Deep Learning Model Based on Tumor and Visceral Adipose Tissue CT Features for Predicting Peritoneal Metastasis Risk after Radical Gastrectomy in Serosa-Invasive Gastric Cancer.

Purpose To develop and validate a deep learning model integrating tumor and visceral adipose tissue (VAT) CT scan features with clinical indicators to predict postoperative peritoneal metastasis in serosa-invasive gastric cancer. Materials and Methods This multicenter, retrospective study between April 2008 and January 2018 included patients with pathologically confirmed serosa-invasive gastric cancer. Patients were divided into training, internal test, and independent external test sets. Tumor and VAT regions were segmented at preoperative CT. Deep features were extracted using a ResNet18 network. A fused tumor-VAT deep learning signature (F-DLS) was generated, incorporating clinical variables into a multimodal deep learning radiomics model (MDLR) using a sparse Bayesian extreme learning machine. Model performance was assessed using receiver operating characteristic curve, integrated discrimination improvement, calibration, decision curve analysis, and recurrence-free survival. Results Among 416 patients (mean age, 56.6 years ± 11.6; 66.1% male patients), the F-DLS achieved area under the receiver operating characteristic curve (AUC) values of 0.81 (95% CI: 0.73, 0.88) in the internal test set and 0.79 (95% CI: 0.71, 0.86) in the external test set. Compared with the tumor tissue DLS and VAT-DLS, the F-DLS showed numerically higher AUCs without statistical significance. The MDLR achieved the strongest predictive performance, with AUCs of 0.86 (95% CI: 0.79, 0.92) in the internal test set and 0.86 (95% CI: 0.78, 0.92) in the external test set. The MDLR statistically significantly outperformed clinical and deep learning-only models (integrated discrimination improvement,