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.
Journal Article by Guo X, Chen M (…) Ji J et 10 al. in Int J Surg
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