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Radiomics and Image-based Artificial Intelligence for Predicting Recurrence and Survival After Surgery in Localized
Georges Mjaess1, Romain Diamand1, Nayoth Dikete1
1Department of Urology, Hôpital Universitaire de Bruxelles, Brussels, Belgium.
Background And Objective:
Radiomics and artificial intelligence (AI)-based imaging models offer a noninvasive approach to preoperative risk stratification in localized renal cell carcinoma (RCC), where existing prognostic tools remain limited. We conducted a systematic review and meta-analysis to evaluate their predictive performance and methodological quality for recurrence and survival outcomes.
Methods:
A systematic review was conducted in PubMed and Scopus from inception through April 2025. Radiomics and AI models were assessed for prognostic accuracy regarding 5-yr fixed-time recurrence-free survival (RFS) and overall survival after surgery for localized RCC. The extracted data included model type, radiomic features, validation methods, and area under the curve (AUC). Methodological quality was assessed using the APPRAISE-AI framework. Pooled 5-yr AUCs were synthesized using a prespecified random-effect model; heterogeneity was quantified (Q and τ2) and explored using a prespecified analysis restricted to external validation-only cohorts and sensitivity analyses.
Key Findings And Limitations:
Thirty studies (n = 17 639) were included, predominantly retrospective and computed tomography (CT) based. The most predictive and frequently retained radiomic features were from the gray-level co-occurrence matrix and shape families. A meta-analysis of 20 radiomic model cohorts showed a pooled AUC of 0.87 (95% confidence interval [CI]: 0.84-0.90) for 5-yr RFS (Q = 271.08; p < 0.001; τ2 = 0.0037). External validation cohorts showed a pooled AUC of 0.86 (95% CI: 0.83-0.88; Q = 12.81; p = 0.172; τ2 = 0.0004). APPRAISE-AI revealed overall moderate methodological quality (median score: 54/100), with limited adherence to TRIPOD-AI and underuse of explainability tools.
Conclusions And Clinical Implications:
Radiomic models for localized RCC built on standardized CT protocols and robust segmentation, and incorporating shape and texture features combined with clinical variables demonstrated high prognostic accuracy. Our meta-analysis confirms that such models predict recurrence and survival outcomes accurately.
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