Interpretable Machine Learning Radiomics Model Predicts 5-year Recurrence-Free Survival in Non-metastatic Clear Cell Renal Cell Carcinoma: A Multicenter and Retrospective Cohort Study.
Authors
Affiliations (8)
Affiliations (8)
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China (J.Z., W.H.).
- Department of Urology, Chongqing University Three Gorges Hospital, Chongqing 404000, China (Y.L.).
- Department of Urology, The Affiliated Yongchuan Hospital of Chongqing Medical University, Chongqing 402160, China (X.Z.).
- Department of Urology, Chongqing University Fuling Hospital, Chongqing 408000, China (Y.C.).
- Department of Urology, Urology Research Institute, The First Affiliated Hospital of Fujian Medical University, Fujian 350005, China (S.C.).
- Department of Urology, The People's Hospital of Dazu, Chongqing 402360, China (Q.M.).
- Department of Urology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China (Q.J.).
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China (Y.X.). Electronic address: [email protected].
Abstract
To develop and validate a computed tomography (CT) radiomics-based interpretable machine learning (ML) model for predicting 5-year recurrence-free survival (RFS) in non-metastatic clear cell renal cell carcinoma (ccRCC). 559 patients with non-metastatic ccRCCs were retrospectively enrolled from eight independent institutes between March 2013 and January 2019, and were assigned to the primary set (n=271), external test set 1 (n=216), and external test set 2 (n=72). 1316 Radiomics features were extracted via "Pyradiomics." The least absolute shrinkage and selection operator algorithm was used for feature selection and Rad-Score construction. Patients were stratified into low and high 5-year recurrence risk groups based on Rad-Score, followed by Kaplan-Meier analyses. Five ML models integrating Rad-Score and clinicopathological risk factors were compared. Models' performances were evaluated via the discrimination, calibration, and decision curve analysis. The most robust ML model was interpreted using the SHapley Additive exPlanation (SHAP) method. 13 radiomic features were filtered to produce the Rad-Score, which predicted 5-year RFS with area under the receiver operating characteristic curve (AUCs) of 0.734-0.836. Kaplan-Meier analysis showed significant survival differences based on Rad-Score (all Log-Rank p values <0.05). The random forest model outperformed other models, obtaining AUCs of 0.826 [95% confidential interval (CI): 0.766-0.879] and 0.799 (95% CI: 0.670-0.899) in the external test set 1 and 2, respectively. The SHAP analysis suggested positive associations between contributing factors and 5-year RFS status in non-metastatic ccRCC. CT radiomics-based interpretable ML model can effectively predict 5-year RFS in non-metastatic ccRCC patients, distinguishing between low and high 5-year recurrence risks.