Machine Learning–Based Survival Prediction Models for Young Patients With Gastric Cancer: Model Development and Validation Study

Background Despite a global decline in the incidence of gastric cancer (GC), the number of cases diagnosed among younger individuals continues to increase. Several studies have been conducted to develop predictive models of mortality in patients with GC. Objective We developed 3- and 5-year survival prediction models for young patients with GC based on machine learning–based survival modeling approaches. Methods Data from 813 young patients (≤50 years) diagnosed with GC between 2013 and 2015 were retrieved from the Gastric Cancer Public Staging Database. Among these 813 patients, data from 569 (70%) and 244 (30%) were allocated to the model training and testing datasets, respectively. Random survival forest, gradient boosting survival analysis, extra survival tree, and the Cox proportional hazards model were applied to predict survival outcomes at the 3- and 5-year time horizons. Model performance was assessed and quantified using the concordance index (C-index) metric. For the machine learning prediction models, the mean C-index values and corresponding 95% CIs were estimated across 100 repeated training iterations. Results For the random survival forest model, the C-index for predicting 3-year mortality was 95.89% (95% CI 95.80%‐95.97%), whereas the C-index for predicting 5-year mortality was 91.82% (95% CI 91.68%‐91.96%). In the gradient boosting survival analysis model, the C-index for predicting 3-year mortality was 95.32% (95% CI 95.31%‐95.33%), and the C-index for predicting 5-year mortality was 89.98% (95% CI 89.95%‐90.01%). For the extra survival tree model, the C-index for predicting 3-year mortality was 95.53% (95% CI 95.46%‐95.60%), whereas the C-index for predicting 5-year mortality was 94.60% (95% CI 94.50%‐94.70%). In addition, the Cox proportional hazards model showed a C-index of 94.15% for predicting 3-year mortality and 82.26% for predicting 5-year mortality. Tumor stage and tumor size were the primary predictive variables used to train the models for mortality prediction at different time points. Other variables exhibited varying levels of predictive contribution across different time points. Conclusions These findings may facilitate the identification of high-risk young patients with GC who may benefit from more aggressive treatment strategies by enabling the prediction of mortality risk at different time points.