Deep learning model predicts early recurrence in gastric cancer

A deep learning model (DLRMLP) integrating clinical factors outperformed traditional methods in predicting early recurrence of locally advanced gastric cancer (LAGC) post-gastrectomy. In a study involving 620 patients, DLRMLP achieved an AUC of 0.891 compared to 0.797 with conventional models. This model effectively stratified early recurrence-free survival, disease-free survival, and overall survival (all p < 0.001), linking high scores to tumor proliferation pathways and immune cell infiltration, thus optimizing treatment strategies.