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Machine learning-based model for predicting distant lymph node metastasis in esophageal cancer.

Guojun Wang1, Jin Wang1

  • 1Department of Surgery, Medical and Health Center, Beijing Friendship Hospital, Capital Medical University, Beijing, China.

Journal of Thoracic Disease
|October 29, 2025
PubMed
Summary

Machine learning models can predict distant lymph node metastasis (LNM) in esophageal cancer. The gradient boosting model showed the best performance, aiding in early diagnosis and patient management.

Keywords:
Esophagus cancerdistant lymph node metastasis (distant LNM)machine learning

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Area of Science:

  • Oncology
  • Medical Informatics
  • Machine Learning

Background:

  • Distant lymph node metastasis (LNM) in esophageal cancer is hard to detect and linked to poor outcomes.
  • Accurate prediction of LNM is crucial for effective treatment and patient stratification.

Purpose of the Study:

  • Develop and evaluate machine learning models for predicting distant LNM in esophageal cancer.
  • Enhance diagnostic accuracy and guide personalized treatment strategies.

Main Methods:

  • Utilized SEER database (2000-2021) for demographic and clinicopathological data.
  • Constructed seven machine learning models (RF, DT, XGBoost, GB, Naïve Bayes, ANN, ADB).
  • Evaluated model performance using accuracy, F1 score, recall, and AUC; validated in a clinical cohort.

Main Results:

  • The gradient boosting (GB) model achieved the highest accuracy (0.929) and AUC (0.912).
  • Tumor stage, node stage, age, liver, and lung metastasis were key predictors.
  • Validation showed high accuracy (0.818), recall (0.857), and AUC (0.857).

Conclusions:

  • The GB model is effective for predicting LNM in esophageal cancer.
  • Model interpretability aids in identifying significant predictors.
  • This tool can assist clinicians in early LNM prediction and patient management.