Deep learning model predicts early recurrence in gastric cancer

A new deep learning model utilizing multiphase CT images effectively predicts early recurrence in patients with locally advanced gastric cancer (LAGC). The model, integrated with clinical factors, demonstrated superior performance (AUC: 0.891) compared to previous models. Significant associations were found between higher prediction scores and tumor proliferation pathways, such as WNT and MYC signaling, as well as immune cell infiltration. This tool aids in optimizing treatment strategies and monitoring patient outcomes following surgery.