A Pathomics-Based Model for Predicting Disease-Free Survival in Gastric Cancer Patients After Curative Gastrectomy

Objectives This study aims to develop and validate a prognostic risk model by integrating pathomics features with clinical variables to predict disease-free survival (DFS) in patients with gastric cancer (GC). Methods GC patients who were pathologically diagnosed and subsequently treated with curative gastrectomy and D2 lymphadenectomy at the Fifth Affiliated Hospital of Wenzhou Medical University between January 2017 and April 2023 were retrospectively enrolled and assigned to a training cohort (n = 275) and an independent validation cohort (n = 118). Pathomics features were extracted from pathological images and LASSO-Cox regression was used to identify pathomics features significantly associated with DFS. The selected pathomics features were integrated with clinical factors to create a prognostic model. Predictive accuracy was evaluated using time-dependent ROC analysis, and the model's performance was compared with the clinic-only and pathomics-only models. A nomogram was constructed to provide individualized DFS predictions. Results 16 pathomics features were selected, and the cut-off for the pathomics scores was set at 0.27. High-risk patients exhibited significantly worse DFS compared to low-risk patients in both the training cohort (HR = 4.57, 95% CI: 3.118–6.697, p < 0.0001) and the validation cohort (HR = 2.264, 95% CI: 1.255–4.083, p < 0.0001). The clinic-pathomics model demonstrated strong predictive performance in both cohorts, with AUCs for 1-, 3-, and 5-year survival of 0.832, 0.821, and 0.851 in the training cohort, and 0.671, 0.702, and 0.682 in the validation cohort. The nomogram, incorporating pathomics score, T stage, differentiation degree, and ECOG performance status, showed high calibration accuracy, as confirmed by calibration plots, and outperformed both the clinic-only and pathomics-only models in decision curve analysis. Conclusion A clinic-pathomics model integrating pathomics features with clinical data provides a reliable tool for DFS prediction in GC patients, which facilitates individualized DFS predictions and personalized treatment strategies.