Deep Neural Networks for Predicting Recurrence and Survival in Patients with Esophageal Cancer After Surgery

TL;DR

This study compares traditional Cox models with deep neural networks like DeepSurv and DeepHit for predicting recurrence and survival in esophageal cancer patients, highlighting the potential of DNNs for personalized prognosis.

Contribution

The paper evaluates the performance of DNN-based models against CoxPH in predicting survival outcomes, demonstrating comparable or slightly improved accuracy using a large multicenter dataset.

Findings

DeepSurv slightly outperformed CoxPH in C-index for DFS and OS.

Prognostic factors identified by models aligned with clinical literature.

DNN models showed potential for personalized risk stratification.

Abstract

Esophageal cancer is a major cause of cancer-related mortality internationally, with high recurrence rates and poor survival even among patients treated with curative-intent surgery. Investigating relevant prognostic factors and predicting prognosis can enhance post-operative clinical decision-making and potentially improve patients' outcomes. In this work, we assessed prognostic factor identification and discriminative performances of three models for Disease-Free Survival (DFS) and Overall Survival (OS) using a large multicenter international dataset from ENSURE study. We first employed Cox Proportional Hazards (CoxPH) model to assess the impact of each feature on outcomes. Subsequently, we utilised CoxPH and two deep neural network (DNN)-based models, DeepSurv and DeepHit, to predict DFS and OS. The significant prognostic factors identified by our models were consistent with clinical literature, with post-operative pathologic features showing higher significance than clinical stage features. DeepSurv and DeepHit demonstrated comparable discriminative accuracy to CoxPH, with DeepSurv slightly outperforming in both DFS and OS prediction tasks, achieving C-index of 0.735 and 0.74, respectively. While these results suggested the potential of DNNs as prognostic tools for improving predictive accuracy and providing personalised guidance with respect to risk stratification, CoxPH still remains an adequately good prediction model, with the data used in this study.