Deep Learning Model Using Contrast-Enhanced CT to Predict Disease-Free Survival in Localized Clear Cell Renal Cell Carcinoma

What this Trial Is About

Researchers are evaluating a deep learning model based on contrast-enhanced CT images to predict disease-free survival DFS before surgery in patients with localized clear cell renal cell carcinoma ccRCC. This study aims to validate the models predictive ability using data from multiple centers and compare its performance with traditional prediction methods. The study focuses on patients who have undergone nephrectomy and have confirmed ccRCC diagnosis. The study involves reviewing preoperative CT images and clinical data of patients who had partial or radical nephrectomies for ccRCC. Participants are categorized into groups based on recurrence status, with the deep learning model assessing risk of disease recurrence. The models ability to predict DFS is compared to that of existing conventional models using retrospective data collected between June 2013 and March 2020. Participants contribute by providing their preoperative CT images and clinical information for analysis. Researchers measure disease-free survival as the primary outcome to assess model accuracy. The study does not involve treatment interventions or active follow-up but relies on existing data. The total study period extends until August 2025, allowing comprehensive validation of the model across multiple centers.

Contrast-enhanced CT-based Deep Learning Model for Preoperative Prediction of Disease-free Survival (DFS) in Localized Clear Cell Renal Cell Carcinoma (ccRCC)