Cancer Medicine (Mar 2024)

Nomogram for predicting pathological response to neoadjuvant treatment in patients with locally advanced gastric cancer: Data from a phase III clinical trial

  • Han Shao,
  • Nai Li,
  • Yi‐hong Ling,
  • Ji‐jin Wang,
  • Yi Fang,
  • Ming Jing,
  • Zhi‐wei Zhou,
  • Yu‐jing Zhang

DOI
https://doi.org/10.1002/cam4.7122
Journal volume & issue
Vol. 13, no. 6
pp. n/a – n/a

Abstract

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Abstract Purpose This study aimed to establish a nomogram using routinely available clinicopathological parameters to predict the pathological response in patients with locally advanced gastric cancer (LAGC) undergoing neoadjuvant treatment. Materials and Methods We conducted this study based on the ongoing Neo‐CRAG trial, a prospective study focused on preoperative treatment in patients with LAGC. A total of 221 patients who underwent surgery following neoadjuvant chemotherapy (nCT) or neoadjuvant chemoradiotherapy (nCRT) at Sun Yat‐sen University Cancer Center between June 2013 and July 2022 were included in the analysis. We defined complete or near‐complete pathological regression and ypN0 as good response (GR), and determined the prognostic value of GR by Kaplan–Meier survival analysis. Eventually, a nomogram for predicting GR was developed based on statistically identified predictors through multivariate logistic regression analysis and internally validated by the bootstrap method. Results GR was confirmed in 54 patients (54/221, 24.4%). Patients who achieved GR had a longer progression‐free survival and overall survival. Then, five independent factors, including pretreatment tumor differentiation, clinical T stage, monocyte count, CA724 level, and the use of nCRT, were identified. Based on these predictors, the nomogram was established with an area under the curve (AUC) of 0.777 (95% CI, 0.705–0.850) and a bias‐corrected AUC of 0.752. Conclusion A good pathological response after neoadjuvant treatment was associated with an improved prognosis in LAGC patients. The nomogram we established exhibits a high predictive capability for GR, offering potential value in devising personalized and precise treatment strategies for LAGC patients.

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