A Dual-phase Enhanced CT-based Nomogram for Predicting the Response to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer.

RATIONALE AND

OBJECTIVES

Early prediction of response to neoadjuvant chemotherapy (NAC) in patients with locally advanced gastric cancer (LAGC) helps guide treatment decisions and optimize treatment. This study aimed to establish a radiomics nomogram for predicting NAC response in LAGC patients using dual-phase contrast-enhanced CT (CECT) images.

MATERIALS AND

METHODS

This retrospective study recruited 143 patients with LAGC from January 2018 to March 2024. Radiomics features were extracted from arterial phase (AP) and venous phase (VP) CT images, and were used to develop three radiomics models: AP, VP, and a combined AP_VP model. Clinicopathological characteristics were selected via univariate and multivariate logistic regression. A nomogram was then constructed by integrating the AP_VP radiomics signature with clinicopathological characteristics. The predictive performance of the model was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves. The clinical utility and benefits were further quantified by comparing the nomogram to the clinical model using the net reclassification index (NRI) and integrated discrimination improvement (IDI). Stratified analyses were conducted to explore the model's performance across different patient subgroups.

RESULTS

The AP_VP model performed well in the radiomics models, with AUCs of 0.810 (95% CI, 0.721-0.899) and 0.745 (95% CI, 0.588-0.903) in the training and validation cohorts, respectively. The clinical model was constructed by cT stage and differentiation, with AUCs of 0.723 (95% CI, 0.619-0.827) and 0.754 (95% CI, 0.591-0.916). The nomogram combining radiomics and clinicopathological characteristics achieved AUCs of 0.845 (95% CI, 0.767-0.924) and 0.829 (95% CI, 0.697-0.96), significantly outperforming the clinical model (DeLong test p < 0.05).

CONCLUSION

The nomogram, incorporating dual-phase enhanced CT and clinicopathological characteristics, demonstrated satisfactory performance in predicting NAC response in LAGC patients, assisting with individualized treatment.