Prognostic models can be valuable for clinicians in counseling and monitoring patients after the surgical resection of nonmetastatic renal cell carcinoma (nmRCC). Over the years, several risk prediction models have been developed, evolving significantly in their ability to predict recurrence and overall survival following surgery. This review comprehensively evaluates and critically appraises current prognostic models for nm-RCC after nephrectomy. The last 2 decades have witnessed a notable increase in the development of various prognostic risk models for RCC, incorporating clinical, pathological, genomic, and molecular factors, primarily using retrospective data. Only a limited number of these models have been developed using prospective data, and their performance has been less effective than expected when applied to broader, real-life patient populations. Recently, artificial intelligence (AI), especially machine learning and deep learning algorithms, has emerged as a significant tool in creating survival prediction models. However, their widespread application remains constrained due to limited external validation, a lack of cost-effectiveness analysis, and unconfirmed clinical utility. Although numerous models that integrate clinical, pathological, and molecular data have been proposed for nm-RCC risk stratification, none have conclusively demonstrated practical effectiveness. As a result, current guidelines do not endorse a specific model. The ongoing development and validation of AI algorithms in RCC risk prediction are crucial areas for future research.
Urologic oncology. 2024 Sep 19 [Epub ahead of print]
Zine-Eddine Khene, Raj Bhanvadia, Isamu Tachibana, Karim Bensalah, Yair Lotan, Vitaly Margulis
Department of Urology, UT Southwestern Medical Center, Dallas, TX. Electronic address: ., Department of Urology, UT Southwestern Medical Center, Dallas, TX., Department of Urology, Rennes University Hospital, Rennes, France.