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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.
Radiomics and artificial intelligence (AI) models accurately predict recurrence and survival in localized renal cell carcinoma (RCC). These AI-based imaging tools offer a noninvasive approach to preoperative risk stratification for better patient outcomes.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Oncology
Background:
- Prognostic tools for localized renal cell carcinoma (RCC) have limitations in preoperative risk stratification.
- Radiomics and AI-based imaging models present a noninvasive alternative for assessing patient risk.
Conclusions:
- Radiomic models, particularly those using standardized CT protocols, robust segmentation, and incorporating shape/texture features with clinical variables, demonstrate high prognostic accuracy for localized RCC.
- These AI-driven models accurately predict recurrence and survival outcomes, supporting their clinical utility.
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