A Radiomics-Clinical Nomogram for Pre-Treatment Prediction of Neoadjuvant Chemotherapy Response in Locally Advanced Gastric Cancer.

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Abstract

<b>Objective:</b> To develop and evaluate a nomogram integrating radiomic features from contrast-enhanced CT with clinical variables for pre-treatment predictions of the response to neoadjuvant therapy (NAT) in locally advanced gastric cancer (LAGC). <b>Methods:</b> In this retrospective multicenter study, 183 LAGC patients from the First Affiliated Hospital of Nanjing Medical University (2014-2023) were included. Radiomic features were extracted from manually delineated pre-treatment CT regions of interest. A machine learning-based predictive model combining radiomic scores and clinical data was constructed. Model performance was assessed using receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA). <b>Results:</b> Multivariate analysis identified the radiomic score, preoperative <i>N</i> stage, and neoadjuvant regimen as independent predictors of NAT responses (all <i>p</i> < 0.05). The integrated nomogram achieved an area under the ROC curve of 0.807 and showed a moderate net benefit in DCA compared with the radiomics-only model. <b>Conclusions:</b> The radiomics-clinical nomogram demonstrates moderate predictive performance for pre-treatment stratification of NAT responses in LAGC. These findings are exploratory and hypothesis-generating, and further validation in independent cohorts is required before clinical application.

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