# The value of 3D contrast-enhanced CT radiomics in predicting response to neoadjuvant chemotherapy for adenocarcinoma of the esophagogastric junction: a two-center study

## TL;DR

This study shows that 3D CT radiomics can help predict how well patients with esophagogastric junction cancer will respond to chemotherapy, potentially improving treatment decisions.

## Contribution

A nomogram combining 3D contrast-enhanced CT radiomics and clinical features is developed to predict neoadjuvant chemotherapy response in AEG patients.

## Key findings

- The combined model of radiomics and clinical features achieved an AUC of 0.894 in predicting chemotherapy response.
- The nomogram showed good calibration and clinical applicability across training and validation cohorts.
- The model included tumor thickness, lymph node diameter, and radiomics score as key predictors.

## Abstract

To investigate the feasibility of 3D contrast-enhanced CT radiomics features to predict response to neoadjuvant chemotherapy (NAC) for adenocarcinoma of the esophagogastric junction (AEG) and to develop and validate a nomogram to assist in clinical decision-making.

The clinical, pathological, and CT data of 239 patients with locally advanced AEG who underwent NAC and radical resection were retrospectively collected between March 2016 and June 2023 from two independent Chinese medical centers. They were randomly assigned to a training cohort, an internal verification cohort, or an external verification cohort. Based on the CT radiomics features after dimension reduction, the radiomics model was constructed using linear discriminant analysis as the classifier to obtain the radiomics score. Clinical characteristics were screened, and multivariable logistic regression was applied to construct the clinical model. The combined model was generated by integrating clinical features and radiomics scores, upon which a nomogram was subsequently developed. Finally, receiver operating characteristic curves, calibration curves, and decision curves were plotted to evaluate the predictive performance, calibration performance, and clinical benefits of each model for the efficacy of NAC in AEG patients.

Overall, 86 of the 239 patients responded well to NAC. The nomogram was comprised of tumor thickness, lymph node short diameter, and the radiomics score. In the training cohort, the AUC values of the clinical model, the radiomics model, and the combined model for predicting NAC response were 0.771 (95% CI, 0.682–0.860), 0.823 (95% CI, 0.742–0.903), and 0.894 (95% CI, 0.834–0.954), respectively, with the combined model displaying optimal discriminatory power. The combined model also demonstrated satisfactory predictive performance in the internal and external validation cohorts, with AUC values of 0.859 and 0.775, respectively. The calibration curves for the three cohorts showed good agreement between predictions and actual observations. Lastly, decision curve analysis highlighted the clinical applicability of the combined model.

The nomogram integrating radiomics and clinical characteristics demonstrated good performance in predicting NAC response in AEG, suggesting its possible role as a decision-support tool for treatment individualization. These preliminary findings warrant confirmation in future studies.

The online version contains supplementary material available at 10.1186/s12885-026-15609-y.