Deep learning based prediction of prognosis in nonmetastatic clear cell renal cell carcinoma.

Survival analyses for malignancies, including renal cell carcinoma (RCC), have primarily been conducted using the Cox proportional hazards (CPH) model. We compared the random survival forest (RSF) and DeepSurv models with the CPH model to predict recurrence-free survival (RFS) and cancer-specific survival (CSS) in non-metastatic clear cell RCC (nm-cRCC) patients. Our cohort included 2139 nm-cRCC patients who underwent curative-intent surgery at six Korean institutions between 2000 and 2014. The data of two largest hospitals' patients were assigned into the training and validation dataset, and the data of the remaining hospitals were assigned into the external validation dataset. The performance of the RSF and DeepSurv models was compared with that of CPH using Harrel's C-index. During the follow-up, recurrence and cancer-specific deaths were recorded in 190 (12.7%) and 108 (7.0%) patients, respectively, in the training-dataset. Harrel's C-indices for RFS in the test-dataset were 0.794, 0.789, and 0.802 for CPH, RSF, and DeepSurv, respectively. Harrel's C-indices for CSS in the test-dataset were 0.831, 0.790, and 0.834 for CPH, RSF, and DeepSurv, respectively. In predicting RFS and CSS in nm-cRCC patients, the performance of DeepSurv was superior to that of CPH and RSF. In no distant time, deep learning-based survival predictions may be useful in RCC patients.

Scientific reports. 2021 Jan 13*** epublish ***

Seok-Soo Byun, Tak Sung Heo, Jeong Myeong Choi, Yeong Seok Jeong, Yu Seop Kim, Won Ki Lee, Chulho Kim

Department of Urology, Seoul National University Bundang Hospital, Seongnam, 13620, Korea., Department of Convergence Software, Hallym University, Chuncheon, 24252, Korea., College of Software, Hallym University, Chuncheon, 24252, Korea., Department of Urology, College of Medicine, Hallym University, Chuncheon Sacred Heart Hospital, 153, Kyo-dong, Chuncheon, 24253, Korea. ., Department of Neurology, College of Medicine, Hallym University, Chuncheon Sacred Heart Hospital, 153, Kyo-dong, Chuncheon, 24253, Korea. .