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

Abstract

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.

Predicting completion of clinical trials in pregnant women: Cox proportional hazard and neural network models

“…25 Random survival forest has also been used for time-to-event analysis, but we did not include it in this study because previous research did not demonstrate better results than DeepSurv or the Cox proportional hazard model. 26,27 Contrary to expectations (very high HDI). Abortion: 0 (no), and 1 (yes).…”

Prediction and Risk Stratification of Cardiovascular Disease in Diabetic Kidney Disease Patients

“…The superior performance of the DeepSurv model demonstrates its ability to handle the complex association of risk factors. In addition, the DeepSurv model has been widely applied to many survival analyses with a favorable prediction value (22,23,29). It also can provide a framework on which more datasets can be trained in the future in a broader population.…”

“…Katzman et al also developed a novel deep feed-forward neural network based on Cox assumption called DeepSurv, which combined survival analysis with deep learning and had the advantage to perform a prediction of time-to-event data. It has been successfully applied in the survival analysis of multiple diseases and showed promising performance in predicting patients' outcomes, such as oncological diseases, Covid-19, and atherosclerotic cardiovascular disease (22)(23)(24)(25)(26). Several online calculation tools were constructed based on the DeepSurv method (27)(28)(29).…”

An energy-efficient in-memory computing architecture for survival data analysis based on resistive switching memories

“…The parameters β in the model are fine-tuned to properly model the hazard rate function. However, in many medical scenarios (Bice et al, 2020 ; Byun et al, 2021 ; Hadanny et al, 2022 ), the assumption of a linear log-risk function (i.e., h ( x )) may be too simplistic. To this extent, Katzman et al ( 2018 ) developed the DeepSurv feed-forward neural network whose non-linear output ĥ θ ( x ) replaces the linear combination of features ĥ β ( x ).…”