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Research Article: Interpretable machine learning for preoperative prediction of WHO/ISUP grade in clear cell renal cell carcinoma using contrast-enhanced CT–derived extracellular volume fraction

Date Published: 2026-10-13

Abstract:
To develop and validate an interpretable machine learning (ML) model that integrates extracellular volume fraction (ECV) derived from contrast-enhanced CT with clinical and imaging features for preoperative prediction of World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade in clear cell renal cell carcinoma (ccRCC). A retrospective cohort of 263 patients with pathologically confirmed ccRCC from two institutions was analyzed. Patients from Hospital A (n=184) were randomly divided into training (n=128) and internal validation (n=56) cohorts, while patients from Hospital B (n=79) served as an independent external validation cohort. CT-derived ECV, arterial enhancement fraction (AEF), and conventional imaging features were extracted. Six machine learning models (Logistic Regression [LR], eXtreme Gradient Boosting [XGBoost], Gaussian Naive Bayes [GNB], Support Vector Machine [SVM], K-Nearest Neighbors [KNN], Light Gradient Boosting Machine [LightGBM]) were constructed, and the best-performing model was identified based on bootstrap resampling. Model interpretability was assessed using SHapley Additive exPlanations (SHAP). Tumor size (low-grade: 4.99 ± 1.98 cm; high-grade: 6.20 ± 2.72 cm, P = 0.002), ECV (low-grade: 0.46 ± 0.09; high-grade: 0.57 ± 0.12, P<0.001), and AEF (low-grade: 1.37 ± 0.24; high-grade: 1.56 ± 0.25, P<0.001) were significantly higher in high-grade ccRCC, while hematocrit (HCT, low-grade: 0.41 ± 0.05; high-grade: 0.38 ± 0.06, P<0.001) was lower. The optimal LR model achieved robust performance: training cohort AUC = 0.995 (95% CI: 0.990–0.999), internal validation AUC = 0.903 (95% CI: 0.818–0.988), external test AUC = 0.857 (95% CI: 0.756–0.958). SHAP analysis showed ECV, tumor diameter, and AEF were the top 3 predictors. An interpretable ML model incorporating CT-derived ECV and imaging features effectively predicts preoperative WHO/ISUP grade of ccRCC, providing a noninvasive tool for individualized risk stratification and clinical decision-making.

Introduction:
Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal malignancy in adults, characterized by highly heterogeneous biological behavior and thus variable clinical outcomes ( 1 , 2 ). According to the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading system, tumor grade is a well-established prognostic factor that reflects tumor aggressiveness and patient outcomes ( 3 ). Accurate preoperative prediction of ccRCC grade is therefore of great clinical…

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