Radiomics and Image-based Artificial Intelligence for Predicting Recurrence and Survival After Surgery in Localized Renal Cell Carcinoma: An APPRAISE-AI Systematic Review and Meta-analysis.
Authors
Affiliations (11)
Affiliations (11)
- Department of Urology, Hôpital Universitaire de Bruxelles, Brussels, Belgium. Electronic address: [email protected].
- Department of Urology, Hôpital Universitaire de Bruxelles, Brussels, Belgium.
- Division of Urology, Department of Surgery, University of Toronto, Toronto, Ontario, Canada.
- Department of Radiology, Hôpital Universitaire de Bruxelles, Brussels, Belgium.
- Department of Urology, University Hospital, Bordeaux, France; I.Care (Integrated research & innovation program for kidney cancer) - Inserm, UMR1312, BRIC, Bordeaux Institute of Oncology, Bordeaux University, Bordeaux, France.
- Department of Radiology, Cliniques Universitaires Saint-Luc, Brussels, Belgium.
- Unit of Urological Robotic Surgery and Renal Transplantation, Careggi Hospital, University of Florence, Florence, Italy.
- Division of Urology, Department of Oncology, School of Medicine, San Luigi Hospital, University of Turin, Orbassano, Italy.
- Department of Urology, Hôtel-Dieu de France, Beirut, Lebanon.
- Urology Unit, Department of Surgical Sciences, Policlinico Tor Vergata, Tor Vergata University, Rome, Italy.
- Division of Urology, Institut de Recherche Clinique, Cliniques Universitaires Saint Luc, Université Catholique de Louvain, Brussels, Belgium.
Abstract
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. 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 τ<sup>2</sup>) and explored using a prespecified analysis restricted to external validation-only cohorts and sensitivity analyses. 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; τ<sup>2</sup> = 0.0037). External validation cohorts showed a pooled AUC of 0.86 (95% CI: 0.83-0.88; Q = 12.81; p = 0.172; τ<sup>2</sup> = 0.0004). APPRAISE-AI revealed overall moderate methodological quality (median score: 54/100), with limited adherence to TRIPOD-AI and underuse of explainability tools. 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.