Dual-energy CT radiomics for predicting neoadjuvant chemotherapy response in locally advanced gastric cancer: A...

To establish and validate a dual-energy CT (DECT) radiomics model for predicting neoadjuvant chemotherapy (NAC) response in locally advanced gastric cancer (LAGC) across two vendors.

This was a secondary analysis drawn from a prospective cohort using DECT data of patients undergoing NAC followed by gastrectomy. Patients were stratified as responders (TRG 0/1) or non-responders (TRG 2/3) based on tumor regression grade (TRG). Radiomics features were extracted from polychromatic images at arterial/venous/delayed phases for building CECT model; Radiomics features extracted from polychromatic images, monochromatic (40 keV, 100 keV) and iodine maps were used to construct DECT model. Predictive features were selected via the least absolute shrinkage and selection operator regression method in the training cohort and tested in the validation cohort. Performances of models were evaluated using areas under the receiver operating characteristic curves (AUCs).

In total, 317 patients were recruited: 221 at training dataset (59.9 ± 9.7 years, 37 females, 184 males) and 96 at validation dataset (61.5 ± 8.0 years, 18 females, 78 males). No clinical factors were found to be related with TRG status. The DECT model outperformed CECT model in the training dataset (AUC: 0.806 vs. 0.729, p = 0.041) and showed non-significant superiority in the validation dataset (AUC: 0.752 vs. 0.679, p = 0.225). High-risk patients defined by DECT model had significantly worse overall survival (HR = 1.996, p = 0.012) and disease-free survival (HR = 1.873, p = 0.037) than low-risk counterparts.

DECT radiomics demonstrates favorable performance in predicting NAC response and stratifying survival outcomes in LAGC, with cross-vendor generalizability supporting potential clinical utility.