Background
Interpretability is critical for the clinical adoption of ML prognostic models, especially when models are used to communicate recurrence risk after neoadjuvant chemotherapy (NACT) in gastric cancer (GC).

Methods
Using the finalized GAM model developed from a four-center cohort (training n=725; external validation cohorts n=149 and n=276), we performed SHAP-based interpretation to quantify feature contributions and directionality. Time-dependent feature importance was ranked by Brier score loss, and SHAP summary plots were used to visualize how predictor values shift individual risk estimates over follow-up.

Results
Time-dependent SHAP identified lymph node ratio, ypT stage, and Bormann classification as the top three contributors to predictive performance, remaining highly influential throughout the follow-up period. Higher lymph node ratio and advanced ypT were consistently associated with higher predicted recurrence risk. For categorical variables, the presence of nerve invasion, lymphovascular tumor thrombus, and a signet-ring cell component shifted predictions toward worse DFS. In addition, advanced Bormann type (III/IV) and diffuse tumor involvement (tumor site characteristic) were associated with increased risk compared with less aggressive or more localized patterns. These explanations provide an intuitive mapping between postoperative pathology/aggressiveness features and individualized risk predictions, facilitating clinician trust and patient-facing communication.

Conclusions
Time-dependent SHAP yields clinically transparent explanations of an externally validated ML model and highlights lymph node ratio, ypT, and Bormann type as dominant drivers of DFS risk after first-line NACT for GC.