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已发表论文
整合系统炎症反应指数和乳酸脱氢酶的实时在线列线图用于预测新辅助化学免疫治疗胃癌患者的病理反应
Authors Li L , Cai W, Han H, Chen S, Long B, Zhang G, Jiang X , Zhou H, Qin L, Yu Z, Jiao Z
Received 8 August 2025
Accepted for publication 9 January 2026
Published 21 January 2026 Volume 2026:18 559532
DOI https://doi.org/10.2147/CMAR.S559532
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Javier-David Benitez-Fuentes
Long Li,1,2 Weiwen Cai,1,2 Haobo Han,1,2 Shigong Chen,1,2 Bo Long,1,2 Gengyuan Zhang,1,2 Xiangyan Jiang,1,2 Huinian Zhou,1,2 Long Qin,2,3 Zeyuan Yu,1,2 Zuoyi Jiao1,2
1Department of General Surgery, The Second Hospital of Lanzhou University, Lanzhou, People’s Republic of China; 2The Second Clinical Medical School, Lanzhou University, Lanzhou, People’s Republic of China; 3Cuiying Biomedical Research Center, Lanzhou University Second Hospital, Lanzhou, People’s Republic of China
Correspondence: Zuoyi Jiao, Department of General Surgery, The Second Hospital of Lanzhou University, Lanzhou, People’s Republic of China, Email jiaozy@lzu.edu.cn
Background: Globally, gastric cancer is a significant health burden. Neoadjuvant chemoimmunotherapy (NACI) has emerged as a promising strategy for locally advanced gastric cancer, but responses vary substantially among patients. Predicting major pathological response (MPR) is crucial for treatment personalization.
Objective: To develop and validate a web-based nomogram that integrates readily available clinical and serological markers to predict MPR in gastric cancer patients receiving NACI.
Methods: This retrospective study analyzed 325 gastric cancer patients who underwent NACI and radical resection. A nomogram was constructed using R software and validated with metrics including receiver operating characteristic curve (ROC), area under curve (AUC), calibration curves, and decision curve analysis (DCA), compared to the use of a single biomarker.
Results: The MPR was 53.5%. Multivariate analysis identified lower stomach location (odds ratio (OR) = 2.90; 95% confidence interval (CI): 1.35– 6.22; P = 0.006), histological differentiation grade (OR = 3.43; 95% CI: 1.77– 6.63; P < 0.001), systemic inflammatory response index (SIRI) (OR = 2.02; 95% CI: 1.02– 3.97; P = 0.043) and lactate dehydrogenase (LDH) (OR = 1.02; 95% CI: 1.01– 1.03; P < 0.001) as independent predictors of MPR. The nomogram demonstrated robust discriminative ability, with AUC values of 0.807 (95% CI: 0.751– 0.863) and 0.799 (95% CI: 0.711– 0.888) in the training and testing sets, respectively. Furthermore, DCA further confirmed its significant clinical utility.
Conclusion: We developed and internally validated a nomogram that accurately predicts MPR after NACI. Implemented as a user-friendly web-based calculator, this model enables real-time, individualized estimation of MPR probability and may assist clinicians in tailoring treatment strategies for patients with gastric cancer. Further external and prospective validation is warranted.
Keywords: gastric cancer, systemic inflammatory response index, lactate dehydrogenase, nomogram, major pathological response, web calculator
1Department of General Surgery, The Second Hospital of Lanzhou University, Lanzhou, People’s Republic of China; 2The Second Clinical Medical School, Lanzhou University, Lanzhou, People’s Republic of China; 3Cuiying Biomedical Research Center, Lanzhou University Second Hospital, Lanzhou, People’s Republic of China
Correspondence: Zuoyi Jiao, Department of General Surgery, The Second Hospital of Lanzhou University, Lanzhou, People’s Republic of China, Email jiaozy@lzu.edu.cn
Background: Globally, gastric cancer is a significant health burden. Neoadjuvant chemoimmunotherapy (NACI) has emerged as a promising strategy for locally advanced gastric cancer, but responses vary substantially among patients. Predicting major pathological response (MPR) is crucial for treatment personalization.
Objective: To develop and validate a web-based nomogram that integrates readily available clinical and serological markers to predict MPR in gastric cancer patients receiving NACI.
Methods: This retrospective study analyzed 325 gastric cancer patients who underwent NACI and radical resection. A nomogram was constructed using R software and validated with metrics including receiver operating characteristic curve (ROC), area under curve (AUC), calibration curves, and decision curve analysis (DCA), compared to the use of a single biomarker.
Results: The MPR was 53.5%. Multivariate analysis identified lower stomach location (odds ratio (OR) = 2.90; 95% confidence interval (CI): 1.35– 6.22; P = 0.006), histological differentiation grade (OR = 3.43; 95% CI: 1.77– 6.63; P < 0.001), systemic inflammatory response index (SIRI) (OR = 2.02; 95% CI: 1.02– 3.97; P = 0.043) and lactate dehydrogenase (LDH) (OR = 1.02; 95% CI: 1.01– 1.03; P < 0.001) as independent predictors of MPR. The nomogram demonstrated robust discriminative ability, with AUC values of 0.807 (95% CI: 0.751– 0.863) and 0.799 (95% CI: 0.711– 0.888) in the training and testing sets, respectively. Furthermore, DCA further confirmed its significant clinical utility.
Conclusion: We developed and internally validated a nomogram that accurately predicts MPR after NACI. Implemented as a user-friendly web-based calculator, this model enables real-time, individualized estimation of MPR probability and may assist clinicians in tailoring treatment strategies for patients with gastric cancer. Further external and prospective validation is warranted.
Keywords: gastric cancer, systemic inflammatory response index, lactate dehydrogenase, nomogram, major pathological response, web calculator