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Journal of Medical Signals and Sensors، جلد ۱۴، شماره ۱۲، صفحات ۱۰-۴۱۰۳
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عنوان انگلیسی Radiomics based Machine Learning Models for Classification of Prostate Cancer Grade Groups from Multi Parametric MRI Images
چکیده انگلیسی مقاله Abstract Purpose:  This study aimed to investigate the performance of multiparametric magnetic resonance imaging (mpMRI) radiomic feature-based machine learning (ML) models in classifying the Gleason grade group (GG) of prostate cancer. Methods:  In this retrospective study, a total of 203 patients with histopathologically confirmed prostate cancer who underwent mpMRI before prostate biopsy were included. After manual segmentation, radiomic features (RFs) were extracted from T2-weighted, apparent diffusion coefficient, and high b-value diffusion-weighted magnetic resonance imaging (DWMRI). Patients were split into training sets and testing sets according to a ratio of 8:2. A pipeline considering combinations of two feature selection (FS) methods and six ML classifiers was developed and evaluated. The performance of models was assessed using the accuracy, sensitivity, precision, F1-measure, and the area under curve (AUC). Results:  On high b-value DWMRI-derived features, a combination of FS method recursive feature elimination (RFE) and classifier random forest achieved the highest performance for classification of prostate cancer into five GGs, with 97.0% accuracy, 98.0% sensitivity, 98.0% precision, and 97.0% F1-measure. The method also achieved an average AUC for GG of 98%. Conclusion:  Preoperative mpMRI radiomic analysis based on ML, as a noninvasive approach, showed good performance for classification of prostate cancer into five GGs. Advances in Knowledge:  Herein, radiomic models based on preoperative mpMRI and ML were developed to classify prostate cancer into 5 GGs. Our study provides evidence that analysis of quantitative RFs extracted from high b-value DWMRI images based on a combination of FS method RFE and classifier random forest can be applied for multiclass grading of prostate cancer with an accuracy of 97.0%.
کلیدواژه‌های انگلیسی مقاله Gleason grading, machine learning, multiparametric magnetic resonance imaging, prostate cancer, radiomics
نویسندگان مقاله | Fatemeh Zandie
Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran


| Mohammad Salehi
Department of Radiation Sciences, School of Allied Medicine, Iran University of Medical Sciences, Tehran, Iran


| Asghar Maziar
Department of Radiotherapy and Medical Physics, Faculty of Para Medicine, Arak University of Medical Sciences and Khansari Hospital, Arak, Iran


| Reza Bayatiani
Department of Radiation Sciences, School of Allied Medicine, Iran University of Medical Sciences, Tehran, Iran


| Reza Paydar


نشانی اینترنتی http://jmss.mui.ac.ir/index.php/jmss/article/view/735
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زبان مقاله منتشر شده en
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نوع مقاله منتشر شده Original Articles
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