[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127910-en":3,"doc-seo-127910-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127910,137451207643,"Noah","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Machine learning-based multiparametric MRI radiomics nomogram for predicting WHO/ISUP nuclear grading of clear cell renal cell carcinoma","A machine learning-based multiparametric MRI radiomics nomogram was developed to predict preoperative WHO/ISUP nuclear grading in patients with clear cell renal cell carcinoma (ccRCC). Retrospective data from 86 patients with preoperative MRI scans (plain and enhanced) were analyzed using radiomics features from FS-T2WI, DWI, and CE-T1WI. Patients were split into training and testing sets at a 7:3 ratio, and optimal features were selected via Mann-Whitney U test, Spearman correlation, and LASSO. The final model selected six features and used rad-score with independent clinical factors to construct a calibrated nomogram with strong ROC performance and clinical utility.","TYPE Original Research PUBLISHED 07 November 2024 DOI 10.3389/fonc.2024.1467775  \nOPEN ACCESS  \nEDITED BY  \nWenle Li,  \nXiamen University, China  \nREVIEWED BY  \nPeimeng You,  \nCapital Medical University, China Jinxiang Shang,  \nThe Affiliated Hospital of Shaoxing University, China  \n*CORRESPONDENCE  \nJianjun Zhang  \n [2665938999@qq.com](2665938999@qq.com)  \nRECEIVED 20 July 2024  \nACCEPTED 18 October 2024  \nPUBLISHED 07 November 2024  \nCITATION  \nYang Y, Zhang Z, Zhang H, Liu M and Zhang J (2024) Machine learning-based multiparametric MRI radiomics nomogram for predicting WHO/ISUP nuclear grading of clear cell renal cell carcinoma.  \nFront. Oncol. 14:1467775 .  \ndoi: 10.3389/fonc.2024.1467775  \nCOPYRIGHT  \n© 2024 Yang, Zhang, Zhang, Liu and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning-based multiparametric MRI radiomicsnomogram for predicting WHO/ ISUP nuclear grading of clear cell renal cell carcinoma  \nYunze Yang1,2, Ziwei Zhang 1,2, Hua Zhang 1,2, Mengtong Liu 2,3 and Jianjun Zhang 1*  \n1 Department of Radiology, Baoding First Central Hospital, Baoding, China, 2 Department of Postgraduate, Chengde Medical University, Chengde, China, 3 Department of Postgraduate, Hebei Medical University, Shijiazhuang, China  \nObjective: To explore the effectiveness of a machine learning-based multiparametric MRI radiomics nomogram for predicting the WHO/ISUP nuclear grading of clear cell renal cell carcinoma (ccRCC) before surgery.  \nMethods: Data from 86 patients who underwent preoperative renal MRI scans (both plain and enhanced) and were confirmed to have ccRCC were retrospectively collected. Based on the 2016 WHO/ISUP grading standards, patients were divided into a low-grade group (Grade I and II) and a high-grade group (Grade III and IV), and randomly split into training and testing sets at a 7:3 ratio. Radiomics features were extracted from FS-T2WI, DWI, and CE-T1WI sequences. Optimal features were selected using the Mann-Whitney U test, Spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO) . Five machine learning classifiers—logistic regression (LR), naive bayes (NB), k-nearest neighbors (KNN), adaptive boosting (AdaBoost), and multilayer perceptron (MLP)—were used to build models to predict ccRCC WHO/ISUP nuclear grading. The model with the highest area under the curve (AUC) in the testing set was chosen as the best radiomics model. Independent clinical risk factors were identified using univariate and multivariate logistic regression to create a clinical model, which was combined with radiomicsscore (rad-score) to develop a nomogram. The model ’s effectiveness was assessed using the receiver operating characteristic (ROC) curve, its calibration was evaluated using a calibration curve, and its clinical utility was analyzed using decision curve analysis.  \nResults: Six radiomics features were ultimately selected. The MLP classifier showed the highest diagnostic performance in the testing set (AUC=0 . 933) . Corticomedullary enhancement level (P=0.020) and renal vein invasion (P=0.011) were identified as independent risk factors for predicting the WHO/ISUP nuclear classification and were included in the nomogram with the rad-score. The ROC curves indicated that the nomogram model had strong diagnostic performance, with AUC values of 0 . 964 in the training set and 0 . 933 in the testing set.  \nFrontiers in Oncology 01 [frontiersin.org](frontiersin.org)  \nConclusion: The machine learning-based multiparametric MRI radiomicsnomogram provides a highly predictive, non-invasive tool f","cbCairrSOKhv4rke","https://ap.wps.com/l/cbCairrSOKhv4rke","pdf",3169296,4,1,10,"English","en",105,"# Objective\n# Methods\n## Study design and dataset\n## Radiomics feature extraction and model building\n## Clinical model and nomogram construction\n## Evaluation metrics (ROC, calibration, decision curve)\n# Results\n# Conclusion","[{\"question\":\"What problem does the radiomics nomogram address?\",\"answer\":\"It predicts preoperative WHO/ISUP nuclear grading of clear cell renal cell carcinoma using multiparametric MRI-derived radiomics and clinical risk factors.\"},{\"question\":\"How were patients and grades defined in the study?\",\"answer\":\"Patients with ccRCC were divided into a low-grade group (Grade I–II) and a high-grade group (Grade III–IV) based on the 2016 WHO/ISUP grading standards, then split into training and testing sets at a 7:3 ratio.\"},{\"question\":\"Which MRI sequences and feature selection strategy were used?\",\"answer\":\"Radiomics features were extracted from FS-T2WI, DWI, and CE-T1WI sequences. Optimal features were selected using the Mann-Whitney U test, Spearman correlation analysis, and LASSO.\"}]","Machine learning-based multiparametric MRI radiomics nomogram for predicting WHO/ISUP nuclear grading of clear cell renal cell carcinoma | PDF",1785942884,25,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"machine-learning-based-multiparametric-mri-radiomics-nomogram-for-predicting-whoisup-nuclear-grading-of-clear-cell-renal-cell-carcinoma","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/machine-learning-based-multiparametric-mri-radiomics-nomogram-for-predicting-whoisup-nuclear-grading-of-clear-cell-renal-cell-carcinoma/127910/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the radiomics nomogram address?","Question",{"text":76,"@type":77},"It predicts preoperative WHO/ISUP nuclear grading of clear cell renal cell carcinoma using multiparametric MRI-derived radiomics and clinical risk factors.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were patients and grades defined in the study?",{"text":81,"@type":77},"Patients with ccRCC were divided into a low-grade group (Grade I–II) and a high-grade group (Grade III–IV) based on the 2016 WHO/ISUP grading standards, then split into training and testing sets at a 7:3 ratio.",{"name":83,"@type":74,"acceptedAnswer":84},"Which MRI sequences and feature selection strategy were used?",{"text":85,"@type":77},"Radiomics features were extracted from FS-T2WI, DWI, and CE-T1WI sequences. 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