Enabling large-scale screening of Barrett’s esophagus using weakly supervised deep learning in histopathology

Timely detection of Barrett’s esophagus, the pre-malignant condition of esophageal adenocarcinoma, can improve patient survival rates. The Cytosponge-TFF3 test, a non-endoscopic minimally invasive procedure, has been used for diagnosing intestinal metaplasia in Barrett’s. However, it depends on path...

Full description

Saved in:

Bibliographic Details
Published in:	Nature communications Vol. 15; no. 1; p. 2026
Main Authors:	Bouzid, Kenza, Sharma, Harshita, Killcoyne, Sarah, Castro, Daniel C., Schwaighofer, Anton, Ilse, Max, Salvatelli, Valentina, Oktay, Ozan, Murthy, Sumanth, Bordeaux, Lucas, Moore, Luiza, O’Donovan, Maria, Thieme, Anja, Nori, Aditya, Gehrung, Marcel, Alvarez-Valle, Javier
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 11-03-2024 Nature Publishing Group Nature Portfolio
Subjects:	631/114/1305 631/67/2322 692/4020/1503/1504/1477 692/700/1421 Adenocarcinoma Adenocarcinoma - diagnosis Adenocarcinoma - pathology Annotations Barrett Esophagus - diagnosis Barrett Esophagus - pathology Barrett's esophagus Biomarkers Datasets Deep Learning Diagnostic systems Esophageal cancer Esophageal Neoplasms - diagnosis Esophageal Neoplasms - pathology Esophagus Histopathology Humanities and Social Sciences Humans Labels Metaplasia multidisciplinary Science Science (multidisciplinary) Survival Workflow
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	Timely detection of Barrett’s esophagus, the pre-malignant condition of esophageal adenocarcinoma, can improve patient survival rates. The Cytosponge-TFF3 test, a non-endoscopic minimally invasive procedure, has been used for diagnosing intestinal metaplasia in Barrett’s. However, it depends on pathologist’s assessment of two slides stained with H&E and the immunohistochemical biomarker TFF3. This resource-intensive clinical workflow limits large-scale screening in the at-risk population. To improve screening capacity, we propose a deep learning approach for detecting Barrett’s from routinely stained H&E slides. The approach solely relies on diagnostic labels, eliminating the need for expensive localized expert annotations. We train and independently validate our approach on two clinical trial datasets, totaling 1866 patients. We achieve 91.4% and 87.3% AUROCs on discovery and external test datasets for the H&E model, comparable to the TFF3 model. Our proposed semi-automated clinical workflow can reduce pathologists’ workload to 48% without sacrificing diagnostic performance, enabling pathologists to prioritize high risk cases. Diagnosis of Barrett’s esophagus depends on pathologist assessment of stained slides. Here, the authors utilise a deep learning approach to prioritize potential cases using diagnostic labels in two datasets, with the aim to improve Barrett’s screening capacity.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2041-1723 2041-1723
DOI:	10.1038/s41467-024-46174-2