Imaging-based stratification of Claudin 18.2-positive gastric adenocarcinoma using ¹⁸F-FDG PET/CT radiomics

This study aimed to develop and validate a non-invasive, multimodal radiomics model based on preoperative ¹⁸F-FDG PET/CT to predict CLDN18.2 expression in gastric adenocarcinoma (GAC), addressing the limitations of intratumoral heterogeneity and invasiveness associated with endoscopic biopsies.

This retrospective study enrolled 291 patients with pathologically confirmed GAC who underwent preoperative ¹⁸F-FDG PET/CT. The cohort was randomly divided into a training set (n = 204) and an independent validation set (n = 87). High-dimensional radiomic features were extracted from PET and CT images. Feature selection was performed using the minimum redundancy maximum relevance (mRMR) algorithm and LASSO regression. A radiomics signature (Rad-score) was constructed using XGBoost and integrated with clinical variables. Among five machine learning algorithms evaluated, AdaBoost was identified as the optimal model. Performance was assessed via receiver operating characteristic (ROC) analysis, and interpretability was visualized using SHapley Additive exPlanations (SHAP).

CLDN18.2-positive tumors exhibited a distinct hypometabolic phenotype, characterized by significantly lower SUVmax (P = 0.012) and SUVmean (P < 0.001) compared to negative tumors. The combined multimodal model demonstrated superior discrimination, achieving an AUC of 0.926 (95% CI: 0.891-0.961) in the training cohort and 0.765 (95% CI: 0.665-0.865) in the validation cohort, consistently outperforming single-modality models. SHAP analysis highlighted PET-derived radiomic features and serum CEA as the most influential predictors, confirming the inverse correlation between metabolic activity and CLDN18.2 expression.

The developed ¹⁸F-FDG PET/CT-based multimodal radiomics model serves as an effective, non-invasive tool for predicting CLDN18.2 expression in GAC. The study validates the hypometabolic nature of CLDN18.2-positive tumors, suggesting this radiomics approach can effectively complement traditional biopsy methods for patient stratification.