ArticleInternational journal of environmental research and public health2021
Risk Prediction of Barrett's Esophagus in a Taiwanese Health Examination Center Based on Regression Models.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Cost-effective identification of Barrett's esophagus in the community: A first step towards screening.Journal of gastroenterology and hepatology · 2024Article
- Development of a prediction score for Barrett's esophagus in Japanese health checkup settings.Esophagus : official journal of the Japan Esophageal Society · 2024Article
- Analysis of immune related gene expression profiles and immune cell components in patients with Barrett esophagus.Scientific reports · 2022Article
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Determining the target population for the screening of Barrett's esophagus (BE), a precancerous condition of esophageal adenocarcinoma, remains a challenge in Asia. The aim of our study was to develop risk prediction models for BE using logistic regression (LR) and artificial neural network (ANN) methods. Their predictive performances were compared. We retrospectively analyzed 9646 adults aged ≥20 years undergoing upper gastrointestinal endoscopy at a health examinations center in Taiwan. Evaluated by using 10-fold cross-validation, both models exhibited good discriminative power, with comparable area under curve (AUC) for the LR and ANN models (Both AUC were 0.702). Our risk prediction models for BE were developed from individuals with or without clinical indications of upper gastrointestinal endoscopy. The models have the potential to serve as a practical tool for identifying high-risk individuals of BE among the general population for endoscopic screening.
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