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A retrospective cohort of 203 histopathologically confirmed patients underwent mpMRI prior to prostate biopsy. After manual segmentation, radiomic features were extracted from T2-weighted, apparent diffusion coefficient, and high b-value diffusion-weighted MRI. Models were evaluated with an 8:2 training-testing split across combinations of two feature selection methods and six classifiers, using accuracy, sensitivity, precision, F1-measure, and AUC. On high b-value DWMRI features, recursive feature elimination with random forest achieved the best multiclass performance, reaching 97.0% accuracy and ~98% AUC and F1. Results support noninvasive preoperative mpMRI radiomic ML grading into five GGs.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/radiomics-based-machine-learning-models-for-classification-of-prostate-cancer-grade-groups-from-multi-parametric-mri-images/332911/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/radiomics-based-machine-learning-models-for-classification-of-prostate-cancer-grade-groups-from-multi-parametric-mri-images/332911.png","ImageObject",300,407,{"name":92,"@type":93},"Theodora","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-25","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the study’s main purpose?","Question",{"text":112,"@type":113},"To evaluate how multiparametric MRI radiomics feature-based machine learning models perform in classifying the Gleason grade group of prostate cancer.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were radiomic features and models constructed?",{"text":117,"@type":113},"Radiomic features were extracted after manual segmentation from T2-weighted, apparent diffusion coefficient, and high b-value diffusion-weighted MRI. The study used combinations of two feature selection methods and six machine-learning classifiers, with an 8:2 training-testing split.",{"name":119,"@type":110,"acceptedAnswer":120},"Which model achieved the best classification performance, and what were the results?",{"text":121,"@type":113},"Using high b-value DWMRI-derived features, recursive feature elimination (RFE) with a random forest classifier achieved the highest performance for five grade groups, with 97.0% accuracy, 98.0% sensitivity, 98.0% precision, and 97.0% F1-measure, plus an average AUC of about 98%.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},332911,1790578336,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144,"preview_page_count":4},687197207919,"https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552","Original Article  \nRadiomics based Machine Learning Models for Classification of Prostate Cancer Grade Groups from Multi Parametric MRI Images  \nAbstract  \nPurpose: 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 toa 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% .  \nKeywords: Gleason grading, machine learning, multiparametric magnetic resonance imaging,  \nprostate cancer, radiomics   \nFatemeh Zandie1, Mohammad Salehi2, Asghar Maziar1, Mohammad  \nReza Bayatiani3,  \nReza Paydar1  \n1Department of Radiation Sciences, School of Allied Medicine, Iran University of Medical Sciences, 2Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, 3Department of Radiotherapy and Medical Physics, Faculty of Para Medicine, Arak University of Medical Sciences and Khansari Hospital, Arak, Iran  \nSubmitted: 03-Oct-2023 Revised: 24-Aug-2024 Accepted: 13-Sep-2024 Published: 03-Dec-2024  \nIntroduction  \nProstate cancer is the second most prevalent malignancy in the male population worldwide. [1] According to the statistics of the American Cancer Society, 268,490 new prostate cancer cases and 34,500 prostate cancer-related deaths would have been estimated in the United States in 2022.[2] The ability to accurately and timely identify the aggressiveness risk of prostate cancer can improve the selection of the most suitable treatment for these patients; therefore, it can be resulted in decreasing prostate cancer morbidity and mortality. [3] Therefore, it is crucial to preoperatively predict the grade of prostate cancer for treatment decision-making.  \nThis is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution‑NonCommercial‑ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non‑commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.  \n[For reprints contact:](For reprints contact: WKHLRPMedknow_reprints@wolterskluwer.com)[ WKHLRPMedknow_reprints@wolterskluwer.com](For reprints contact: WKHLRPMedknow_reprints@wolterskluwer.com)  \nCurrently, the standard approach for grading of prostate cancer is transrectal ultrasound (TRUS)-guided prostate biopsy.[4] The Gleason score","cbCairMsrqvtgWi4","https://ap.wps.com/l/cbCairMsrqvtgWi4","pdf",1533589,"English","# Introduction\n## Prostate cancer grading and clinical motivation\n# Methods\n## Patient cohort and mpMRI acquisition\n## Radiomic feature extraction and preprocessing\n## Feature selection and machine-learning classifiers\n# Results\n## Best-performing model and metrics\n# Conclusion\n## Clinical value of noninvasive preoperative grading","[{\"question\":\"What is the study’s main purpose?\",\"answer\":\"To evaluate how multiparametric MRI radiomics feature-based machine learning models perform in classifying the Gleason grade group of prostate cancer.\"},{\"question\":\"How were radiomic features and models constructed?\",\"answer\":\"Radiomic features were extracted after manual segmentation from T2-weighted, apparent diffusion coefficient, and high b-value diffusion-weighted MRI. The study used combinations of two feature selection methods and six machine-learning classifiers, with an 8:2 training-testing split.\"},{\"question\":\"Which model achieved the best classification performance, and what were the results?\",\"answer\":\"Using high b-value DWMRI-derived features, recursive feature elimination (RFE) with a random forest classifier achieved the highest performance for five grade groups, with 97.0% accuracy, 98.0% sensitivity, 98.0% precision, and 97.0% F1-measure, plus an average AUC of about 98%.\"}]","Radiomics based Machine Learning Models for Classification of Prostate Cancer Grade Groups from Multi Parametric MRI Images | PDF",1789999965,23]