AUTHOR=Cui Hao , Li Rui , Song Liqiang , Yang Yongpu , Yuan Zhen , Zhou Xin , Du Junfeng , Zhang Chaojun , Xu Hong , Chen Lin , Shi Yan , Cui Jianxin , Wei Bo 

TITLE=Development and validation of nomogram for predicting pathological complete response to neoadjuvant chemotherapy and immunotherapy for locally advanced gastric cancer: a multicenter real-world study in China

JOURNAL=Frontiers in Immunology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1603196

DOI=10.3389/fimmu.2025.1603196

ISSN=1664-3224

ABSTRACT=BackgroundThe combination of neoadjuvant chemotherapy and immunotherapy (NICT) brings a higher proportion of pathological complete response (pCR) compared with neoadjuvant chemotherapy for locally advanced gastric cancer (LAGC). Here we constructed and validated a prediction model to provide a clinical reference for predicting pCR.Methods456 patients who accepted radical gastrectomy after NICT in seven large-scale gastrointestinal medical centers from Jan 2020 to Jan 2025 were enrolled in this study, with 320 patients in the training set and 136 patients in the validation set. The uni- and multivariate logistic regression model were used to evaluate the factors influencing pCR and a nomogram model was constructed. The area under the receiver operating characteristic curve (AUC), the calibration curve and decision curve analysis (DCA) were used to evaluate the discrimination, accuracy and clinical value of the nomogram model.ResultsThere was no significant difference in the baseline characteristics between training and validation set. The pCR and MPR rates were respectively 16.2% and 39.5%. Complete response by abdominal enhanced CT, less diameter of tumor bed, non-signet-ring cell, ages≥70 years old, and CEA<4.25 ng/mL were proved as the independent predictors for pCR (P<0.05). The nomogram model showed that the AUC (95%CI) predicting the pCR were 0.862 (95% CI: 0.807-0.916) in the training set and 0.934(95%CI: 0.889-0.979) in the validation set. The calibration curves showed that the prediction curve of the nomogram was good in fit with the actual pCR in the training and validation set respectively (Hosmer-Lemeshow test: χ2 = 9.093, P=0.168; χ2 = 2.853, P=0.827). Decision curve analysis showed a good outcome to assess net benefit.ConclusionOur nomogram model could provide satisfactory predictive effect for the pCR in LAGC patients with NICT, which proves to be a valuable approach for surgeons to make personalized strategies.