A predictive model for WHO/ISUP pathologic grading of renal clear cell carcinoma based on CT radiomics: a multicenter study

作者：Chunying Wu, Yuzhen Xi, Juanjuan Hu, Guangjin Li, Xu Wang, Xiaofei Jiao, Zhongxiang Ding, Weiying Sun · 发表于：BMC Nephrology · 年份：2025 · DOI：10.1186/s12882-025-04268-z · 被引用次数：2 · 研究领域：Renal cell carcinoma treatment、Radiomics and Machine Learning in Medical Imaging、Advanced X-ray and CT Imaging

OBJECTIVE: This study aims to evaluate the predictive value of CT radiomics combined with clinical-imaging features for the WHO/ISUP pathological grade of clear cell renal cell carcinoma(ccRCC). METHODS: In this multicenter retrospective study enrolled 169 patients (110 males, 59 females) with pathological confirmed ccRCC between November 2017 and February 2022. Based on the WHO/ISUP pathological grading criteria, patients were stratified into two groups: low-grade (grades I-II, n = 93) and high-grade (grades III-IV, n = 76). Three-dimensional tumor segmentation was performed on CT cortical-phase images using ITK-SNAP software. The segmented data were subsequently processed through the United Imaging Intelligent Scientific Research Platform for radiomic feature extraction and selection. Logistic regression analyses were conducted to identify independent predictive factors. Based on these factors, an optimized predictive model was developed through random forest classification and evaluated using calibration curves, ROC analysis, Delong test and decision curve analysis. RESULTS: Significant differences were observed in tumor size, morphology, hemorrhage, necrosis, tumor thrombus, capsular invasion and tumor extending beyond the renal margin between the two groups. Logistic regression analysis identified tumor size, hemorrhage and tumor thrombus as independent predictors. Six radiomic features were selected to establish prediction model. In the training cohort, the combined mod...