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
School of Medicine, Nankai University, Tianjin, China.
Department of General Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
Front Immunol. 2025 Jul 18;16:1603196. doi: 10.3389/fimmu.2025.1603196. eCollection 2025.
The 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.
456 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.
There 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.
Our 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.
与新辅助化疗相比，新辅助化疗联合免疫治疗（NICT）使局部晚期胃癌（LAGC）患者的病理完全缓解（pCR）比例更高。在此，我们构建并验证了一个预测模型，为预测pCR提供临床参考。
本研究纳入了2020年1月至2025年1月期间在7家大型胃肠病医学中心接受NICT后行根治性胃切除术的456例患者，其中320例患者作为训练集，136例患者作为验证集。采用单因素和多因素逻辑回归模型评估影响pCR的因素，并构建列线图模型。采用受试者操作特征曲线（AUC）下面积、校准曲线和决策曲线分析（DCA）评估列线图模型的辨别力、准确性和临床价值。
训练集和验证集的基线特征无显著差异。pCR率和MPR率分别为16.2%和39.5%。腹部增强CT完全缓解、瘤床直径较小、非印戒细胞、年龄≥70岁以及CEA<4.25 ng/mL被证明是pCR的独立预测因素（P<0.05）。列线图模型显示，训练集中预测pCR的AUC（95%CI）为0.862（95%CI：0.807-0.916），验证集中为0.934（95%CI：0.889-0.979）。校准曲线显示，列线图的预测曲线分别与训练集和验证集中的实际pCR拟合良好（Hosmer-Lemeshow检验：χ2 = 9.093，P = 0. sixteen8；χ2 = 2.853，P = 0.827）。决策曲线分析显示评估净效益的结果良好。
我们的列线图模型可为接受NICT的LAGC患者的pCR提供满意的预测效果，这被证明是外科医生制定个性化策略的有价值方法。
