Patient characteristics

This study tracked the 5-year survival status of 2158 gastric cancer patients, of which 66.5% (1435 patients) had intestinal-type gastric cancer and 33.5% (723 patients) had diffuse-type gastric cancer. Among these patients, males accounted for 72% (1553 patients), and females accounted for 28% (605 patients). The largest proportion were Stage III gastric cancer patients representing 46.6% (1005 patients). The main primary site of gastric cancer was the gastric antrum (1413 patients, 65.5%), followed by the gastric body (437 patients, 20.3%). At the end of the study period, a total of 796 patients (36.8%) had died. Compared with intestinal-type gastric cancer, diffuse-type gastric cancer was associated with later pTNM staging, PNI, and positive lymphovascular invasion (LVI), among other clinicopathological features (Table 1). Kaplan-Meier curve analysis revealed that the survival of diffuse-type gastric cancer patients was significantly worse than that of intestinal-type gastric cancer patients (Figure S1).

Table 1 Patients’ demographics and clinical characteristicsData preprocessing and feature selection

The k-nearest neighbor imputer method was applied to impute missing values for variables with a missing rate of less than 30%, and one-hot encoding was used to handle nonordinal multicategory variables. Through 10-fold cross-validation via the RFE‒RF feature selection method, 5 features were selected for intestinal-type gastric cancer, and 11 features were selected for diffuse-type gastric cancer. Using Lasso, 11 features were selected for intestinal-type gastric cancer, and 9 features were selected for diffuse-type gastric cancer. By taking the intersection of features selected by RFE-RF and Lasso, 5 key features were ultimately determined for intestinal-type gastric cancer: pTNM, CA125, tumor size, CA199, and serum prealbumin (PALB); additionally, 8 key features were determined for diffuse-type gastric cancer: pTNM, Borrmann IV, lymphocytes (LYM), lactate dehydrogenase (LDH), potassium (K), PNI, tumor size, and location-whole (Fig. 1).

Fig. 1

Selection of key clinical pathological features for prognostic models of intestinal-type and diffuse-type gastric cancer

Development and validation of machine learning modelsPrognostic model for intestinal-type gastric cancer

Patients were randomly allocated to training (1004 cases) or testing (431 cases) sets at a 7:3 ratio. The feature distribution between the training and testing sets was random and uniform (Table S1).

ML models, including logistic regression (LR), support vector machine (SVM), random forest (RF), gradient boosting decision tree (GBDT), decision tree (DT), k nearest neighbors (KNN), and XGBoost (XGB), were constructed from the 5 selected variables in the training set and validated in the testing set. After a comprehensive comparison of multiple model evaluation metrics in the training and testing sets, GBDT exhibited the best performance. The training set presented an AUC of 0.862, a sensitivity of 0.852, a specificity of 0.805, an accuracy of 0.801, a precision of 0.816, a recall of 0.714, and an F1 score of 0.735. In the testing set, the corresponding metrics were 0.822, 0.804, 0.724, 0.749, 0.721, 0.662, and 0.674, respectively (Fig. 2A and C).

Fig. 2

Comparison of the predictive performance of different machine learning models for the prognosis of intestinal-type and diffuse-type gastric cancer. (A) Intestinal-type gastric cancer - training set; (B) Diffuse-type gastric cancer - training set; (C) Intestinal-type gastric cancer - test set; (D) Diffuse-type gastric cancer - test set

Prognostic model for diffuse-type gastric cancer

Patients were randomly allocated to training (506 patients) or testing (217 patients) sets at a 7:3 ratio. The feature distribution between the training and testing sets was random and uniform (Table S2).

Machine learning models, including the LR, SVM, RF, GBDT, DT, KNN, and XGB models, were constructed from the selected 8 variables in the training set and validated in the testing set. After a comprehensive comparison of multiple model evaluation metrics in the training and testing sets, the GBDT model exhibited the best performance. In the training set, it demonstrated an AUC of 0.902, a sensitivity of 0.812, a specificity of 0.820, an accuracy of 0.802, a precision of 0.802, a recall of 0.804, and an F1 score of 0.802. In the testing set, the corresponding metrics were 0.878, 0.851, 0.776, 0.811, 0.810, 0.810, and 0.810, respectively (Fig. 2B and D).

Visualization of feature importance and interpretation for personalized prediction

SHAP values were used to rank the importance of variables by their means, revealing the features most relevant to patient survival risk. By optimizing the model, risk factors affecting prognosis were ranked by importance, where higher feature values (in red) indicated an increased risk of patient death. Case examples were then used to illustrate the interpretability of the model. The arrows indicate the direction of influence of each variable on the prediction outcome, with red and blue arrows representing increased and decreased risk of death, respectively.

Interpretation of the prognostic model for intestinal-type gastric cancer

pTNM stage was the feature most relevant to survival risk in patients with intestinal-type gastric cancer. A later pTNM stage, higher CA125 level, larger tumor size, higher CA199, and lower PALB indicate poorer outcomes. Two patient cases were presented to illustrate the interpretability of the model: one with stage I and higher PALB suggesting long-term survival and another with stage IV and higher CA199 resulting in death within 5 years. SHAP values and prediction scores, reflecting lower SHAP values (-2.24) and prediction scores (0.096103) for surviving patients and higher SHAP values (0.99) and prediction scores (0.729057) for deceased patients, were calculated by integrating the effects of all variables (Fig. 3).

Fig. 3

Global and local explanations of the prognostic model for intestinal-type gastric cancer

Interpretation of the prognostic model for diffuse-type gastric cancer

pTNM stage and whether Borrmann type IV disease is present were the two features most relevant to survival risk in patients with diffuse-type gastric cancer. Later, pTNM stage, Borrmann type IV disease, lower LYM, higher LDH, lower K, positive PNI, larger tumor size, and whole stomach involvement were associated with poorer outcomes. Two patient cases are presented to illustrate the interpretability of the model: one with stage I and higher LYM, suggesting long-term survival, and another with stage IV and lower LYM, resulting in death within 5 years. SHAP values and prediction scores, reflecting lower SHAP values (-2.1) and prediction scores (0.109062) for surviving patients and higher SHAP values (1.68) and prediction scores (0.843024) for deceased patients, were calculated by integrating the effects of all the variables (Fig. 4).

Fig. 4

Global and local explanations of the prognostic model for diffuse-type gastric cancer

Risk stratification

Using ROC curves, optimal cutoff values for the training and testing sets of intestinal-type and diffuse-type gastric cancer patients were determined separately, and patients were divided into high-risk and low-risk groups. Survival analysis revealed that the prognosis of high-risk patients was significantly worse than that of low-risk patients (P < 0.001) (Fig. 5).

Fig. 5

Kaplan-Meier curves for high and low ML risk subjects in patients with intestinal-type and diffuse-type gastric cancer