ArticleDigestive diseases and sciences2025
A Simple, Interpretable Machine Learning Model Based on Clinical Factors Accurately Predicts Incident Dysplasia or Malignancy in Barrett's Esophagus.
Article in Digestive diseases and sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeIdentifying patients likely to develop dysplasia or malignancy is critical for effective surveillance in patients with Barrett's Esophagus (BE). However, current predictive models are limited. We evaluated the performance of machine learning (ML) models in predicting incident dysplasia or malignancy in a cohort of veteran patients with BE.
methodsWe analyzed data from 598 patients newly diagnosed with non-dysplastic BE (NDBE), BE indefinite for dysplasia (BE-IND), and BE with non-persistent low-grade dysplasia (LGD) at the Michael DeBakey Veterans Affairs Medical Center from November 1990 to January 2019 with follow-up through January 2024. Progressors were patients who developed persistent LGD, HGD, or EAC within 5 years of index endoscopy. Six models were evaluated, encompassing regression and ensemble-based ML methods.
resultsOf 598 qualifying patients, 61 (10.2%) progressed. Longer segments and indefinite/non-persistent LGD pathology were associated with higher risk of progression in unadjusted analyses. BE segment length remained significant on multivariate analysis (OR 1.26; 95% CI 1.17-1.36 per 1 cm increase). A decision tree (DT) model, using only segment length, achieved the highest discrimination (AUROC = 0.79) and excellent sensitivity (93.3%). The DT model also identified segment length thresholds for risk stratification: < 0.95 cm (minimal risk), 0.95-2.44 cm (low), 2.44-9.45 cm (moderate), > 9.45 cm (high).
conclusionsA simple, interpretable DT model with segment length as the sole predictor outperformed regression and complex ML-based models in predicting BE progressors. Findings align with European Society of Gastrointestinal Endoscopy (ESGE) guidelines suggesting tailored surveillance based on segment length and provide actionable thresholds. These results offer a practical ML tool for BE surveillance.
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