational histopathology tools. Recently, deep learning technology has been extremely successful in the field of computer vision, which has also boosted considerable interest in digital pathology applications. Deep learning and its extensions have opened several avenues to tackle many challenging histopathological image analysis problems including color normalization, image segmentation, and the diagnosis/prognosis of human cancers. In this paper, we provide a comprehensive up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis. Specifically, we first describe recent literature that uses deep learning for color normalization, which is one essential research direction for H&E-stained histopathological image analysis. Followed by the discussion of color normalization, we review applications of the deep learning method for various H&E-stained image analysis tasks such as nuclei and tissue segmentation. We also summarize several key clinical studies that use deep learning for the diagnosis and prognosis of human cancers from H&E-stained histopathological images. Finally, online resources and open research problems on pathological image analysis are also provided in this review for the convenience of researchers who are interested in this exciting field. Full article

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15 pages, 12400 KiB

Open AccessEditor’s ChoiceArticle

A Deep Learning Model for Cervical Cancer Screening on Liquid-Based Cytology Specimens in Whole Slide Images

by Fahdi Kanavati, Naoki Hirose, Takahiro Ishii, Ayaka Fukuda, Shin Ichihara and Masayuki Tsuneki

Cancers 2022, 14(5), 1159; https://doi.org/10.3390/cancers14051159 - 24 Feb 2022

Cited by 34 | Viewed by 8259

Abstract

Liquid-based cytology (LBC) for cervical cancer screening is now more common than the conventional smears, which when digitised from glass slides into whole-slide images (WSIs), opens up the possibility of artificial intelligence (AI)-based automated image analysis. Since conventional screening processes by cytoscreeners and [...] Read more.

Liquid-based cytology (LBC) for cervical cancer screening is now more common than the conventional smears, which when digitised from glass slides into whole-slide images (WSIs), opens up the possibility of artificial intelligence (AI)-based automated image analysis. Since conventional screening processes by cytoscreeners and cytopathologists using microscopes is limited in terms of human resources, it is important to develop new computational techniques that can automatically and rapidly diagnose a large amount of specimens without delay, which would be of great benefit for clinical laboratories and hospitals. The goal of this study was to investigate the use of a deep learning model for the classification of WSIs of LBC specimens into neoplastic and non-neoplastic. To do so, we used a dataset of 1605 cervical WSIs. We evaluated the model on three test sets with a combined total of 1468 WSIs, achieving ROC AUCs for WSI diagnosis in the range of 0.89–0.96, demonstrating the promising potential use of such models for aiding screening processes. Full article

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12 pages, 3225 KiB

Open AccessArticle

Diagnosing Ovarian Cancer on MRI: A Preliminary Study Comparing Deep Learning and Radiologist Assessments

by Tsukasa Saida, Kensaku Mori, Sodai Hoshiai, Masafumi Sakai, Aiko Urushibara, Toshitaka Ishiguro, Manabu Minami, Toyomi Satoh and Takahito Nakajima

Cancers 2022, 14(4), 987; https://doi.org/10.3390/cancers14040987 - 16 Feb 2022

Cited by 29 | Viewed by 5905

Abstract

Background: This study aimed to compare deep learning with radiologists’ assessments for diagnosing ovarian carcinoma using MRI. Methods: This retrospective study included 194 patients with pathologically confirmed ovarian carcinomas or borderline tumors and 271 patients with non-malignant lesions who underwent MRI between January [...] Read more.

Background: This study aimed to compare deep learning with radiologists’ assessments for diagnosing ovarian carcinoma using MRI. Methods: This retrospective study included 194 patients with pathologically confirmed ovarian carcinomas or borderline tumors and 271 patients with non-malignant lesions who underwent MRI between January 2015 and December 2020. T2WI, DWI, ADC map, and fat-saturated contrast-enhanced T1WI were used for the analysis. A deep learning model based on a convolutional neural network (CNN) was trained using 1798 images from 146 patients with malignant tumors and 1865 images from 219 patients with non-malignant lesions for each sequence, and we tested with 48 and 52 images of patients with malignant and non-malignant lesions, respectively. The sensitivity, specificity, accuracy, and AUC were compared between the CNN and interpretations of three experienced radiologists. Results: The CNN of each sequence had a sensitivity of 0.77–0.85, specificity of 0.77–0.92, accuracy of 0.81–0.87, and an AUC of 0.83–0.89, and it achieved a diagnostic performance equivalent to the radiologists. The CNN showed the highest diagnostic performance on the ADC map among all sequences (specificity = 0.85; sensitivity = 0.77; accuracy = 0.81; AUC = 0.89). Conclusion: The CNNs provided a diagnostic performance that was non-inferior to the radiologists for diagnosing ovarian carcinomas on MRI. Full article

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15 pages, 4748 KiB

Open AccessArticle

Development of High-Resolution Dedicated PET-Based Radiomics Machine Learning Model to Predict Axillary Lymph Node Status in Early-Stage Breast Cancer

by Jingyi Cheng, Caiyue Ren, Guangyu Liu, Ruohong Shui, Yingjian Zhang, Junjie Li and Zhimin Shao

Cancers 2022, 14(4), 950; https://doi.org/10.3390/cancers14040950 - 14 Feb 2022

Cited by 21 | Viewed by 3069

Abstract

Purpose of the Report: Accurate clinical axillary evaluation plays an important role in the diagnosis and treatment planning for early-stage breast cancer (BC). This study aimed to develop a scalable, non-invasive and robust machine learning model for predicting of the pathological node status [...] Read more.

Purpose of the Report: Accurate clinical axillary evaluation plays an important role in the diagnosis and treatment planning for early-stage breast cancer (BC). This study aimed to develop a scalable, non-invasive and robust machine learning model for predicting of the pathological node status using dedicated-PET integrating the clinical characteristics in early-stage BC. Materials and Methods: A total of 420 BC patients confirmed by postoperative pathology were retrospectively analyzed. 18F-fluorodeoxyglucose (¹⁸F-FDG) Mammi-PET, ultrasound, physical examination, Lymph-PET, and clinical characteristics were analyzed. The least absolute shrinkage and selection operator (LASSO) regression analysis were used in developing prediction models. The characteristic curve (ROC) of the area under receiver-operator (AUC) and DeLong test were used to evaluate and compare the performance of the models. The clinical utility of the models was determined via decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. Results: A total of 290 patients were enrolled in this study. The AUC of the integrated model diagnosed performance was 0.94 (95% confidence interval (CI), 0.91–0.97) in the training set (n = 203) and 0.93 (95% CI, 0.88–0.99) in the validation set (n = 87) (both p < 0.05). In clinical N0 subgroup, the negative predictive value reached 96.88%, and in clinical N1 subgroup, the positive predictive value reached 92.73%. Conclusions: The use of a machine learning integrated model can greatly improve the true positive and true negative rate of identifying clinical axillary lymph node status in early-stage BC. Full article

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15 pages, 522 KiB

Open AccessReview

Advances in and the Applicability of Machine Learning-Based Screening and Early Detection Approaches for Cancer: A Primer

by Leo Benning, Andreas Peintner and Lukas Peintner

Cancers 2022, 14(3), 623; https://doi.org/10.3390/cancers14030623 - 26 Jan 2022

Cited by 11 | Viewed by 3696

Abstract

Despite the efforts of the past decades, cancer is still among the key drivers of global mortality. To increase the detection rates, screening programs and other efforts to improve early detection were initiated to cover the populations at a particular risk for developing [...] Read more.

Despite the efforts of the past decades, cancer is still among the key drivers of global mortality. To increase the detection rates, screening programs and other efforts to improve early detection were initiated to cover the populations at a particular risk for developing a specific malignant condition. These diagnostic approaches have, so far, mostly relied on conventional diagnostic methods and have made little use of the vast amounts of clinical and diagnostic data that are routinely being collected along the diagnostic pathway. Practitioners have lacked the tools to handle this ever-increasing flood of data. Only recently, the clinical field has opened up more for the opportunities that come with the systematic utilisation of high-dimensional computational data analysis. We aim to introduce the reader to the theoretical background of machine learning (ML) and elaborate on the established and potential use cases of ML algorithms in screening and early detection. Furthermore, we assess and comment on the relevant challenges and misconceptions of the applicability of ML-based diagnostic approaches. Lastly, we emphasise the need for a clear regulatory framework to responsibly introduce ML-based diagnostics in clinical practice and routine care. Full article

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12 pages, 816 KiB

Open AccessArticle

Machine Learning to Discern Interactive Clusters of Risk Factors for Late Recurrence of Metastatic Breast Cancer

by Juan Luis Gomez Marti, Adam Brufsky, Alan Wells and Xia Jiang

Cancers 2022, 14(1), 253; https://doi.org/10.3390/cancers14010253 - 5 Jan 2022

Cited by 4 | Viewed by 2907

Abstract

Background: Risk of metastatic recurrence of breast cancer after initial diagnosis and treatment depends on the presence of a number of risk factors. Although most univariate risk factors have been identified using classical methods, machine-learning methods are also being used to tease out [...] Read more.

Background: Risk of metastatic recurrence of breast cancer after initial diagnosis and treatment depends on the presence of a number of risk factors. Although most univariate risk factors have been identified using classical methods, machine-learning methods are also being used to tease out non-obvious contributors to a patient’s individual risk of developing late distant metastasis. Bayesian-network algorithms can identify not only risk factors but also interactions among these risks, which consequently may increase the risk of developing metastatic breast cancer. We proposed to apply a previously developed machine-learning method to discern risk factors of 5-, 10- and 15-year metastases. Methods: We applied a previously validated algorithm named the Markov Blanket and Interactive Risk Factor Learner (MBIL) to the electronic health record (EHR)-based Lynn Sage Database (LSDB) from the Lynn Sage Comprehensive Breast Center at Northwestern Memorial Hospital. This algorithm provided an output of both single and interactive risk factors of 5-, 10-, and 15-year metastases from the LSDB. We individually examined and interpreted the clinical relevance of these interactions based on years to metastasis and reliance on interactivity between risk factors. Results: We found that, with lower alpha values (low interactivity score), the prevalence of variables with an independent influence on long-term metastasis was higher (i.e., HER2, TNEG). As the value of alpha increased to 480, stronger interactions were needed to define clusters of factors that increased the risk of metastasis (i.e., ER, smoking, race, alcohol usage). Conclusion: MBIL identified single and interacting risk factors of metastatic breast cancer, many of which were supported by clinical evidence. These results strongly recommend the development of further large data studies with different databases to validate the degree to which some of these variables impact metastatic breast cancer in the long term. Full article

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13 pages, 2379 KiB

Open AccessEditor’s ChoiceArticle

Delta-Radiomics Predicts Response to First-Line Oxaliplatin-Based Chemotherapy in Colorectal Cancer Patients with Liver Metastases

by Valentina Giannini, Laura Pusceddu, Arianna Defeudis, Giulia Nicoletti, Giovanni Cappello, Simone Mazzetti, Andrea Sartore-Bianchi, Salvatore Siena, Angelo Vanzulli, Francesco Rizzetto, Elisabetta Fenocchio, Luca Lazzari, Alberto Bardelli, Silvia Marsoni and Daniele Regge

Cancers 2022, 14(1), 241; https://doi.org/10.3390/cancers14010241 - 4 Jan 2022

Cited by 19 | Viewed by 6591

Abstract

The purpose of this paper is to develop and validate a delta-radiomics score to predict the response of individual colorectal cancer liver metastases (lmCRC) to first-line FOLFOX chemotherapy. Three hundred one lmCRC were manually segmented on both CT performed at baseline and after [...] Read more.

The purpose of this paper is to develop and validate a delta-radiomics score to predict the response of individual colorectal cancer liver metastases (lmCRC) to first-line FOLFOX chemotherapy. Three hundred one lmCRC were manually segmented on both CT performed at baseline and after the first cycle of first-line FOLFOX, and 107 radiomics features were computed by subtracting textural features of CT at baseline from those at timepoint 1 (TP1). LmCRC were classified as nonresponders (R−) if they showed progression of disease (PD), according to RECIST1.1, before 8 months, and as responders (R+), otherwise. After feature selection, we developed a decision tree statistical model trained using all lmCRC coming from one hospital. The final output was a delta-radiomics signature subsequently validated on an external dataset. Sensitivity, specificity, positive (PPV), and negative (NPV) predictive values in correctly classifying individual lesions were assessed on both datasets. Per-lesion sensitivity, specificity, PPV, and NPV were 99%, 94%, 95%, 99%, 85%, 92%, 90%, and 87%, respectively, in the training and validation datasets. The delta-radiomics signature was able to reliably predict R− lmCRC, which were wrongly classified by lesion RECIST as R+ at TP1, (93%, averaging training and validation set, versus 67% of RECIST). The delta-radiomics signature developed in this study can reliably predict the response of individual lmCRC to oxaliplatin-based chemotherapy. Lesions forecasted as poor or nonresponders by the signature could be further investigated, potentially paving the way to lesion-specific therapies. Full article

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2021

Jump to: 2024, 2023, 2022

13 pages, 3010 KiB

Open AccessArticle

Feasibility of Synthetic Computed Tomography Images Generated from Magnetic Resonance Imaging Scans Using Various Deep Learning Methods in the Planning of Radiation Therapy for Prostate Cancer

by Gyu Sang Yoo, Huan Minh Luu, Heejung Kim, Won Park, Hongryull Pyo, Youngyih Han, Ju Young Park and Sung-Hong Park

Cancers 2022, 14(1), 40; https://doi.org/10.3390/cancers14010040 - 23 Dec 2021

Cited by 9 | Viewed by 3946

Abstract

We aimed to evaluate and compare the qualities of synthetic computed tomography (sCT) generated by various deep-learning methods in volumetric modulated arc therapy (VMAT) planning for prostate cancer. Simulation computed tomography (CT) and T2-weighted simulation magnetic resonance image from 113 patients were used [...] Read more.

We aimed to evaluate and compare the qualities of synthetic computed tomography (sCT) generated by various deep-learning methods in volumetric modulated arc therapy (VMAT) planning for prostate cancer. Simulation computed tomography (CT) and T2-weighted simulation magnetic resonance image from 113 patients were used in the sCT generation by three deep-learning approaches: generative adversarial network (GAN), cycle-consistent GAN (CycGAN), and reference-guided CycGAN (RgGAN), a new model which performed further adjustment of sCTs generated by CycGAN with available paired images. VMAT plans on the original simulation CT images were recalculated on the sCTs and the dosimetric differences were evaluated. For soft tissue, a significant difference in the mean Hounsfield unites (HUs) was observed between the original CT images and only sCTs from GAN (p = 0.03). The mean relative dose differences for planning target volumes or organs at risk were within 2% among the sCTs from the three deep-learning approaches. The differences in dosimetric parameters for D_98% and D_95% from original CT were lowest in sCT from RgGAN. In conclusion, HU conservation for soft tissue was poorest for GAN. There was the trend that sCT generated from the RgGAN showed best performance in dosimetric conservation D_98% and D_95% than sCTs from other methodologies. Full article

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36 pages, 5409 KiB

Open AccessReview

A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis

by Muhammad Firoz Mridha, Md. Abdul Hamid, Muhammad Mostafa Monowar, Ashfia Jannat Keya, Abu Quwsar Ohi, Md. Rashedul Islam and Jong-Myon Kim

Cancers 2021, 13(23), 6116; https://doi.org/10.3390/cancers13236116 - 4 Dec 2021

Cited by 47 | Viewed by 10227

Abstract

Breast cancer is now the most frequently diagnosed cancer in women, and its percentage is gradually increasing. Optimistically, there is a good chance of recovery from breast cancer if identified and treated at an early stage. Therefore, several researchers have established deep-learning-based automated [...] Read more.

Breast cancer is now the most frequently diagnosed cancer in women, and its percentage is gradually increasing. Optimistically, there is a good chance of recovery from breast cancer if identified and treated at an early stage. Therefore, several researchers have established deep-learning-based automated methods for their efficiency and accuracy in predicting the growth of cancer cells utilizing medical imaging modalities. As of yet, few review studies on breast cancer diagnosis are available that summarize some existing studies. However, these studies were unable to address emerging architectures and modalities in breast cancer diagnosis. This review focuses on the evolving architectures of deep learning for breast cancer detection. In what follows, this survey presents existing deep-learning-based architectures, analyzes the strengths and limitations of the existing studies, examines the used datasets, and reviews image pre-processing techniques. Furthermore, a concrete review of diverse imaging modalities, performance metrics and results, challenges, and research directions for future researchers is presented. Full article

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23 pages, 355 KiB

Open AccessReview

Scope of Artificial Intelligence in Gastrointestinal Oncology

by Hemant Goyal, Syed A. A. Sherazi, Rupinder Mann, Zainab Gandhi, Abhilash Perisetti, Muhammad Aziz, Saurabh Chandan, Jonathan Kopel, Benjamin Tharian, Neil Sharma and Nirav Thosani

Cancers 2021, 13(21), 5494; https://doi.org/10.3390/cancers13215494 - 1 Nov 2021

Cited by 12 | Viewed by 5460

Abstract

Gastrointestinal cancers are among the leading causes of death worldwide, with over 2.8 million deaths annually. Over the last few decades, advancements in artificial intelligence technologies have led to their application in medicine. The use of artificial intelligence in endoscopic procedures is a [...] Read more.

Gastrointestinal cancers are among the leading causes of death worldwide, with over 2.8 million deaths annually. Over the last few decades, advancements in artificial intelligence technologies have led to their application in medicine. The use of artificial intelligence in endoscopic procedures is a significant breakthrough in modern medicine. Currently, the diagnosis of various gastrointestinal cancer relies on the manual interpretation of radiographic images by radiologists and various endoscopic images by endoscopists. This can lead to diagnostic variabilities as it requires concentration and clinical experience in the field. Artificial intelligence using machine or deep learning algorithms can provide automatic and accurate image analysis and thus assist in diagnosis. In the field of gastroenterology, the application of artificial intelligence can be vast from diagnosis, predicting tumor histology, polyp characterization, metastatic potential, prognosis, and treatment response. It can also provide accurate prediction models to determine the need for intervention with computer-aided diagnosis. The number of research studies on artificial intelligence in gastrointestinal cancer has been increasing rapidly over the last decade due to immense interest in the field. This review aims to review the impact, limitations, and future potentials of artificial intelligence in screening, diagnosis, tumor staging, treatment modalities, and prediction models for the prognosis of various gastrointestinal cancers. Full article

14 pages, 16223 KiB

Open AccessArticle

Breast Invasive Ductal Carcinoma Classification on Whole Slide Images with Weakly-Supervised and Transfer Learning

by Fahdi Kanavati and Masayuki Tsuneki

Cancers 2021, 13(21), 5368; https://doi.org/10.3390/cancers13215368 - 26 Oct 2021

Cited by 18 | Viewed by 6829

Abstract

Invasive ductal carcinoma (IDC) is the most common form of breast cancer. For the non-operative diagnosis of breast carcinoma, core needle biopsy has been widely used in recent years for the evaluation of histopathological features, as it can provide a definitive diagnosis between [...] Read more.

Invasive ductal carcinoma (IDC) is the most common form of breast cancer. For the non-operative diagnosis of breast carcinoma, core needle biopsy has been widely used in recent years for the evaluation of histopathological features, as it can provide a definitive diagnosis between IDC and benign lesion (e.g., fibroadenoma), and it is cost effective. Due to its widespread use, it could potentially benefit from the use of AI-based tools to aid pathologists in their pathological diagnosis workflows. In this paper, we trained invasive ductal carcinoma (IDC) whole slide image (WSI) classification models using transfer learning and weakly-supervised learning. We evaluated the models on a core needle biopsy (n = 522) test set as well as three surgical test sets (n = 1129) obtaining ROC AUCs in the range of 0.95–0.98. The promising results demonstrate the potential of applying such models as diagnostic aid tools for pathologists in clinical practice. Full article

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17 pages, 3809 KiB

Open AccessArticle

Skin Lesion Classification Based on Surface Fractal Dimensions and Statistical Color Cluster Features Using an Ensemble of Machine Learning Techniques

by Simona Moldovanu, Felicia Anisoara Damian Michis, Keka C. Biswas, Anisia Culea-Florescu and Luminita Moraru

Cancers 2021, 13(21), 5256; https://doi.org/10.3390/cancers13215256 - 20 Oct 2021

Cited by 29 | Viewed by 4119

Abstract

(1) Background: An approach for skin cancer recognition and classification by implementation of a novel combination of features and two classifiers, as an auxiliary diagnostic method, is proposed. (2) Methods: The predictions are made by k-nearest neighbor with a 5-fold cross validation algorithm [...] Read more.

(1) Background: An approach for skin cancer recognition and classification by implementation of a novel combination of features and two classifiers, as an auxiliary diagnostic method, is proposed. (2) Methods: The predictions are made by k-nearest neighbor with a 5-fold cross validation algorithm and a neural network model to assist dermatologists in the diagnosis of cancerous skin lesions. As a main contribution, this work proposes a descriptor that combines skin surface fractal dimension and relevant color area features for skin lesion classification purposes. The surface fractal dimension is computed using a 2D generalization of Higuchi’s method. A clustering method allows for the selection of the relevant color distribution in skin lesion images by determining the average percentage of color areas within the nevi and melanoma lesion areas. In a classification stage, the Higuchi fractal dimensions (HFDs) and the color features are classified, separately, using a kNN-CV algorithm. In addition, these features are prototypes for a Radial basis function neural network (RBFNN) classifier. The efficiency of our algorithms was verified by utilizing images belonging to the 7-Point, Med-Node, and PH2 databases; (3) Results: Experimental results show that the accuracy of the proposed RBFNN model in skin cancer classification is 95.42% for 7-Point, 94.71% for Med-Node, and 94.88% for PH2, which are all significantly better than that of the kNN algorithm. (4) Conclusions: 2D Higuchi’s surface fractal features have not been previously used for skin lesion classification purpose. We used fractal features further correlated to color features to create a RBFNN classifier that provides high accuracies of classification. Full article

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25 pages, 4784 KiB

Open AccessReview

Artificial Intelligence in Brain Tumour Surgery—An Emerging Paradigm

by Simon Williams, Hugo Layard Horsfall, Jonathan P. Funnell, John G. Hanrahan, Danyal Z. Khan, William Muirhead, Danail Stoyanov and Hani J. Marcus

Cancers 2021, 13(19), 5010; https://doi.org/10.3390/cancers13195010 - 7 Oct 2021

Cited by 35 | Viewed by 8411

Abstract

Artificial intelligence (AI) platforms have the potential to cause a paradigm shift in brain tumour surgery. Brain tumour surgery augmented with AI can result in safer and more effective treatment. In this review article, we explore the current and future role of AI [...] Read more.

Artificial intelligence (AI) platforms have the potential to cause a paradigm shift in brain tumour surgery. Brain tumour surgery augmented with AI can result in safer and more effective treatment. In this review article, we explore the current and future role of AI in patients undergoing brain tumour surgery, including aiding diagnosis, optimising the surgical plan, providing support during the operation, and better predicting the prognosis. Finally, we discuss barriers to the successful clinical implementation, the ethical concerns, and we provide our perspective on how the field could be advanced. Full article

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19 pages, 1189 KiB

Open AccessArticle

Melanoma Recognition by Fusing Convolutional Blocks and Dynamic Routing between Capsules

by Eduardo Pérez and Sebastián Ventura

Cancers 2021, 13(19), 4974; https://doi.org/10.3390/cancers13194974 - 3 Oct 2021

Cited by 14 | Viewed by 3822

Abstract

Skin cancer is one of the most common types of cancers in the world, with melanoma being the most lethal form. Automatic melanoma diagnosis from skin images has recently gained attention within the machine learning community, due to the complexity involved. In the [...] Read more.

Skin cancer is one of the most common types of cancers in the world, with melanoma being the most lethal form. Automatic melanoma diagnosis from skin images has recently gained attention within the machine learning community, due to the complexity involved. In the past few years, convolutional neural network models have been commonly used to approach this issue. This type of model, however, presents disadvantages that sometimes hamper its application in real-world situations, e.g., the construction of transformation-invariant models and their inability to consider spatial hierarchies between entities within an image. Recently, Dynamic Routing between Capsules architecture (CapsNet) has been proposed to overcome such limitations. This work is aimed at proposing a new architecture which combines convolutional blocks with a customized CapsNet architecture, allowing for the extraction of richer abstract features. This architecture uses high-quality

299 \times 299 \times 3

skin lesion images, and a hyper-tuning of the main parameters is performed in order to ensure effective learning under limited training data. An extensive experimental study on eleven image datasets was conducted where the proposal significantly outperformed several state-of-the-art models. Finally, predictions made by the model were validated through the application of two modern model-agnostic interpretation tools. Full article

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17 pages, 2322 KiB

Open AccessArticle

Machine-Learning Assisted Discrimination of Precancerous and Cancerous from Healthy Oral Tissue Based on Multispectral Autofluorescence Lifetime Imaging Endoscopy

by Elvis Duran-Sierra, Shuna Cheng, Rodrigo Cuenca, Beena Ahmed, Jim Ji, Vladislav V. Yakovlev, Mathias Martinez, Moustafa Al-Khalil, Hussain Al-Enazi, Yi-Shing Lisa Cheng, John Wright, Carlos Busso and Javier A. Jo

Cancers 2021, 13(19), 4751; https://doi.org/10.3390/cancers13194751 - 23 Sep 2021

Cited by 23 | Viewed by 4058

Abstract

Multispectral autofluorescence lifetime imaging (maFLIM) can be used to clinically image a plurality of metabolic and biochemical autofluorescence biomarkers of oral epithelial dysplasia and cancer. This study tested the hypothesis that maFLIM-derived autofluorescence biomarkers can be used in machine-learning (ML) models to discriminate [...] Read more.

Multispectral autofluorescence lifetime imaging (maFLIM) can be used to clinically image a plurality of metabolic and biochemical autofluorescence biomarkers of oral epithelial dysplasia and cancer. This study tested the hypothesis that maFLIM-derived autofluorescence biomarkers can be used in machine-learning (ML) models to discriminate dysplastic and cancerous from healthy oral tissue. Clinical widefield maFLIM endoscopy imaging of cancerous and dysplastic oral lesions was performed at two clinical centers. Endoscopic maFLIM images from 34 patients acquired at one of the clinical centers were used to optimize ML models for automated discrimination of dysplastic and cancerous from healthy oral tissue. A computer-aided detection system was developed and applied to a set of endoscopic maFLIM images from 23 patients acquired at the other clinical center, and its performance was quantified in terms of the area under the receiver operating characteristic curve (ROC-AUC). Discrimination of dysplastic and cancerous from healthy oral tissue was achieved with an ROC-AUC of 0.81. This study demonstrates the capabilities of widefield maFLIM endoscopy to clinically image autofluorescence biomarkers that can be used in ML models to discriminate dysplastic and cancerous from healthy oral tissue. Widefield maFLIM endoscopy thus holds potential for automated in situ detection of oral dysplasia and cancer. Full article

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16 pages, 1164 KiB

Open AccessFeature PaperReview

Reinforcement Learning for Precision Oncology

by Jan-Niklas Eckardt, Karsten Wendt, Martin Bornhäuser and Jan Moritz Middeke

Cancers 2021, 13(18), 4624; https://doi.org/10.3390/cancers13184624 - 15 Sep 2021

Cited by 35 | Viewed by 5222

Abstract

Precision oncology is grounded in the increasing understanding of genetic and molecular mechanisms that underly malignant disease and offer different treatment pathways for the individual patient. The growing complexity of medical data has led to the implementation of machine learning techniques that are [...] Read more.

Precision oncology is grounded in the increasing understanding of genetic and molecular mechanisms that underly malignant disease and offer different treatment pathways for the individual patient. The growing complexity of medical data has led to the implementation of machine learning techniques that are vastly applied for risk assessment and outcome prediction using either supervised or unsupervised learning. Still largely overlooked is reinforcement learning (RL) that addresses sequential tasks by exploring the underlying dynamics of an environment and shaping it by taking actions in order to maximize cumulative rewards over time, thereby achieving optimal long-term outcomes. Recent breakthroughs in RL demonstrated remarkable results in gameplay and autonomous driving, often achieving human-like or even superhuman performance. While this type of machine learning holds the potential to become a helpful decision support tool, it comes with a set of distinctive challenges that need to be addressed to ensure applicability, validity and safety. In this review, we highlight recent advances of RL focusing on studies in oncology and point out current challenges and pitfalls that need to be accounted for in future studies in order to successfully develop RL-based decision support systems for precision oncology. Full article

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25 pages, 5988 KiB

Open AccessArticle

A Comprehensive Evaluation and Benchmarking of Convolutional Neural Networks for Melanoma Diagnosis

by Saeed Alzahrani, Baidaa Al-Bander and Waleed Al-Nuaimy

Cancers 2021, 13(17), 4494; https://doi.org/10.3390/cancers13174494 - 6 Sep 2021

Cited by 14 | Viewed by 4608

Abstract

Melanoma is the most invasive skin cancer with the highest risk of death. While it is a serious skin cancer, it is highly curable if detected early. Melanoma diagnosis is difficult, even for experienced dermatologists, due to the wide range of morphologies in [...] Read more.

Melanoma is the most invasive skin cancer with the highest risk of death. While it is a serious skin cancer, it is highly curable if detected early. Melanoma diagnosis is difficult, even for experienced dermatologists, due to the wide range of morphologies in skin lesions. Given the rapid development of deep learning algorithms for melanoma diagnosis, it is crucial to validate and benchmark these models, which is the main challenge of this work. This research presents a new benchmarking and selection approach based on the multi-criteria analysis method (MCDM), which integrates entropy and the preference ranking organization method for enrichment of evaluations (PROMETHEE) methods. The experimental study is carried out in four phases. Firstly, 19 convolution neural networks (CNNs) are trained and evaluated on a public dataset of 991 dermoscopic images. Secondly, to obtain the decision matrix, 10 criteria, including accuracy, classification error, precision, sensitivity, specificity, F1-score, false-positive rate, false-negative rate, Matthews correlation coefficient (MCC), and the number of parameters are established. Third, entropy and PROMETHEE methods are integrated to determine the weights of criteria and rank the models. Fourth, the proposed benchmarking framework is validated using the VIKOR method. The obtained results reveal that the ResNet101 model is selected as the optimal diagnosis model for melanoma in our case study data. Thus, the presented benchmarking framework is proven to be useful at exposing the optimal melanoma diagnosis model targeting to ease the selection process of the proper convolutional neural network architecture. Full article

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16 pages, 3333 KiB

Open AccessArticle

Automated PD-L1 Scoring Using Artificial Intelligence in Head and Neck Squamous Cell Carcinoma

by Behrus Puladi, Mark Ooms, Svetlana Kintsler, Khosrow Siamak Houschyar, Florian Steib, Ali Modabber, Frank Hölzle, Ruth Knüchel-Clarke and Till Braunschweig

Cancers 2021, 13(17), 4409; https://doi.org/10.3390/cancers13174409 - 31 Aug 2021

Cited by 20 | Viewed by 4400

Abstract

Immune checkpoint inhibitors (ICI) represent a new therapeutic approach in recurrent and metastatic head and neck squamous cell carcinoma (HNSCC). The patient selection for the PD-1/PD-L1 inhibitor therapy is based on the degree of PD-L1 expression in immunohistochemistry reflected by manually determined PD-L1 [...] Read more.

Immune checkpoint inhibitors (ICI) represent a new therapeutic approach in recurrent and metastatic head and neck squamous cell carcinoma (HNSCC). The patient selection for the PD-1/PD-L1 inhibitor therapy is based on the degree of PD-L1 expression in immunohistochemistry reflected by manually determined PD-L1 scores. However, manual scoring shows variability between different investigators and is influenced by cognitive and visual traps and could therefore negatively influence treatment decisions. Automated PD-L1 scoring could facilitate reliable and reproducible results. Our novel approach uses three neural networks sequentially applied for fully automated PD-L1 scoring of all three established PD-L1 scores: tumor proportion score (TPS), combined positive score (CPS) and tumor-infiltrating immune cell score (ICS). Our approach was validated using WSIs of HNSCC cases and compared with manual PD-L1 scoring by human investigators. The inter-rater correlation (ICC) between human and machine was very similar to the human-human correlation. The ICC was slightly higher between human-machine compared to human-human for the CPS and ICS, but a slightly lower for the TPS. Our study provides deeper insights into automated PD-L1 scoring by neural networks and its limitations. This may serve as a basis to improve ICI patient selection in the future. Full article

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15 pages, 11020 KiB

Open AccessArticle

Validation of a Point-of-Care Optical Coherence Tomography Device with Machine Learning Algorithm for Detection of Oral Potentially Malignant and Malignant Lesions

by Bonney Lee James, Sumsum P. Sunny, Andrew Emon Heidari, Ravindra D. Ramanjinappa, Tracie Lam, Anne V. Tran, Sandeep Kankanala, Shiladitya Sil, Vidya Tiwari, Sanjana Patrick, Vijay Pillai, Vivek Shetty, Naveen Hedne, Darshat Shah, Nameeta Shah, Zhong-ping Chen, Uma Kandasarma, Subhashini Attavar Raghavan, Shubha Gurudath, Praveen Birur Nagaraj, Petra Wilder-Smith, Amritha Suresh and Moni Abraham Kuriakose Show full author list Hide full author list

Cancers 2021, 13(14), 3583; https://doi.org/10.3390/cancers13143583 - 17 Jul 2021

Cited by 29 | Viewed by 4498

Abstract

Non-invasive strategies that can identify oral malignant and dysplastic oral potentially-malignant lesions (OPML) are necessary in cancer screening and long-term surveillance. Optical coherence tomography (OCT) can be a rapid, real time and non-invasive imaging method for frequent patient surveillance. Here, we report the [...] Read more.

Non-invasive strategies that can identify oral malignant and dysplastic oral potentially-malignant lesions (OPML) are necessary in cancer screening and long-term surveillance. Optical coherence tomography (OCT) can be a rapid, real time and non-invasive imaging method for frequent patient surveillance. Here, we report the validation of a portable, robust OCT device in 232 patients (lesions: 347) in different clinical settings. The device deployed with algorithm-based automated diagnosis, showed efficacy in delineation of oral benign and normal (n = 151), OPML (n = 121), and malignant lesions (n = 75) in community and tertiary care settings. This study showed that OCT images analyzed by automated image processing algorithm could distinguish the dysplastic-OPML and malignant lesions with a sensitivity of 95% and 93%, respectively. Furthermore, we explored the ability of multiple (n = 14) artificial neural network (ANN) based feature extraction techniques for delineation high grade-OPML (moderate/severe dysplasia). The support vector machine (SVM) model built over ANN, delineated high-grade dysplasia with sensitivity of 83%, which in turn, can be employed to triage patients for tertiary care. The study provides evidence towards the utility of the robust and low-cost OCT instrument as a point-of-care device in resource-constrained settings and the potential clinical application of device in screening and surveillance of oral cancer. Full article

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12 pages, 1624 KiB

Open AccessArticle

Deep Learning with Quantitative Features of Magnetic Resonance Images to Predict Biochemical Recurrence of Radical Prostatectomy: A Multi-Center Study

by Ye Yan, Lizhi Shao, Zhenyu Liu, Wei He, Guanyu Yang, Jiangang Liu, Haizhui Xia, Yuting Zhang, Huiying Chen, Cheng Liu, Min Lu, Lulin Ma, Kai Sun, Xuezhi Zhou, Xiongjun Ye, Lei Wang, Jie Tian and Jian Lu

Cancers 2021, 13(12), 3098; https://doi.org/10.3390/cancers13123098 - 21 Jun 2021

Cited by 24 | Viewed by 4296

Abstract

Biochemical recurrence (BCR) occurs in up to 27% of patients after radical prostatectomy (RP) and often compromises oncologic survival. To determine whether imaging signatures on clinical prostate magnetic resonance imaging (MRI) could noninvasively characterize biochemical recurrence and optimize treatment. We retrospectively enrolled 485 [...] Read more.

Biochemical recurrence (BCR) occurs in up to 27% of patients after radical prostatectomy (RP) and often compromises oncologic survival. To determine whether imaging signatures on clinical prostate magnetic resonance imaging (MRI) could noninvasively characterize biochemical recurrence and optimize treatment. We retrospectively enrolled 485 patients underwent RP from 2010 to 2017 in three institutions. Quantitative and interpretable features were extracted from T2 delineated tumors. Deep learning-based survival analysis was then applied to develop the deep-radiomic signature (DRS-BCR). The model’s performance was further evaluated, in comparison with conventional clinical models. The model achieved C-index of 0.802 in both primary and validating cohorts, outweighed the CAPRA-S score (0.677), NCCN model (0.586) and Gleason grade group systems (0.583). With application analysis, DRS-BCR model can significantly reduce false-positive predictions, so that nearly one-third of patients could benefit from the model by avoiding overtreatments. The deep learning-based survival analysis assisted quantitative image features from MRI performed well in prediction for BCR and has significant potential in optimizing systemic neoadjuvant or adjuvant therapies for prostate cancer patients. Full article

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5 pages, 552 KiB

Open AccessReply

Reply to Orlhac, F.; Buvat, I. Comment on “Ibrahim et al. The Effects of In-Plane Spatial Resolution on CT-Based Radiomic Features’ Stability with and without ComBat Harmonization. Cancers 2021, 13, 1848”

by Abdalla Ibrahim, Turkey Refaee, Sergey Primakov, Bruno Barufaldi, Raymond J. Acciavatti, Renée W. Y. Granzier, Roland Hustinx, Felix M. Mottaghy, Henry C. Woodruff, Joachim E. Wildberger, Philippe Lambin and Andrew D. A. Maidment

Cancers 2021, 13(12), 3080; https://doi.org/10.3390/cancers13123080 - 21 Jun 2021

Cited by 7 | Viewed by 2485

Abstract

We would like to thank Orlhac and Buvat [...] Full article

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Open AccessComment

Comment on Ibrahim et al. The Effects of In-Plane Spatial Resolution on CT-Based Radiomic Features’ Stability with and without ComBat Harmonization. Cancers 2021, 13, 1848

by Fanny Orlhac and Irène Buvat

Cancers 2021, 13(12), 3037; https://doi.org/10.3390/cancers13123037 - 18 Jun 2021

Cited by 11 | Viewed by 2403

Abstract

We read with great interest the paper by Ibrahim et al. [...] Full article

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13 pages, 942 KiB

Open AccessArticle

Modeling-Based Decision Support System for Radical Prostatectomy Versus External Beam Radiotherapy for Prostate Cancer Incorporating an In Silico Clinical Trial and a Cost–Utility Study

by Yvonka van Wijk, Bram Ramaekers, Ben G. L. Vanneste, Iva Halilaj, Cary Oberije, Avishek Chatterjee, Tom Marcelissen, Arthur Jochems, Henry C. Woodruff and Philippe Lambin

Cancers 2021, 13(11), 2687; https://doi.org/10.3390/cancers13112687 - 29 May 2021

Cited by 1 | Viewed by 3686

Abstract

The aim of this study is to build a decision support system (DSS) to select radical prostatectomy (RP) or external beam radiotherapy (EBRT) for low- to intermediate-risk prostate cancer patients. We used an individual state-transition model based on predictive models for estimating tumor [...] Read more.

The aim of this study is to build a decision support system (DSS) to select radical prostatectomy (RP) or external beam radiotherapy (EBRT) for low- to intermediate-risk prostate cancer patients. We used an individual state-transition model based on predictive models for estimating tumor control and toxicity probabilities. We performed analyses on a synthetically generated dataset of 1000 patients with realistic clinical parameters, externally validated by comparison to randomized clinical trials, and set up an in silico clinical trial for elderly patients. We assessed the cost-effectiveness (CE) of the DSS for treatment selection by comparing it to randomized treatment allotment. Using the DSS, 47.8% of synthetic patients were selected for RP and 52.2% for EBRT. During validation, differences with the simulations of late toxicity and biochemical failure never exceeded 2%. The in silico trial showed that for elderly patients, toxicity has more influence on the decision than TCP, and the predicted QoL depends on the initial erectile function. The DSS is estimated to result in cost savings (EUR 323 (95% CI: EUR 213–433)) and more quality-adjusted life years (QALYs; 0.11 years, 95% CI: 0.00–0.22) than randomized treatment selection. Full article

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23 pages, 2335 KiB

Open AccessReview

Enhancing Clinical Translation of Cancer Using Nanoinformatics

by Madjid Soltani, Farshad Moradi Kashkooli, Mohammad Souri, Samaneh Zare Harofte, Tina Harati, Atefeh Khadem, Mohammad Haeri Pour and Kaamran Raahemifar

Cancers 2021, 13(10), 2481; https://doi.org/10.3390/cancers13102481 - 19 May 2021

Cited by 34 | Viewed by 4631

Abstract

Application of drugs in high doses has been required due to the limitations of no specificity, short circulation half-lives, as well as low bioavailability and solubility. Higher toxicity is the result of high dosage administration of drug molecules that increase the side effects [...] Read more.

Application of drugs in high doses has been required due to the limitations of no specificity, short circulation half-lives, as well as low bioavailability and solubility. Higher toxicity is the result of high dosage administration of drug molecules that increase the side effects of the drugs. Recently, nanomedicine, that is the utilization of nanotechnology in healthcare with clinical applications, has made many advancements in the areas of cancer diagnosis and therapy. To overcome the challenge of patient-specificity as well as time- and dose-dependency of drug administration, artificial intelligence (AI) can be significantly beneficial for optimization of nanomedicine and combinatorial nanotherapy. AI has become a tool for researchers to manage complicated and big data, ranging from achieving complementary results to routine statistical analyses. AI enhances the prediction precision of treatment impact in cancer patients and specify estimation outcomes. Application of AI in nanotechnology leads to a new field of study, i.e., nanoinformatics. Besides, AI can be coupled with nanorobots, as an emerging technology, to develop targeted drug delivery systems. Furthermore, by the advancements in the nanomedicine field, AI-based combination therapy can facilitate the understanding of diagnosis and therapy of the cancer patients. The main objectives of this review are to discuss the current developments, possibilities, and future visions in naoinformatics, for providing more effective treatment for cancer patients. Full article

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19 pages, 2569 KiB

Open AccessArticle

Prediction of Microvascular Invasion in Hepatocellular Carcinoma via Deep Learning: A Multi-Center and Prospective Validation Study

by Jingwei Wei, Hanyu Jiang, Mengsu Zeng, Meiyun Wang, Meng Niu, Dongsheng Gu, Huanhuan Chong, Yanyan Zhang, Fangfang Fu, Mu Zhou, Jie Chen, Fudong Lyv, Hong Wei, Mustafa R. Bashir, Bin Song, Hongjun Li and Jie Tian

Cancers 2021, 13(10), 2368; https://doi.org/10.3390/cancers13102368 - 14 May 2021

Cited by 43 | Viewed by 4428

Abstract

Microvascular invasion (MVI) is a critical risk factor for postoperative recurrence of hepatocellular carcinoma (HCC). Preknowledge of MVI would assist tailored surgery planning in HCC management. In this multicenter study, we aimed to explore the validity of deep learning (DL) in MVI prediction [...] Read more.

Microvascular invasion (MVI) is a critical risk factor for postoperative recurrence of hepatocellular carcinoma (HCC). Preknowledge of MVI would assist tailored surgery planning in HCC management. In this multicenter study, we aimed to explore the validity of deep learning (DL) in MVI prediction using two imaging modalities—contrast-enhanced computed tomography (CE-CT) and gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI). A total of 750 HCCs were enrolled from five Chinese tertiary hospitals. Retrospective CE-CT (n = 306, collected between March, 2013 and July, 2019) and EOB-MRI (n = 329, collected between March, 2012 and March, 2019) data were used to train two DL models, respectively. Prospective external validation (n = 115, collected between July, 2015 and February, 2018) was performed to assess the developed models. Furthermore, DL-based attention maps were utilized to visualize high-risk MVI regions. Our findings revealed that the EOB-MRI-based DL model achieved superior prediction outcome to the CE-CT-based DL model (area under receiver operating characteristics curve (AUC): 0.812 vs. 0.736, p = 0.038; sensitivity: 70.4% vs. 57.4%, p = 0.015; specificity: 80.3% vs. 86.9%, p = 0.052). DL attention maps could visualize peritumoral high-risk areas with genuine histopathologic confirmation. Both DL models could stratify high and low-risk groups regarding progression free survival and overall survival (p < 0.05). Thus, DL can be an efficient tool for MVI prediction, and EOB-MRI was proven to be the modality with advantage for MVI assessment than CE-CT. Full article

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9 pages, 1173 KiB

Open AccessArticle

A Neural Network Approach to Identify Glioblastoma Progression Phenotype from Multimodal MRI

by Jiun-Lin Yan, Cheng-Hong Toh, Li Ko, Kuo-Chen Wei and Pin-Yuan Chen

Cancers 2021, 13(9), 2006; https://doi.org/10.3390/cancers13092006 - 21 Apr 2021

Cited by 6 | Viewed by 2770

Abstract

The phenotypes of glioblastoma (GBM) progression after treatment are heterogeneous in both imaging and clinical prognosis. This study aims to apply radiomics and neural network analysis to preoperative multimodal MRI data to characterize tumor progression phenotypes. We retrospectively reviewed 41 patients with newly [...] Read more.

The phenotypes of glioblastoma (GBM) progression after treatment are heterogeneous in both imaging and clinical prognosis. This study aims to apply radiomics and neural network analysis to preoperative multimodal MRI data to characterize tumor progression phenotypes. We retrospectively reviewed 41 patients with newly diagnosed cerebral GBM from 2009–2016 who comprised the machine learning training group, and prospectively included 18 patients from 2017–2018 for data validation. Preoperative MRI examinations included structural MRI, diffusion tensor imaging, and perfusion MRI. Tumor progression patterns were categorized as diffuse or localized. A supervised machine learning model and neural network-based models (VGG16 and ResNet50) were used to establish the prediction model of the pattern of progression. The diffuse progression pattern showed a significantly worse prognosis regarding overall survival (p = 0.032). A total of 153 of the 841 radiomic features were used to classify progression patterns using different machine learning models with an overall accuracy of 81% (range: 77.5–82.5%, AUC = 0.83–0.89). Further application of the pretrained ResNet50 and VGG 16 neural network models demonstrated an overall accuracy of 93.1 and 96.1%. The progression patterns of GBM are an important prognostic factor and can potentially be predicted by combining multimodal MR radiomics with machine learning. Full article

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16 pages, 1682 KiB

Open AccessArticle

The Effects of In-Plane Spatial Resolution on CT-Based Radiomic Features’ Stability with and without ComBat Harmonization

by Abdalla Ibrahim, Turkey Refaee, Sergey Primakov, Bruno Barufaldi, Raymond J. Acciavatti, Renée W. Y. Granzier, Roland Hustinx, Felix M. Mottaghy, Henry C. Woodruff, Joachim E. Wildberger, Philippe Lambin and Andrew D. A. Maidment

Cancers 2021, 13(8), 1848; https://doi.org/10.3390/cancers13081848 - 13 Apr 2021

Cited by 39 | Viewed by 3700

Abstract

While handcrafted radiomic features (HRFs) have shown promise in the field of personalized medicine, many hurdles hinder its incorporation into clinical practice, including but not limited to their sensitivity to differences in acquisition and reconstruction parameters. In this study, we evaluated the effects [...] Read more.

While handcrafted radiomic features (HRFs) have shown promise in the field of personalized medicine, many hurdles hinder its incorporation into clinical practice, including but not limited to their sensitivity to differences in acquisition and reconstruction parameters. In this study, we evaluated the effects of differences in in-plane spatial resolution (IPR) on HRFs, using a phantom dataset (n = 14) acquired on two scanner models. Furthermore, we assessed the effects of interpolation methods (IMs), the choice of a new unified in-plane resolution (NUIR), and ComBat harmonization on the reproducibility of HRFs. The reproducibility of HRFs was significantly affected by variations in IPR, with pairwise concordant HRFs, as measured by the concordance correlation coefficient (CCC), ranging from 42% to 95%. The number of concordant HRFs (CCC > 0.9) after resampling varied depending on (i) the scanner model, (ii) the IM, and (iii) the NUIR. The number of concordant HRFs after ComBat harmonization depended on the variations between the batches harmonized. The majority of IMs resulted in a higher number of concordant HRFs compared to ComBat harmonization, and the combination of IMs and ComBat harmonization did not yield a significant benefit. Our developed framework can be used to assess the reproducibility and harmonizability of RFs. Full article

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21 pages, 14076 KiB

Open AccessArticle

ImmunoAIzer: A Deep Learning-Based Computational Framework to Characterize Cell Distribution and Gene Mutation in Tumor Microenvironment

by Chang Bian, Yu Wang, Zhihao Lu, Yu An, Hanfan Wang, Lingxin Kong, Yang Du and Jie Tian

Cancers 2021, 13(7), 1659; https://doi.org/10.3390/cancers13071659 - 1 Apr 2021

Cited by 19 | Viewed by 4749

Abstract

Spatial distribution of tumor infiltrating lymphocytes (TILs) and cancer cells in the tumor microenvironment (TME) along with tumor gene mutation status are of vital importance to the guidance of cancer immunotherapy and prognoses. In this work, we developed a deep learning-based computational framework, [...] Read more.

Spatial distribution of tumor infiltrating lymphocytes (TILs) and cancer cells in the tumor microenvironment (TME) along with tumor gene mutation status are of vital importance to the guidance of cancer immunotherapy and prognoses. In this work, we developed a deep learning-based computational framework, termed ImmunoAIzer, which involves: (1) the implementation of a semi-supervised strategy to train a cellular biomarker distribution prediction network (CBDPN) to make predictions of spatial distributions of CD3, CD20, PanCK, and DAPI biomarkers in the tumor microenvironment with an accuracy of 90.4%; (2) using CBDPN to select tumor areas on hematoxylin and eosin (H&E) staining tissue slides and training a multilabel tumor gene mutation detection network (TGMDN), which can detect APC, KRAS, and TP53 mutations with area-under-the-curve (AUC) values of 0.76, 0.77, and 0.79. These findings suggest that ImmunoAIzer could provide comprehensive information of cell distribution and tumor gene mutation status of colon cancer patients efficiently and less costly; hence, it could serve as an effective auxiliary tool for the guidance of immunotherapy and prognoses. The method is also generalizable and has the potential to be extended for application to other types of cancers other than colon cancer. Full article

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18 pages, 4107 KiB

Open AccessArticle

Breast Tumor Characterization Using [¹⁸F]FDG-PET/CT Imaging Combined with Data Preprocessing and Radiomics

by Denis Krajnc, Laszlo Papp, Thomas S. Nakuz, Heinrich F. Magometschnigg, Marko Grahovac, Clemens P. Spielvogel, Boglarka Ecsedi, Zsuzsanna Bago-Horvath, Alexander Haug, Georgios Karanikas, Thomas Beyer, Marcus Hacker, Thomas H. Helbich and Katja Pinker

Cancers 2021, 13(6), 1249; https://doi.org/10.3390/cancers13061249 - 12 Mar 2021

Cited by 35 | Viewed by 5645

Abstract

Background: This study investigated the performance of ensemble learning holomic models for the detection of breast cancer, receptor status, proliferation rate, and molecular subtypes from [¹⁸F]FDG-PET/CT images with and without incorporating data pre-processing algorithms. Additionally, machine learning (ML) models were [...] Read more.

Background: This study investigated the performance of ensemble learning holomic models for the detection of breast cancer, receptor status, proliferation rate, and molecular subtypes from [¹⁸F]FDG-PET/CT images with and without incorporating data pre-processing algorithms. Additionally, machine learning (ML) models were compared with conventional data analysis using standard uptake value lesion classification. Methods: A cohort of 170 patients with 173 breast cancer tumors (132 malignant, 38 benign) was examined with [¹⁸F]FDG-PET/CT. Breast tumors were segmented and radiomic features were extracted following the imaging biomarker standardization initiative (IBSI) guidelines combined with optimized feature extraction. Ensemble learning including five supervised ML algorithms was utilized in a 100-fold Monte Carlo (MC) cross-validation scheme. Data pre-processing methods were incorporated prior to machine learning, including outlier and borderline noisy sample detection, feature selection, and class imbalance correction. Feature importance in each model was assessed by calculating feature occurrence by the R-squared method across MC folds. Results: Cross validation demonstrated high performance of the cancer detection model (80% sensitivity, 78% specificity, 80% accuracy, 0.81 area under the curve (AUC)), and of the triple negative tumor identification model (85% sensitivity, 78% specificity, 82% accuracy, 0.82 AUC). The individual receptor status and luminal A/B subtype models yielded low performance (0.46–0.68 AUC). SUV_max model yielded 0.76 AUC in cancer detection and 0.70 AUC in predicting triple negative subtype. Conclusions: Predictive models based on [¹⁸F]FDG-PET/CT images in combination with advanced data pre-processing steps aid in breast cancer diagnosis and in ML-based prediction of the aggressive triple negative breast cancer subtype. Full article

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