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Radiomics and Machine Learning in Disease Diagnosis

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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 25606

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Special Issue Editors

Dr. Renato Cuocolo

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Department of Clinical Medicine and Surgery, University of Naples "Federico II", 80138 Naples, Italy
Interests: musculoskeletal imaging; prostate cancer; radiomics; machine learning; CT; MRI
Special Issues, Collections and Topics in MDPI journals

Dr. Gaia Spadarella

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Department of Advanced Biomedical Sciences, Università degli Studi di Napoli “Federico II”, Naples, Italy
Interests: systematic reviews and meta-analyses; oncology; radiomics; MRI

Dr. Lorenzo Ugga

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Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
Interests: neuroradiology; neurological sciences; neurosurgery; brain tumor; head and neck pathology; radiomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Interest in the potential of machine learning and radiomics has steadily grown over the past decade. These techniques have opened the door for innovative applications in the prediction of patient prognosis and in lesion characterization, expanding the amount of information obtainable from routine radiological exams. Machine learning also holds the potential to streamline radiologists’ clinical workflow by automating repetitive, menial tasks as well as increase overall efficiency, for example by improving image acquisition speed without quality loss. However, even as commercial solutions based on these tools are continually being launched, the need for research in this domain is greater than ever. In particular, validation of radiomics and machine learning software to demonstrate their clinical usefulness should be required by potential end users.

This Special Issue will be open to collect original research and review papers centered on radiomics and machine learning. The aim is to provide an avenue for preliminary and feasibility studies exploring new avenues for radiomics and machine learning in radiology, validation studies for previously developed models, and an overview of the current state of the art.

Dr. Renato Cuocolo
Dr. Gaia Spadarella
Dr. Lorenzo Ugga
Guest Editors

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Keywords

medical imaging
radiomics
texture analysis
computed tomography
magnetic resonance imaging
oncology
precision medicine

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Published Papers (9 papers)

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Research

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15 pages, 2171 KiB

Open AccessArticle

Papillary-Muscle-Derived Radiomic Features for Hypertrophic Cardiomyopathy versus Hypertensive Heart Disease Classification

by Qiming Liu, Qifan Lu, Yezi Chai, Zhengyu Tao, Qizhen Wu, Meng Jiang and Jun Pu

Diagnostics 2023, 13(9), 1544; https://doi.org/10.3390/diagnostics13091544 - 25 Apr 2023

Cited by 5 | Viewed by 1758

Abstract

Purpose: This study aimed to assess the value of radiomic features derived from the myocardium (MYO) and papillary muscle (PM) for left ventricular hypertrophy (LVH) detection and hypertrophic cardiomyopathy (HCM) versus hypertensive heart disease (HHD) differentiation. Methods: There were 345 subjects who underwent cardiovascular magnetic resonance (CMR) examinations that were analyzed. After quality control and manual segmentation, the 3D radiomic features were extracted from the MYO and PM. The data were randomly split into training (70%) and testing (30%) datasets. Feature selection was performed on the training dataset. Five machine learning models were evaluated using the MYO, PM, and MYO+PM features in the detection and differentiation tasks. The optimal differentiation model was further evaluated using CMR parameters and combined features. Results: Six features were selected for the MYO, PM, and MYO+PM groups. The support vector machine models performed best in both the detection and differentiation tasks. For LVH detection, the highest area under the curve (AUC) was 0.966 in the MYO group. For HCM vs. HHD differentiation, the best AUC was 0.935 in the MYO+PM group. Comparing the radiomics models to the CMR parameter models for the differentiation tasks, the radiomics models achieved significantly improved the performance (p = 0.002). Conclusions: The radiomics model with the MYO+PM features showed similar performance to the models developed from the MYO features in the detection task, but outperformed the models developed from the MYO or PM features in the differentiation task. In addition, the radiomic models performed better than the CMR parameters’ models. Full article

(This article belongs to the Special Issue Radiomics and Machine Learning in Disease Diagnosis)

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11 pages, 1909 KiB

Open AccessArticle

Differentiating Multiple Myeloma and Osteolytic Bone Metastases on Contrast-Enhanced Computed Tomography Scans: The Feasibility of Radiomics Analysis

by Seungeun Lee, So-Yeon Lee, Sanghee Kim, Yeon-Jung Huh, Jooyeon Lee, Ko-Eun Lee and Joon-Yong Jung

Diagnostics 2023, 13(4), 755; https://doi.org/10.3390/diagnostics13040755 - 16 Feb 2023

Cited by 1 | Viewed by 3433

Abstract

Osteolytic lesions can be seen in both multiple myeloma (MM), and osteolytic bone metastasis on computed tomography (CT) scans. We sought to assess the feasibility of a CT-based radiomics model to distinguish MM from metastasis. This study retrospectively included patients with pre-treatment thoracic or abdominal contrast-enhanced CT from institution 1 (training set: 175 patients with 425 lesions) and institution 2 (external test set: 50 patients with 85 lesions). After segmenting osteolytic lesions on CT images, 1218 radiomics features were extracted. A random forest (RF) classifier was used to build the radiomics model with 10-fold cross-validation. Three radiologists distinguished MM from metastasis using a five-point scale, both with and without the assistance of RF model results. Diagnostic performance was evaluated using the area under the curve (AUC). The AUC of the RF model was 0.807 and 0.762 for the training and test set, respectively. The AUC of the RF model and the radiologists (0.653–0.778) was not significantly different for the test set (p ≥ 0.179). The AUC of all radiologists was significantly increased (0.833–0.900) when they were assisted by RF model results (p < 0.001). In conclusion, the CT-based radiomics model can differentiate MM from osteolytic bone metastasis and improve radiologists’ diagnostic performance. Full article

(This article belongs to the Special Issue Radiomics and Machine Learning in Disease Diagnosis)

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27 pages, 3423 KiB

Open AccessArticle

Synergies of Radiomics and Transcriptomics in Lung Cancer Diagnosis: A Pilot Study

by Aikaterini Dovrou, Ekaterini Bei, Stelios Sfakianakis, Kostas Marias, Nickolas Papanikolaou and Michalis Zervakis

Diagnostics 2023, 13(4), 738; https://doi.org/10.3390/diagnostics13040738 - 15 Feb 2023

Cited by 5 | Viewed by 2516

Abstract

Radiotranscriptomics is an emerging field that aims to investigate the relationships between the radiomic features extracted from medical images and gene expression profiles that contribute in the diagnosis, treatment planning, and prognosis of cancer. This study proposes a methodological framework for the investigation of these associations with application on non-small-cell lung cancer (NSCLC). Six publicly available NSCLC datasets with transcriptomics data were used to derive and validate a transcriptomic signature for its ability to differentiate between cancer and non-malignant lung tissue. A publicly available dataset of 24 NSCLC-diagnosed patients, with both transcriptomic and imaging data, was used for the joint radiotranscriptomic analysis. For each patient, 749 Computed Tomography (CT) radiomic features were extracted and the corresponding transcriptomics data were provided through DNA microarrays. The radiomic features were clustered using the iterative K-means algorithm resulting in 77 homogeneous clusters, represented by meta-radiomic features. The most significant differentially expressed genes (DEGs) were selected by performing Significance Analysis of Microarrays (SAM) and 2-fold change. The interactions among the CT imaging features and the selected DEGs were investigated using SAM and a Spearman rank correlation test with a False Discovery Rate (FDR) of 5%, leading to the extraction of 73 DEGs significantly correlated with radiomic features. These genes were used to produce predictive models of the meta-radiomics features, defined as p-metaomics features, by performing Lasso regression. Of the 77 meta-radiomic features, 51 can be modeled in terms of the transcriptomic signature. These significant radiotranscriptomics relationships form a reliable basis to biologically justify the radiomics features extracted from anatomic imaging modalities. Thus, the biological value of these radiomic features was justified via enrichment analysis on their transcriptomics-based regression models, revealing closely associated biological processes and pathways. Overall, the proposed methodological framework provides joint radiotranscriptomics markers and models to support the connection and complementarities between the transcriptome and the phenotype in cancer, as demonstrated in the case of NSCLC. Full article

(This article belongs to the Special Issue Radiomics and Machine Learning in Disease Diagnosis)

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16 pages, 1910 KiB

Open AccessArticle

Primary Tumor Radiomic Model for Identifying Extrahepatic Metastasis of Hepatocellular Carcinoma Based on Contrast Enhanced Computed Tomography

by Lawrence Wing Chi Chan, Sze Chuen Cesar Wong, William Chi Shing Cho, Mohan Huang, Fei Zhang, Man Lik Chui, Una Ngo Yin Lai, Tiffany Yuen Kwan Chan, Zoe Hoi Ching Cheung, Jerry Chun Yin Cheung, Kin Fu Tang, Man Long Tse, Hung Kit Wong, Hugo Man Fung Kwok, Xinping Shen, Sailong Zhang and Keith Wan Hang Chiu

Diagnostics 2023, 13(1), 102; https://doi.org/10.3390/diagnostics13010102 - 29 Dec 2022

Cited by 2 | Viewed by 2215

Abstract

This study aimed to identify radiomic features of primary tumor and develop a model for indicating extrahepatic metastasis of hepatocellular carcinoma (HCC). Contrast-enhanced computed tomographic (CT) images of 177 HCC cases, including 26 metastatic (MET) and 151 non-metastatic (non-MET), were retrospectively collected and analyzed. For each case, 851 radiomic features, which quantify shape, intensity, texture, and heterogeneity within the segmented volume of the largest HCC tumor in arterial phase, were extracted using Pyradiomics. The dataset was randomly split into training and test sets. Synthetic Minority Oversampling Technique (SMOTE) was performed to augment the training set to 145 MET and 145 non-MET cases. The test set consists of six MET and six non-MET cases. The external validation set is comprised of 20 MET and 25 non-MET cases collected from an independent clinical unit. Logistic regression and support vector machine (SVM) models were identified based on the features selected using the stepwise forward method while the deep convolution neural network, visual geometry group 16 (VGG16), was trained using CT images directly. Grey-level size zone matrix (GLSZM) features constitute four of eight selected predictors of metastasis due to their perceptiveness to the tumor heterogeneity. The radiomic logistic regression model yielded an area under receiver operating characteristic curve (AUROC) of 0.944 on the test set and an AUROC of 0.744 on the external validation set. Logistic regression revealed no significant difference with SVM in the performance and outperformed VGG16 significantly. As extrahepatic metastasis workups, such as chest CT and bone scintigraphy, are standard but exhaustive, radiomic model facilitates a cost-effective method for stratifying HCC patients into eligibility groups of these workups. Full article

(This article belongs to the Special Issue Radiomics and Machine Learning in Disease Diagnosis)

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21 pages, 5032 KiB

Open AccessArticle

Lung Radiomics Features Selection for COPD Stage Classification Based on Auto-Metric Graph Neural Network

by Yingjian Yang, Shicong Wang, Nanrong Zeng, Wenxin Duan, Ziran Chen, Yang Liu, Wei Li, Yingwei Guo, Huai Chen, Xian Li, Rongchang Chen and Yan Kang

Diagnostics 2022, 12(10), 2274; https://doi.org/10.3390/diagnostics12102274 - 20 Sep 2022

Cited by 10 | Viewed by 2643

Abstract

Chronic obstructive pulmonary disease (COPD) is a preventable, treatable, progressive chronic disease characterized by persistent airflow limitation. Patients with COPD deserve special consideration regarding treatment in this fragile population for preclinical health management. Therefore, this paper proposes a novel lung radiomics combination vector generated by a generalized linear model (GLM) and Lasso algorithm for COPD stage classification based on an auto-metric graph neural network (AMGNN) with a meta-learning strategy. Firstly, the parenchyma images were segmented from chest high-resolution computed tomography (HRCT) images by ResU-Net. Second, lung radiomics features are extracted from the parenchyma images by PyRadiomics. Third, a novel lung radiomics combination vector (3 + 106) is constructed by the GLM and Lasso algorithm for determining the radiomics risk factors (K = 3) and radiomics node features (d = 106). Last, the COPD stage is classified based on the AMGNN. The results show that compared with the convolutional neural networks and machine learning models, the AMGNN based on constructed novel lung radiomics combination vector performs best, achieving an accuracy of 0.943, precision of 0.946, recall of 0.943, F1-score of 0.943, and ACU of 0.984. Furthermore, it is found that our method is effective for COPD stage classification. Full article

(This article belongs to the Special Issue Radiomics and Machine Learning in Disease Diagnosis)

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17 pages, 4184 KiB

Open AccessArticle

Comparison of Multiple Radiomics Models for Identifying Histological Grade of Pancreatic Ductal Adenocarcinoma Preoperatively Based on Multiphasic Contrast-Enhanced Computed Tomography: A Two-Center Study in Southwest China

by Hongfan Liao, Yongmei Li, Yaying Yang, Huan Liu, Jiao Zhang, Hongwei Liang, Gaowu Yan and Yanbing Liu

Diagnostics 2022, 12(8), 1915; https://doi.org/10.3390/diagnostics12081915 - 8 Aug 2022

Cited by 4 | Viewed by 2091

Abstract

Background: We designed and validated the value of multiple radiomics models for diagnosing histological grade of pancreatic ductal adenocarcinoma (PDAC), holding a promise of assisting in precision medicine and providing clinical therapeutic strategies. Methods: 198 PDAC patients receiving surgical resection and pathological confirmation were enrolled and classified as 117 low-grade PDAC and 81 high-grade PDAC group. An external validation group was used to assess models’ performance. Available radiomics features were selected using GBDT algorithm on the basis of the arterial and venous phases, respectively. Five different machine learning models were built including k-nearest neighbour, logistic regression, naive bayes model, support vector machine, and random forest using ten times tenfold cross-validation. Multivariable logistic regression analysis was applied to establish clinical model and combined model. The models’ performance was assessed according to its predictive performance, calibration curves, and decision curves. A nomogram was established for visualization. Survival analysis was conducted for stratifying the overall survival prior to treatment. Results: In the training group, the RF model demonstrated the optimal predictive ability and robustness with an AUC of 0.943; the SVM model achieved the secondary performance, followed by Bayes model. In the external validation group, these three models (Bayes, RF, SVM) also achieved the top three predictive ability. A clinical model was built by selected clinical features with an AUC of 0.728, and combined model was established by an RF model and a clinical model with an AUC of 0.961. The log-rank test revealed that the low-grade group survived longer than the high-grade group. Conclusions: The multiphasic CECT radiomics models offered an accurate and noninvasive perspective to differentiate histological grade in PDAC and advantages of machine learning models including RF, SVM and Bayes were more remarkable. Full article

(This article belongs to the Special Issue Radiomics and Machine Learning in Disease Diagnosis)

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23 pages, 2817 KiB

Open AccessArticle

Novel Survival Features Generated by Clinical Text Information and Radiomics Features May Improve the Prediction of Ischemic Stroke Outcome

by Yingwei Guo, Yingjian Yang, Fengqiu Cao, Wei Li, Mingming Wang, Yu Luo, Jia Guo, Asim Zaman, Xueqiang Zeng, Xiaoqiang Miu, Longyu Li, Weiyan Qiu and Yan Kang

Diagnostics 2022, 12(7), 1664; https://doi.org/10.3390/diagnostics12071664 - 8 Jul 2022

Cited by 10 | Viewed by 2183

Abstract

Background: Accurate outcome prediction is of great clinical significance in customizing personalized treatment plans, reducing the situation of poor recovery, and objectively and accurately evaluating the treatment effect. This study intended to evaluate the performance of clinical text information (CTI), radiomics features, and survival features (SurvF) for predicting functional outcomes of patients with ischemic stroke. Methods: SurvF was constructed based on CTI and mRS radiomics features (mRSRF) to improve the prediction of the functional outcome in 3 months (90-day mRS). Ten machine learning models predicted functional outcomes in three situations (2-category, 4-category, and 7-category) using seven feature groups constructed by CTI, mRSRF, and SurvF. Results: For 2-category, ALL (CTI + mRSRF+ SurvF) performed best, with an mAUC of 0.884, mAcc of 0.864, mPre of 0.877, mF1 of 0.86, and mRecall of 0.864. For 4-category, ALL also achieved the best mAuc of 0.787, while CTI + SurvF achieved the best score with mAcc = 0.611, mPre = 0.622, mF1 = 0.595, and mRe-call = 0.611. For 7-category, CTI + SurvF performed best, with an mAuc of 0.788, mPre of 0.519, mAcc of 0.529, mF1 of 0.495, and mRecall of 0.47. Conclusions: The above results indicate that mRSRF + CTI can accurately predict functional outcomes in ischemic stroke patients with proper machine learning models. Moreover, combining SurvF will improve the prediction effect compared with the original features. However, limited by the small sample size, further validation on larger and more varied datasets is necessary. Full article

(This article belongs to the Special Issue Radiomics and Machine Learning in Disease Diagnosis)

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11 pages, 1821 KiB

Open AccessArticle

Detecting Multiple Myeloma Infiltration of the Bone Marrow on CT Scans in Patients with Osteopenia: Feasibility of Radiomics Analysis

by Hyerim Park, So-Yeon Lee, Jooyeon Lee, Juyoung Pak, Koeun Lee, Seung-Eun Lee and Joon-Yong Jung

Diagnostics 2022, 12(4), 923; https://doi.org/10.3390/diagnostics12040923 - 7 Apr 2022

Cited by 6 | Viewed by 2389

Abstract

It is difficult to detect multiple myeloma (MM) infiltration of the bone marrow on computed tomography (CT) scans of patients with osteopenia. Our aim is to determine the feasibility of using radiomics analysis to detect MM infiltration of the bone marrow on CT scans of patients with osteopenia. The contrast-enhanced thoracic CT scans of 104 patients with MM and 104 age- and sex-matched controls were retrospectively evaluated. All individuals had decreased bone density on radiography. The study group was divided into development (n = 160) and temporal validation sets (n = 48). The radiomics model was developed using 805 texture features extracted from the bone marrow for a development set, using a Random Forest algorithm. The developed models were applied to evaluate a temporal validation set. For comparison, three radiologists evaluated the CTs for the possibility of MM infiltration in the bone marrow. The diagnostic performances were assessed and compared using an area under the receiver operating characteristic curve (AUC) analysis. The AUC of the radiomics model was not significantly different from those of the radiologists (p = 0.056–0.821). The radiomics analysis results showed potential for detecting MM infiltration in the bone marrow on CT scans of patients with osteopenia. Full article

(This article belongs to the Special Issue Radiomics and Machine Learning in Disease Diagnosis)

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Review

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28 pages, 2023 KiB

Open AccessReview

An Overview of In Vitro Assays of ⁶⁴Cu-, ⁶⁸Ga-, ¹²⁵I-, and ^99mTc-Labelled Radiopharmaceuticals Using Radiometric Counters in the Era of Radiotheranostics

by Viviana Benfante, Alessandro Stefano, Muhammad Ali, Riccardo Laudicella, Walter Arancio, Antonino Cucchiara, Fabio Caruso, Francesco Paolo Cammarata, Claudia Coronnello, Giorgio Russo, Monica Miele, Alessandra Vieni, Antonino Tuttolomondo, Anthony Yezzi and Albert Comelli

Diagnostics 2023, 13(7), 1210; https://doi.org/10.3390/diagnostics13071210 - 23 Mar 2023

Cited by 12 | Viewed by 4964

Abstract

Radionuclides are unstable isotopes that mainly emit alpha (α), beta (β) or gamma (γ) radiation through radiation decay. Therefore, they are used in the biomedical field to label biomolecules or drugs for diagnostic imaging applications, such as positron emission tomography (PET) and/or single-photon emission computed tomography (SPECT). A growing field of research is the development of new radiopharmaceuticals for use in cancer treatments. Preclinical studies are the gold standard for translational research. Specifically, in vitro radiopharmaceutical studies are based on the use of radiopharmaceuticals directly on cells. To date, radiometric β- and γ-counters are the only tools able to assess a preclinical in vitro assay with the aim of estimating uptake, retention, and release parameters, including time- and dose-dependent cytotoxicity and kinetic parameters. This review has been designed for researchers, such as biologists and biotechnologists, who would like to approach the radiobiology field and conduct in vitro assays for cellular radioactivity evaluations using radiometric counters. To demonstrate the importance of in vitro radiopharmaceutical assays using radiometric counters with a view to radiogenomics, many studies based on ⁶⁴Cu-, ⁶⁸Ga-, ¹²⁵I-, and ^99mTc-labeled radiopharmaceuticals have been revised and summarized in this manuscript. Full article

(This article belongs to the Special Issue Radiomics and Machine Learning in Disease Diagnosis)

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