Clinical Edge

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Machine learning may enable noninvasive prediction of Barrett’s esophagus

Key clinical point: A new risk prediction panel based on machine learning may enable routine, noninvasive identification of patients with a high risk of Barrett’s esophagus.

Major finding: In external validation testing, area under the receiver-operator curve (AUC) was 0.81.

Study details: A machine learning study based on two previous case-control trials, BEST2 and BOOST.

Disclosures: The study was funded by the Charles Wolfson Charitable Trust and Guts UK, the National Institute for Health Research Biomedical Research Centre, Cancer Research UK, and the Wellcome/EPSRC Centre for Interventional and Surgical Sciences at University College London. Dr. Fitzgerald reported a relationship with Medtronic via licensing of the cytosponge device.

Citation:

Rosenfeld A et al. Lancet Digital Health. 2019 Dec 5. doi: 10.1016/S2589-7500(19)30216-X.