Development and validation of esophageal cancer prediction model based on DeepSurv model: A cohort study

Objective Today, several new machine learning models are being applied to perform survival analyses; however, due to the variations of the size and characteristics of samples, predictive performance of many models is not ideal. In this work, relevant data were collected from the surveillance, epidemiology, and end results (SEER) database of esophageal cancer patients and a multivariate survival analysis model was established based on DeepSurv model to predict the survival rate of esophageal cancer patients and provide reasonable recommendations for individual treatment of these patients. The application of novel machine learning models in survival analysis has been gaining momentum; however, due to the heterogeneity in sample size and characteristics, the predictive accuracy of many models remains suboptimal. The aim was to forecast the survival rates of esophageal cancer patients and to offer informed recommendations for personalized treatment strategies. Methods In this retrospective cohort study, the data of 5276 patients were collected from the SEER database using SEER*Stat software(version 8.4.3). The collected data was divided into training and testing sets with 7:3 ratio and the predictive performance of the model was explored using the concordance index (CI). The efficiencies of different treatment methods were compared. In this study, we developed a survival analysis model based on the DeepSurv model. The data was stratified into training (70%, n = 3693) and testing (30%, n = 1583) sets with a 7:3 ratio. The model's predictive efficacy was assessed using the CI, and the effectiveness of various treatment modalities was comparatively analyzed. Results The selected 5276 patients with esophageal cancer were divided into a training set (70%, 3693) and a testing set (30%, 1583). Cox univariate regression analysis results showed that race, diagnosis year, and marital status were risk factors affecting prognosis ( p < 0.05). The consistency indexes of training and test sets were 0.84 and 0.83, respectively. These tests indicated that the model had good performance. There is a significant difference in the therapeutic efficacy between the surgical group and the non-surgical group ( p < 0.001). Conclusions According to DeepSurv model, a survival prediction model was developed for patients with esophageal cancer and reasonable suggestions were provided for individual treatment selection. Utilizing the DeepSurv model, we have developed a predictive survival model tailored for esophageal cancer patients, providing actionable insights for the selection of individualized treatment approaches.