Imaging lymph node regression can predict the pathological tumor regression grade in gastric cancer after neoadjuvant treatment

Neoadjuvant chemotherapy has become the standard treatment for locally advanced gastric cancer. The tumor regression grade system is an effective and widely used tool for the evaluation of treatment response to neoadjuvant chemotherapy. However, whether tumor regression grade could be predicted using clinical characteristics is uncertain.A total of 287 locally advanced gastric cancer patients from 2014 to 2021 were retrospectively included. According to the College of American Pathologists' tumor regression grade system, patients were classified into response group (tumor regression grade 0-1) and non-response group (tumor regression grade 2-3). Associations between clinical characteristics and neoadjuvant chemotherapy response were performed by the logistic regression model. The Kaplan-Meier method was used to estimate the survival. A prediction scoring system was constructed based on the β coefficients of multivariate analysis. The receiver operating characteristic curve and decision curve analysis were used to evaluate the performance of the predictive scoring system.Survival analysis showed that patients with tumor regression grades 0 to 1 had significantly better disease-free survival and overall survival than the tumor regression grades 2 to 3. Tumor differentiation, ycT stage, immunotherapy, and lymph node regression were independent predictors of pathological response to neoadjuvant chemotherapy. We further developed a scoring system to predict the tumor regression grade. The receiver operating characteristic and decision curve analysis showed good predictive performance of the scoring system.Lymph node regression could be used as a predictor for pathological response. We developed a scoring system to predict the treatment response of patients with gastric cancer receiving neoadjuvant chemotherapy. The scoring system based on the predictors could provide guidance for making clinical decisions.