Abstract
OBJECTIVE: To identify the factors influencing pathological responses after neoadjuvant therapy in advanced gastric cancer and to construct an effective prediction model for an improved response. METHODS: Clinical data from 100 patients with advanced gastric cancer who received neoadjuvant therapy at The Fifth Hospital of Wuhan from January 2020 to December 2023 were retrospectively analyzed. Basic data, laboratory test results, and other patient information were collected. Univariate and multivariate logistic regression were used to analyze the factors influencing good disease recovery after neoadjuvant therapy. Based on the results of multi-factor analysis, a nomogram risk prediction model was constructed, and its effectiveness was validated. The model's discriminatory power was assessed using the receiver operating characteristic curve (ROC) and the area under the ROC curve (AUC), while its fit was evaluated using a calibration curve. The model's consistency was assessed using the Hosmer-Lemeshow (HL) test. RESULTS: Among the 100 patients, 22 (22%) had a good pathological response. Multivariate analysis showed that tumor differentiation, carcinoembryonic antigen (CEA), longest tumor diameter, and cN stage were significant factors influencing the pathological response of patients after neoadjuvant therapy. Based on the above indicators, a nomogram prediction model was constructed, with the following formula: Logit (P) = -1.653 + 1.562 × (tumor differentiation degree) + 1.925 × (CEA) + 1.620 × (longest tumor diameter) + 1.483 × (cN stage). The AUCs of the training set and the test set were 0.884 (95% CI: 0.778-0.990) and 0.861 (95% CI: 0.709-1.000), respectively. The HL test showed good fit (χ(2) = 4.939, P = 0.764). The calibration curve demonstrated that the predicted values closely matched the observed values. CONCLUSION: Tumor differentiation, CEA, longest tumor diameter, and cN stage are significant factors influencing the pathological response to neoadjuvant therapy in advanced gastric cancer. The prediction model developed based on these factors demonstrates good predictive performance and may aid in clinical decision-making.