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Publications

Journal Article Ppublish

PubMed: 41956235 DOI: 10.1016/j.cgh.2026.03.026

A Machine-Based Learning Model for Recurrence Prediction and Timing After Endoscopic Eradication Therapy for Barrett's Esophagus.

Clin Gastroenterol Hepatol 24(11):2956-2967

Authors: Venkata Akshintala, Samuel Han, Yukun Yan, Raf Bisschops, Case Brennan, Yanna Cai, D Chamil Codipilly, Cary Cotton, Dayna Early, Steven A Edmundowicz, Swathi Eluri, Jazmyne Gallegos, Rohit Goyal, Hazem T Hammad, Rehan Haidry, Thomas Hollander, Khalid Husain, Prasad G Iyer, Justeena Jojo, Vladimir Kushnir, Srinadh Komanduri, Cadman Leggett, Laurence Lovat, V Raman Muthusamy, Amit Rastogi, Stefan Seewald, Nicholas J Shaheen, Adarsh Thaker, Kornpong Vantanasiri, Craig Jones, Sachin Wani

Affiliations

  • Division of Gastroenterology, Johns Hopkins Medical Institutions, Baltimore, Maryland.
  • Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota.
  • Department of Computer Science, Johns Hopkins University, Baltimore, Maryland.
  • Department of Gastroenterology and Hepatology, UZ Leuven, Leuven, Belgium.
  • Division of Gastroenterology and Hepatology, University of Colorado Anschutz Medical Campus, Aurora, Colorado.
  • Division of Gastroenterology and Hepatology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
  • Division of Gastroenterology, Washington University School of Medicine, St Louis, Missouri.
  • Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, Florida.
  • Department of Gastroenterology, Cleveland Clinic London, London, United Kingdom.
  • Division of Gastroenterology and Hepatology, Mayo Clinic, Scottsdale, Arizona.
  • Division of Gastroenterology and Hepatology, Northwestern Feinberg School of Medicine, Chicago, Illinois.
  • Division of Surgery and Interventional Sciences, University College London, London, United Kingdom.
  • Vatche and Tamar Manoukian Division of Digestive Diseases, University of California, Los Angles, Los Angeles, California.
  • Division of Gastroenterology and Hepatology, University of Kansas, Kansas City, Kansas.
  • Center for Gastroenterology, Hirlanden Clinic Zurich, Zurich, Switzerland.
  • Division of Gastroenterology and Hepatology, University of Colorado Anschutz Medical Campus, Aurora, Colorado.

Abstract

BACKGROUND & AIMS

Tools that can predict recurrence in patients with Barrett's esophagus (BE)-related neoplasia treated with endoscopic eradication therapies (EET) to guide surveillance decisions are needed. We aimed to develop and validate a machine learning (ML)-based prediction tool to predict the risk and timing of recurrence status post EET.

METHODS

Three prospective United States databases of patients who underwent EET for BE-related neoplasia and achieved complete eradication of intestinal metaplasia (CE-IM; n = 1114) were utilized to develop and internally validate a ML-based prediction tool using the Random Forest model and imputation techniques. Predictors incorporated in this model included demographics, endoscopy and pathology results, and EET details. A Cox proportional hazards model was utilized to predict the time to recurrence. External validation was performed using the United States Radiofrequency Ablation database (n = 1397).

RESULTS

BE recurrence occurred in 29.2% (n = 734) of patients and BE-related neoplasia recurrence in 10.6% (n = 265), with a mean time to recurrence of 21.3 months (mean follow-up, 37.7 months). The top predictors for recurrence included BE length, body mass index, age, sessions needed to achieve CE-IM, and baseline histology. The model was well-calibrated, and area under the receiver operating characteristic curve (AUC) was 0.92 (95% confidence interval [CI], 0.85-0.95) on internal validation and 0.91 (95% CI, 0.87-0.95) on external validation for BE recurrence. For BE-related neoplasia recurrence, the AUC was 0.90 (95% CI, 0.88-0.93). The model had moderate discriminative performance to predict timing of recurrence with a C-index of 0.701 at 1 year (AUC 0.71), 0.68 (AUC 0.69) at 3 years, and 0.66 (AUC 0.69) at 5 years.

CONCLUSIONS

This United States-based externally validated tool accurately predicts BE and BE-related neoplasia recurrence and timing post EET. This practical tool may help provide a personalized approach to surveillance strategies.

MeSH

Esophagus, Digestive System Diseases, Neoplasms, Barrett Esophagus, Therapeutics, Learning