Study on risk stratification and treatment strategy of blood indicators in patients with moderate risk of GIST

Intermediate-risk gastrointestinal stromal tumor (GIST) patients exhibit marked prognostic heterogeneity. The traditional NIH risk classification often results in undertreatment of latent high-risk patients and overtreatment of truly low-risk ones. This study aimed to develop an interpretable machine learning model integrating hematologic inflammatory markers to achieve precise risk re-stratification and optimize adjuvant therapy strategies for intermediate-risk patients.

Primary GIST patients were retrospectively enrolled. LASSO regression was applied to select key features from eight inflammatory markers (including NLR, PLR, and SII). A random survival forest model was then constructed, followed by 5-fold cross-validation. SHAP values were used to interpret feature contributions, and Kaplan-Meier survival analysis was conducted to evaluate stratification performance.

LASSO regression identified seven inflammatory markers, among which PLR, SII, and PIV were the top three key variables. The optimal random survival forest model (five-feature model) achieved an AUC of 0.777, with an internally validated mean AUC of 0.782 (95% CI: 0.679-0.878) and an out-of-bag (OOB) error of 0.124. SHAP analysis revealed that PLR, NLR, and PAR were the major contributors to model prediction. The model effectively stratified intermediate-risk patients into "intermediate-high-risk" and "intermediate-low-risk" subgroups with significantly different survival outcomes (p<0.0001).

This study represents the first construction of an interpretable predictive model integrating blood-based inflammatory markers with machine learning algorithms. The model accurately identifies occult high-risk individuals among patients with intermediate-risk GIST, thereby providing exploratory evidence and a foundation for hypothesis generation for future individualized management strategies.