# Development and analysis of a nomogram for predicting pathological response to neoadjuvant immunochemotherapy in locally advanced gastric cancer

## TL;DR

This study developed a predictive model to identify gastric cancer patients likely to benefit from neoadjuvant immunochemotherapy based on clinical and biomarker data.

## Contribution

A novel nomogram model with high accuracy was created to predict major pathological response to neoadjuvant immunochemotherapy in gastric cancer.

## Key findings

- Five independent predictors of major pathological response were identified: tumor bed diameter, CEA, CA19-9, NLR, and SII.
- The nomogram achieved an area under the curve of 0.848, showing strong predictive accuracy.
- The model demonstrated good calibration, aligning predicted outcomes with actual results.

## Abstract

Neoadjuvant immunochemotherapy (NICT) has demonstrated potential to enhance tumor regression in patients with locally advanced gastric cancer (LAGC). However, the benefits for some patients are limited. Existing biological markers have only restricted ability to predict pathological response. New biomarkers and predictive models are essential for identifying patients optimally responsive to immunotherapy.

In our retrospective analysis, we included LAGC patients who underwent surgical treatment following NICT at our center between January 2021 and March 2025. Classification was done according to the pathological response rates observed in the excised tumor samples, categorizing patients into major pathological response (MPR) and non-MPR groups. Least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression models were used to pinpoint risk factors linked to MPR. A nomogram was subsequently constructed using the significant predictors.

In total, 113 LAGC patients fitting the criteria were enrolled, with 46 in the MPR cohort and 67 in the non-MPR cohort, yielding an overall MPR incidence of 40.7%. Independent predictors of MPR following NICT were identified through multivariate logistic regression. These include pre-treatment tumor bed diameter < 3.75 cm (OR = 0.22), CEA < 1.765 ng/mL (OR = 0.26), CA19-9 < 18.390 U/mL (OR = 0.148), NLR < 2.422 (OR = 0.265), and SII < 597.483 (OR = 0.194). We constructed a nomogram model with an area under the curve of 0.848 (95% CI:0.773–0.923) based on these five predictors. The calibration curve indicated a robust agreement between forecasted probabilities and real MPR occurrences (Hosmer–Lemeshow test: χ2 = 4.705, 23 P = 0.789).

Tumor bed diameter, CEA, CA19-9, NLR, and SII were determined to be independent predictors of MPR in LAGC patients undergoing NICT. The constructed nomogram demonstrated good accuracy and clinical utility in predicting MPR after NICT, and may help guide the implementation of personalized treatment strategies.