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Background <p>Adenocarcinoma of the esophagogastric junction (AEG) often presents with subtle early symptoms and delayed diagnosis, frequently resulting in liver metastasis and a poor prognosis. This study aimed to investigate the primary risk factors influencing postoperative liver metastasis in AEG and to develop a simple predictive model to facilitate clinical risk stratification and individualized follow-up strategies.</p> Methods <p>This retrospective study analyzed data from 524 patients with AEG who underwent radical resection, with patients randomly divided into a training group (368 cases) and a validation group (156 cases). Clinical and pathological information was collected, and independent factors significantly associated with postoperative liver metastasis were identified using univariate and multivariate Cox regression analyses. Based on these findings, a nomogram model was constructed to predict the 1-year and 3-year liver metastasis-free survival rates, and the model’s predictive performance and clinical utility were evaluated using the C-index, ROC curves, and calibration curves.</p> Results <p>Multivariate analysis revealed that thoracoabdominal surgery, higher N stage (N1 and N2/N3), moderate-to-poor differentiation, the presence of vascular tumor thrombus, intestinal type according to Lauren classification, and P53 status were independent risk factors for postoperative liver metastasis. The nomogram model based on these six indicators demonstrated high predictive accuracy in both the training group (C-index = 0.966) and the validation group (C-index = 0.976), with ROC AUCs for both the 1-year and 3-year predictions exceeding 0.96 and favorable calibration curves, confirming the model’s strong predictive efficacy.</p> Conclusions <p>The predictive model developed in this study can effectively assess the risk of postoperative liver metastasis in patients with AEG, thereby providing a scientific basis for postoperative monitoring and individualized treatment, with the potential to improve patient outcomes in clinical practice.</p>

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Comprehensive analysis of risk factors and nomogram development for predicting hepatic metastasis following radical resection of adenocarcinoma of the esophagogastric junction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月27日

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  • Lili Deng,
  • Jie Sun,
  • Jing Wang,
  • Xiaokai Duan,
  • Baozhong Li

摘要

Background

Adenocarcinoma of the esophagogastric junction (AEG) often presents with subtle early symptoms and delayed diagnosis, frequently resulting in liver metastasis and a poor prognosis. This study aimed to investigate the primary risk factors influencing postoperative liver metastasis in AEG and to develop a simple predictive model to facilitate clinical risk stratification and individualized follow-up strategies.

Methods

This retrospective study analyzed data from 524 patients with AEG who underwent radical resection, with patients randomly divided into a training group (368 cases) and a validation group (156 cases). Clinical and pathological information was collected, and independent factors significantly associated with postoperative liver metastasis were identified using univariate and multivariate Cox regression analyses. Based on these findings, a nomogram model was constructed to predict the 1-year and 3-year liver metastasis-free survival rates, and the model’s predictive performance and clinical utility were evaluated using the C-index, ROC curves, and calibration curves.

Results

Multivariate analysis revealed that thoracoabdominal surgery, higher N stage (N1 and N2/N3), moderate-to-poor differentiation, the presence of vascular tumor thrombus, intestinal type according to Lauren classification, and P53 status were independent risk factors for postoperative liver metastasis. The nomogram model based on these six indicators demonstrated high predictive accuracy in both the training group (C-index = 0.966) and the validation group (C-index = 0.976), with ROC AUCs for both the 1-year and 3-year predictions exceeding 0.96 and favorable calibration curves, confirming the model’s strong predictive efficacy.

Conclusions

The predictive model developed in this study can effectively assess the risk of postoperative liver metastasis in patients with AEG, thereby providing a scientific basis for postoperative monitoring and individualized treatment, with the potential to improve patient outcomes in clinical practice.