Radiomics-Based Classification of Clear Cell Renal Cell Carcinoma ISUP Grade: A Machine Learning Approach with SHAP-Enhanced Explainability

<b>Background</b>: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cancer, and its prognosis is closely linked to the International Society of Urological Pathology (ISUP) grade. While histopathological evaluation remains the gold standard for grading, non-invasive methods, such as radiomics, offer potential for automated classification. This study aims to develop a radiomics-based machine learning model for the ISUP grade classification of ccRCC using nephrographic-phase CT images, with an emphasis on model interpretability through SHAP (SHapley Additive exPlanations) values. <b>Objective</b>: To develop and interpret a radiomics-based machine learning model for classifying ISUP grade in clear cell renal cell carcinoma (ccRCC) using nephrographic-phase CT images. <b>Materials and Methods</b>: This retrospective study included 109 patients with histopathologically confirmed ccRCC. Radiomic features were extracted from the nephrographic-phase CT scans. Feature robustness was evaluated via intraclass correlation coefficient (ICC), followed by redundancy reduction using Pearson correlation and minimum Redundancy Maximum Relevance (mRMR). Logistic regression, support vector machine, and random forest classifiers were trained using 8-fold cross-validation. SHAP values were computed to assess feature contribution. <b>Results</b>: The logistic regression model achieved the highest classification performance, with an accuracy of 82% and an AUC of 0.86. SHAP analysis identified major axis length, busyness, and large area emphasis as the most influential features. These variables represented shape and texture information, critical for distinguishing between high and low ISUP grades. <b>Conclusions</b>: A radiomics-based logistic regression model using nephrographic-phase CT enables accurate, non-invasive classification of ccRCC according to ISUP grade. The use of SHAP values enhances model transparency, supporting clinical interpretability and potential adoption in precision oncology.