BACKGROUND: Predicting pathological response to neoadjuvant chemotherapy combined with immunotherapy (NACI) in locally advanced gastric cancer (LAGC) remains challenging. This study aimed to develop a non-invasive predictive model by integrating intratumoral habitat imaging features, peritumoral radiomics, and clinical characteristics from baseline contrast-enhanced CT (CECT). METHODS: In this retrospective, two-center study, 281 LAGC patients receiving NACI followed by surgery were enrolled. Patients were classified as responders or non-responders based on tumor regression grade (TRG). Tumors on pre-treatment CECT were segmented into distinct habitats via K-means clustering. Radiomic features were extracted from intra-tumoral, habitat subregions, and 1-mm/2-mm peritumoral areas. After feature selection, multiple models were constructed using machine learning algorithms. The optimal combined model integrated habitat features, peritumoral (1 mm) features, and clinical factors. RESULTS: The combined model demonstrated superior predictive performance, achieving area under the curve (AUC) values of 0.885, 0.789, and 0.763 in the training, internal validation, and external test cohorts, respectively. It outperformed models based solely on intratumoral radiomics, habitat features, peritumoral features, or clinical data. Decision curve analysis confirmed its clinical utility. CONCLUSION: A CT-based multiparametric model that captures intratumoral spatial heterogeneity and peritumoral microenvironmental information can effectively predict pathological response to NACI in LAGC patients preoperatively. This approach offers a promising non-invasive tool to guide personalized treatment selection and optimize therapeutic strategies.
Ran et al. (Mon,) studied this question.