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Background <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月23日

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  • Chenglong Luo,
  • Jing Li,
  • Wanling Mu,
  • Mengchen Yuan,
  • Pengchao Zhan,
  • Yiyang Liu,
  • Yue Zhou,
  • Liming Li,
  • Changmao Ding,
  • Xuejun Chen,
  • Jianbo Gao

摘要

Background

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.

Methods

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.

Results

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.

Conclusion

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.