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Cancers
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Artificial Intelligence and MRI Characterization of Tumors
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Artificial Intelligence and MRI Characterization of Tumors

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Informatics and Big Data".

Deadline for manuscript submissions: closed (25 October 2023) | Viewed by 16377

Special Issue Editors

Dr. Eliodoro Faiella

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Guest Editor
1. Department Radiology, Università Campus Bio-Medico of Rome, Via Alvaro del Portillo 21, 00128 Rome, Italy
2. Department of Radiology, Sant'Anna Hospital, 22100 San Fermo della Battaglia (Co), Italy
Interests: diagnostic imaging and interventional radiology oncology; diagnostic imaging and interventional vascular radiology; musculosheletal diagnostics and intervention; urogynecological diagnostics; computed tomography; magnetic resonance imaging; ultrasound; radiomics; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Paolo Soda

E-Mail Website
Guest Editor
Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
Interests: AI; machine learning and big data analytics with applications to data signals; 2D and 3D image and video processing and analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Domiziana Santucci

E-Mail Website
Guest Editor
1. Department of Radiology, Università Campus Bio-Medico of Rome, Via Alvaro del Portillo 21, 00128 Rome, Italy
2. Department of Radiology, Sant'Anna Hospital, 22100 San Fermo della Battaglia, CO, Italy
Interests: breast diagnostics; prostate diagnostics; gynecological diagnostics; mammography; magnetic resonance imaging; artificial intelligence; radiomics; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Ermanno Cordelli

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Guest Editor
Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
Interests: artificial intelligence; machine learning; deep learning; medical imaging; precision medicine; radiomics; multimodal learning; decision support systems; federated learning; smart devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cancer diagnosis and management remain complex and frequently require a multi-imaging assessment that allows for the staging of local and systemic disease. MRI is a highly accurate technique for the diagnosis and assessment of local disease extension, while CT, 18F-FDG PET/CT, and scintigraphy are often used for the confirmation of lymph node and systemic localization. Other laboratory, genetic, and histological parameters are essential to aid diagnosis, stratify risk, predict prognosis, and monitor patients during follow-up. However, many of these tools are susceptible to significant subjectivity.

In recent years, imaging-based machine learning processes, referred to as artificial intelligence, have been employed in many oncological fields, with promising results in the support of medical decisions. This kind of analysis allows the extraction of a large number of quantitative characteristics from medical images, called “features”, providing physicians with a valid decision-making tool. Using artificial intelligence algorithms reduces the degree of subjectivity and uses fewer resources to improve overall efficiency and accuracy in the diagnosis and management of cancer.

In this Special Issue, we intend to enclose a current and important chapter on the role of artificial intelligence applied to various types of imaging modalities, in all phases of cancer evaluation, from diagnosis, to therapy, to prognosis. Both types of traditional machine learning approaches will be examined: radionics analysis and convolutional neural networks.

Dr. Eliodoro Faiella
Dr. Paolo Soda
Dr. Domiziana Santucci
Dr. Ermanno Cordelli
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

 

Keywords

  • cancer
  • MRI
  • CT
  • PET
  • 18F-FDG PET/CT
  • scintigraphy
  • artificial intelligence (AI)
  • radiomics
  • convolutional neural networks (CNN)

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Published Papers (6 papers)

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Research

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17 pages, 10241 KiB  
Article
Prediction of the Molecular Subtype of IDH Mutation Combined with MGMT Promoter Methylation in Gliomas via Radiomics Based on Preoperative MRI
by Yongjian Sha, Qianqian Yan, Yan Tan, Xiaochun Wang, Hui Zhang and Guoqiang Yang
Cancers 2023, 15(5), 1440; https://doi.org/10.3390/cancers15051440 - 24 Feb 2023
Cited by 4 | Viewed by 2242
Abstract
Background: The molecular subtype of IDH mut combined with MGMT meth in gliomas suggests a good prognosis and potential benefit from TMZ chemotherapy. The aim of this study was to establish a radiomics model to predict this molecular subtype. Method: The preoperative MR [...] Read more.
Background: The molecular subtype of IDH mut combined with MGMT meth in gliomas suggests a good prognosis and potential benefit from TMZ chemotherapy. The aim of this study was to establish a radiomics model to predict this molecular subtype. Method: The preoperative MR images and genetic data of 498 patients with gliomas were retrospectively collected from our institution and the TCGA/TCIA dataset. A total of 1702 radiomics features were extracted from the tumour region of interest (ROI) of CE-T1 and T2-FLAIR MR images. Least absolute shrinkage and selection operator (LASSO) and logistic regression were used for feature selection and model building. Receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the predictive performance of the model. Results: Regarding clinical variables, age and tumour grade were significantly different between the two molecular subtypes in the training, test and independent validation cohorts (p < 0.05). The areas under the curve (AUCs) of the radiomics model based on 16 selected features in the SMOTE training cohort, un-SMOTE training cohort, test set and independent TCGA/TCIA validation cohort were 0.936, 0.932, 0.916 and 0.866, respectively, and the corresponding F1-scores were 0.860, 0.797, 0.880 and 0.802. The AUC of the independent validation cohort increased to 0.930 for the combined model when integrating the clinical risk factors and radiomics signature. Conclusions: radiomics based on preoperative MRI can effectively predict the molecular subtype of IDH mut combined with MGMT meth. Full article
(This article belongs to the Special Issue Artificial Intelligence and MRI Characterization of Tumors)
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13 pages, 17308 KiB  
Article
3T-MRI Artificial Intelligence in Patients with Invasive Breast Cancer to Predict Distant Metastasis Status: A Pilot Study
by Alessandro Calabrese, Domiziana Santucci, Michela Gravina, Eliodoro Faiella, Ermanno Cordelli, Paolo Soda, Giulio Iannello, Carlo Sansone, Bruno Beomonte Zobel, Carlo Catalano and Carlo de Felice
Cancers 2023, 15(1), 36; https://doi.org/10.3390/cancers15010036 - 21 Dec 2022
Viewed by 1708
Abstract
Background: The incidence of breast cancer metastasis has decreased over the years. However, 20–30% of patients with early breast cancer still die from metastases. The purpose of this study is to evaluate the performance of a Deep Learning Convolutional Neural Networks (CNN) model [...] Read more.
Background: The incidence of breast cancer metastasis has decreased over the years. However, 20–30% of patients with early breast cancer still die from metastases. The purpose of this study is to evaluate the performance of a Deep Learning Convolutional Neural Networks (CNN) model to predict the risk of distant metastasis using 3T-MRI DCE sequences (Dynamic Contrast-Enhanced). Methods: A total of 157 breast cancer patients who underwent staging 3T-MRI examinations from January 2011 to July 2022 were retrospectively examined. Patient data, tumor histological and MRI characteristics, and clinical and imaging follow-up examinations of up to 7 years were collected. Of the 157 MRI examinations, 39/157 patients (40 lesions) had distant metastases, while 118/157 patients (120 lesions) were negative for distant metastases (control group). We analyzed the role of the Deep Learning technique using a single variable size bounding box (SVB) option and employed a Voxel Based (VB) NET CNN model. The CNN performance was evaluated in terms of accuracy, sensitivity, specificity, and area under the ROC curve (AUC). Results: The VB-NET model obtained a sensitivity, specificity, accuracy, and AUC of 52.50%, 80.51%, 73.42%, and 68.56%, respectively. A significant correlation was found between the risk of distant metastasis and tumor size, and the expression of PgR and HER2. Conclusions: We demonstrated a currently insufficient ability of the Deep Learning approach in predicting a distant metastasis status in patients with BC using CNNs. Full article
(This article belongs to the Special Issue Artificial Intelligence and MRI Characterization of Tumors)
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20 pages, 2883 KiB  
Article
MRI- and Histologic-Molecular-Based Radio-Genomics Nomogram for Preoperative Assessment of Risk Classes in Endometrial Cancer
by Veronica Celli, Michele Guerreri, Angelina Pernazza, Ilaria Cuccu, Innocenza Palaia, Federica Tomao, Violante Di Donato, Paola Pricolo, Giada Ercolani, Sandra Ciulla, Nicoletta Colombo, Martina Leopizzi, Valeria Di Maio, Eliodoro Faiella, Domiziana Santucci, Paolo Soda, Ermanno Cordelli, Giorgia Perniola, Benedetta Gui, Stefania Rizzo, Carlo Della Rocca, Giuseppe Petralia, Carlo Catalano and Lucia Manganaroadd Show full author list remove Hide full author list
Cancers 2022, 14(23), 5881; https://doi.org/10.3390/cancers14235881 - 29 Nov 2022
Cited by 20 | Viewed by 2553
Abstract
High- and low-risk endometrial carcinoma (EC) differ in whether or not a lymphadenectomy is performed. We aimed to develop MRI-based radio-genomic models able to preoperatively assess lymph-vascular space invasion (LVSI) and discriminate between low- and high-risk EC according to the ESMO-ESGO-ESTRO 2020 guidelines, [...] Read more.
High- and low-risk endometrial carcinoma (EC) differ in whether or not a lymphadenectomy is performed. We aimed to develop MRI-based radio-genomic models able to preoperatively assess lymph-vascular space invasion (LVSI) and discriminate between low- and high-risk EC according to the ESMO-ESGO-ESTRO 2020 guidelines, which include molecular risk classification proposed by “ProMisE”. This is a retrospective, multicentric study that included 64 women with EC who underwent 3T-MRI before a hysterectomy. Radiomics features were extracted from T2WI images and apparent diffusion coefficient maps (ADC) after manual segmentation of the gross tumor volume. We constructed a multiple logistic regression approach from the most relevant radiomic features to distinguish between low- and high-risk classes under the ESMO-ESGO-ESTRO 2020 guidelines. A similar approach was taken to assess LVSI. Model diagnostic performance was assessed via ROC curves, accuracy, sensitivity and specificity on training and test sets. The LVSI predictive model used a single feature from ADC as a predictor; the risk class model used two features as predictors from both ADC and T2WI. The low-risk predictive model showed an AUC of 0.74 with an accuracy, sensitivity, and specificity of 0.74, 0.76, 0.94; the LVSI model showed an AUC of 0.59 with an accuracy, sensitivity, and specificity of 0.60, 0.50, 0.61. MRI-based radio-genomic models are useful for preoperative EC risk stratification and may facilitate therapeutic management. Full article
(This article belongs to the Special Issue Artificial Intelligence and MRI Characterization of Tumors)
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19 pages, 1786 KiB  
Article
CNN-Based Approaches with Different Tumor Bounding Options for Lymph Node Status Prediction in Breast DCE-MRI
by Domiziana Santucci, Eliodoro Faiella, Michela Gravina, Ermanno Cordelli, Carlo de Felice, Bruno Beomonte Zobel, Giulio Iannello, Carlo Sansone and Paolo Soda
Cancers 2022, 14(19), 4574; https://doi.org/10.3390/cancers14194574 - 21 Sep 2022
Cited by 11 | Viewed by 2069
Abstract
Background: The axillary lymph node status (ALNS) is one of the most important prognostic factors in breast cancer (BC) patients, and it is currently evaluated by invasive procedures. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), highlights the physiological and morphological characteristics of primary tumor [...] Read more.
Background: The axillary lymph node status (ALNS) is one of the most important prognostic factors in breast cancer (BC) patients, and it is currently evaluated by invasive procedures. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), highlights the physiological and morphological characteristics of primary tumor tissue. Deep learning approaches (DL), such as convolutional neural networks (CNNs), are able to autonomously learn the set of features directly from images for a specific task. Materials and Methods: A total of 155 malignant BC lesions evaluated via DCE-MRI were included in the study. For each patient’s clinical data, the tumor histological and MRI characteristics and axillary lymph node status (ALNS) were assessed. LNS was considered to be the final label and dichotomized (LN+ (27 patients) vs. LN− (128 patients)). Based on the concept that peritumoral tissue contains valuable information about tumor aggressiveness, in this work, we analyze the contributions of six different tumor bounding options to predict the LNS using a CNN. These bounding boxes include a single fixed-size box (SFB), a single variable-size box (SVB), a single isotropic-size box (SIB), a single lesion variable-size box (SLVB), a single lesion isotropic-size box (SLIB), and a two-dimensional slice (2DS) option. According to the characteristics of the volumes considered as inputs, three different CNNs were investigated: the SFB-NET (for the SFB), the VB-NET (for the SVB, SIB, SLVB, and SLIB), and the 2DS-NET (for the 2DS). All the experiments were run in 10-fold cross-validation. The performance of each CNN was evaluated in terms of accuracy, sensitivity, specificity, the area under the ROC curve (AUC), and Cohen’s kappa coefficient (K). Results: The best accuracy and AUC are obtained by the 2DS-NET (78.63% and 77.86%, respectively). The 2DS-NET also showed the highest specificity, whilst the highest sensibility was attained by the VB-NET based on the SVB and SIB as bounding options. Conclusion: We have demonstrated that a selective inclusion of the DCE-MRI’s peritumoral tissue increases accuracy in the lymph node status prediction in BC patients using CNNs as a DL approach. Full article
(This article belongs to the Special Issue Artificial Intelligence and MRI Characterization of Tumors)
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Review

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23 pages, 1400 KiB  
Review
Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review
by Wilson Ong, Lei Zhu, Yi Liang Tan, Ee Chin Teo, Jiong Hao Tan, Naresh Kumar, Balamurugan A. Vellayappan, Beng Chin Ooi, Swee Tian Quek, Andrew Makmur and James Thomas Patrick Decourcy Hallinan
Cancers 2023, 15(6), 1837; https://doi.org/10.3390/cancers15061837 - 18 Mar 2023
Cited by 12 | Viewed by 3549
Abstract
An accurate diagnosis of bone tumours on imaging is crucial for appropriate and successful treatment. The advent of Artificial intelligence (AI) and machine learning methods to characterize and assess bone tumours on various imaging modalities may assist in the diagnostic workflow. The purpose [...] Read more.
An accurate diagnosis of bone tumours on imaging is crucial for appropriate and successful treatment. The advent of Artificial intelligence (AI) and machine learning methods to characterize and assess bone tumours on various imaging modalities may assist in the diagnostic workflow. The purpose of this review article is to summarise the most recent evidence for AI techniques using imaging for differentiating benign from malignant lesions, the characterization of various malignant bone lesions, and their potential clinical application. A systematic search through electronic databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 34 articles were retrieved from the databases and the key findings were compiled and summarised. A total of 34 articles reported the use of AI techniques to distinguish between benign vs. malignant bone lesions, of which 12 (35.3%) focused on radiographs, 12 (35.3%) on MRI, 5 (14.7%) on CT and 5 (14.7%) on PET/CT. The overall reported accuracy, sensitivity, and specificity of AI in distinguishing between benign vs. malignant bone lesions ranges from 0.44–0.99, 0.63–1.00, and 0.73–0.96, respectively, with AUCs of 0.73–0.96. In conclusion, the use of AI to discriminate bone lesions on imaging has achieved a relatively good performance in various imaging modalities, with high sensitivity, specificity, and accuracy for distinguishing between benign vs. malignant lesions in several cohort studies. However, further research is necessary to test the clinical performance of these algorithms before they can be facilitated and integrated into routine clinical practice. Full article
(This article belongs to the Special Issue Artificial Intelligence and MRI Characterization of Tumors)
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17 pages, 1471 KiB  
Review
Mapping Lymph Node during Indocyanine Green Fluorescence-Imaging Guided Gastric Oncologic Surgery: Current Applications and Future Directions
by Yiqun Liao, Jiahao Zhao, Yuji Chen, Bin Zhao, Yongkun Fang, Fei Wang, Chen Wei, Yichao Ma, Hao Ji, Daorong Wang and Dong Tang
Cancers 2022, 14(20), 5143; https://doi.org/10.3390/cancers14205143 - 20 Oct 2022
Cited by 4 | Viewed by 3075
Abstract
Huge strides have been made in the navigation of gastric cancer surgery thanks to the improvement of intraoperative techniques. For now, the use of indocyanine green (ICG) enhanced fluorescence imaging has received promising results in detecting sentinel lymph nodes (SLNs) and tracing lymphatic [...] Read more.
Huge strides have been made in the navigation of gastric cancer surgery thanks to the improvement of intraoperative techniques. For now, the use of indocyanine green (ICG) enhanced fluorescence imaging has received promising results in detecting sentinel lymph nodes (SLNs) and tracing lymphatic drainages, which make it applicable for limited and precise lymphadenectomy. Nevertheless, issues of the lack of specificity and unpredictable false-negative lymph nodes were encountered in gastric oncologic surgery practice using ICG-enhanced fluorescence imaging (ICG-FI), which restrict its application. Here, we reviewed the current application of ICG-FI and assessed potential approaches to improving ICG-FI. Full article
(This article belongs to the Special Issue Artificial Intelligence and MRI Characterization of Tumors)
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