ArticleEuropean urology open science2025
Radiomic Profiling of Tumor Thrombus for Predicting Recurrence in Renal Cell Carcinoma.
Article in European urology open science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background and objective: Clear cell renal cell carcinoma (ccRCC) with tumor thrombus (TT) presents a significant prognostic challenge due to its high recurrence risk. Radiomics, an imaging-based biomarker approach, has primarily focused on the primary tumor, while the prognostic potential of TT radiomics remains largely unexplored. This study aimed to assess the added value of tumor thrombus radiomic signatures (RSs) in predicting recurrence in ccRCC patients with TT. Methods: We conducted a retrospective analysis of patients undergoing surgical resection for nonmetastatic ccRCC with TT. Preoperative contrast-enhanced computed tomography images were used to extract radiomic features from the primary tumor and TT. Features were selected using least absolute shrinkage and selection operator (LASSO) Cox regression and incorporated into predictive models. Performance was assessed using the integrated area under the curve (iAUC), calibration, decision curve analysis (DCA), and incremental value over clinical models (pTNM, UISS, and Leibovich) for the prediction of disease-free survival (DFS). Key findings and limitations: A total of 166 patients (training set: Conclusions and clinical implications: Tumor thrombus radiomic profiling improves DFS prediction significantly and adds complementary prognostic value to established models in patients with ccRCC. Incorporation of these features into clinical workflows may enhance risk stratification and guide personalized treatment planning. Prospective validation in large, multicenter cohorts is warranted to support clinical adoption. Patient summary: This study focused on kidney cancer with tumor thrombus and demonstrated that an analysis of the imaging features from the thrombus improved the prediction of cancer recurrence significantly. This approach could enhance the understanding of individual patient risks and support more personalized treatment strategies.
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