Prediction of WHO/ISUP Grades in Papillary Renal Cell Carcinoma Using MRI Radiomics
Zhao Houming
Jia Tongyu
Yu Miao
Bai Xu
Wang Jichen
Xu Qingjiang
Li Shangwei
Song Jialong
Yang Guoqiang
Ding Xiaohui
Huang Qingbo
Ma Xin
Abstract：Objective:To investigate the clinical utility of a machine learning(ML)-based magnetic resonance imaging(MRI)radiomics approach for the preoperative prediction of World Health Organization/International Society of Urological Pathology(WHO/ISUP)grades in papillary renal cell carcinoma(pRCC).Methods:A retrospective collection of 153 pRCC patients,confirmed by postoperative pathology after surgical treatment at the First Medical Center of the PLA General Hospital from January 2010 to December 2023,was performed.According to the WHO/ISUP pathological grading standard,patients were classified into low-grade group(grades I and II)and high-grade group(grades III and IV).Univariate and multivariate analyses were conducted to determine independent clinical pre-dictive factors.MRI images were analyzed for radiomic features,which were selected using methods such as mini-mum redundancy maximum relevance(mRMR)and least absolute shrinkage and selection operator(LASSO)regres-sion.Clinical,radiomic,and clinical-radiomic combined models were constructed using Logistic regression and a sup-port vector machine(SVM).The predictive performance of the three models was evaluated using receiver operating characteristic(ROC)curves and the area under the curve(AUC).DeLong's test was used to compare the AUC val-ues of the models,and calibration curves were employed to assess model calibration.Decision curve analysis(DCA)was used to evaluate clinical utility.Results:A total of 3,776 radiomic features were extracted from four MRI se-quences,and after selection,13 features were used to build the models.Multivariate analysis identified the maximum tumor diameter and the systemic inflammation response index(SIRI)as independent predictors of WHO/ISUP grad-ing.In the training set,the radiomic model(AUC=0.837)outperformed the clinical model(AUC=0.776),and the clinical-radiomic combined model demonstrated the best predictive performance(AUC=0.889),significantly better than the clinical model(P=0.017)according to DeLong's test.In the validation set,the combined model(AUC=0.853)significantly outperformed both the clinical model(AUC=0.725,P=0.019)and the radiomic model(AUC=0.826,P=0.106).Calibration curves showed that the combined model's prediction of WHO/ISUP grading closely matched the actual results in both the training and validation sets.DCA demonstrated that the combined model had a higher net benefit than the radiomic and clinical models.Conclusion:The machine learning-based MRI radiomics model using multiple MRI sequences is an effective non-invasive diagnostic tool that shows good predictive perfor-mance for preoperative assessment of pRCC WHO/ISUP grading.
Keywords：papillary renal cell carcinomaradiomicsWorld Health Organization/International Society of Urologi-cal Pathology gradingpredictive model
Publication Date：2025-02-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 6-13 )