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Related Concept Videos

Barrett Esophagus-I: Introduction01:21

Barrett Esophagus-I: Introduction

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Barrett's esophagus is a medical condition where the esophageal mucosa is significantly damaged by stomach acid or other digestive fluids, often due to long-term exposure associated with gastroesophageal reflux disease (GERD). In GERD, a weakened or abnormally relaxed lower esophageal sphincter allows stomach acid to flow persistently into the esophagus.
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Individuals with Barrett's esophagus are often asymptomatic, but they may experience symptoms commonly associated with GERD, such as heartburn and acid regurgitation. Additional symptoms can include difficulty swallowing, chest pain, unintentional weight loss, blood in the stool (which may appear black, tarry, or bloody), and episodes of vomiting.
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Gastroesophageal reflux disease, or GERD, is a persistent medical condition that affects many individuals worldwide. Its clinical manifestations can vary greatly, making diagnosis and management challenging for healthcare professionals. The following is a comprehensive overview of the clinical manifestations, assessment, and management strategies for GERD.
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Related Experiment Video

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A Machine-Based Learning Model for Recurrence Prediction and Timing After Endoscopic Eradication Therapy for

Venkata Akshintala1, Samuel Han2, Yukun Yan3

  • 1Division of Gastroenterology, Johns Hopkins Medical Institutions, Baltimore, Maryland.

Clinical Gastroenterology and Hepatology : the Official Clinical Practice Journal of the American Gastroenterological Association
|April 9, 2026
PubMed
Summary

A new machine learning tool accurately predicts recurrence of Barrett's esophagus (BE) and related neoplasia after endoscopic eradication therapy (EET). This tool aids in personalizing surveillance strategies for patients with BE.

Keywords:
Barrett’s EsophagusEndoscopic Eradication TherapyRecurrenceSurveillance

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Area of Science:

  • Gastroenterology
  • Oncology
  • Medical Informatics

Background:

  • Patients with Barrett's esophagus (BE) undergoing endoscopic eradication therapy (EET) require tools to predict neoplasia recurrence for effective surveillance.
  • Current surveillance strategies lack precision in identifying patients at high risk for recurrence post-EET.

Purpose of the Study:

  • To develop and validate a machine learning (ML)-based prediction tool for estimating the risk and timing of recurrence after EET in BE patients.
  • To enhance personalized surveillance by accurately predicting post-EET recurrence of BE and BE-related neoplasia.

Main Methods:

  • A Random Forest ML model was developed using data from three prospective US databases (n=1114) of BE patients achieving complete intestinal metaplasia eradication (CE-IM).
  • Predictors included demographics, endoscopy, pathology, and EET details. Time to recurrence was analyzed using Cox proportional hazards models.
  • External validation was performed on a US Radiofrequency Ablation database (n=1397).

Main Results:

  • Recurrence of BE occurred in 29.2% and BE-related neoplasia in 10.6% of patients, with a mean recurrence time of 21.3 months.
  • The ML model demonstrated high accuracy for predicting BE recurrence (AUC 0.92 internally, 0.91 externally) and neoplasia recurrence (AUC 0.90).
  • Top predictors included BE length, BMI, age, CE-IM sessions, and baseline histology. Moderate performance was observed for predicting recurrence timing.

Conclusions:

  • An externally validated ML tool accurately predicts the risk and timing of BE and BE-related neoplasia recurrence post-EET.
  • This practical tool supports personalized surveillance strategies, potentially improving patient outcomes and resource allocation.