Background: Renal cell carcinoma (RCC) is a global health challenge characterized by significant histological heterogeneity. Conventional contrast-enhanced CT often struggles to differentiate RCC from solid benign renal tumors like fat-poor angiomyolipoma (fpAML) and renal oncocytoma (RO), leading to potential surgical overtreatment. CT-based radiomics has emerged as a promising non-invasive approach that extracts high-dimensional quantitative imaging features to support lesion characterization and may contribute toward more comprehensive, biopsy-adjacent decision support, although it does not yet replace histopathological assessment. Methods: This review systematically evaluates the predictive performance of CT-based radiomics in characterizing solid renal tumors. A literature search was conducted in PubMed/MEDLINE, Cochrane, and Scopus databases for original research published between 2012 and 2025. The review focuses on four key areas: differentiating benign renal tumors from RCC, clear cell (ccRCC) from non-ccRCC, fpAML from RCC, and RO from RCC. Results: In total, 47 studies were assessed, including 11,999 patients. CT-based radiomics demonstrates high diagnostic performance across all categories. Median Area Under the Curve values were 0.830 (0.747–0.900) for benign vs. malignant differentiation, 0.900 (0.861–0.910) for ccRCC vs. non-ccRCC, 0.912 (0.879–0.933) for fpAML vs. RCC, and 0.885 (0.841–0.947) for RO vs. RCC. The integration of radiomic features with clinical parameters into combined nomograms consistently yielded the highest predictive accuracy. Conclusions: Radiomics provides a non-invasive, objective method to characterize renal tumors, potentially reducing unnecessary surgeries and enabling personalized treatment. However, widespread clinical adoption remains limited by a lack of protocol standardization, the need for automated segmentation, and the requirement for prospective, multicenter validation.
Koumpis et al. (Wed,) studied this question.