Enabling large-scale screening of Barrett's esophagus using weakly supervised deep learning in histopathology
Bouzid, K.; Sharma, H.; Killcoyne, S.; Castro, D. C.; Schwaighofer, A.; Ilse, M.; Salvatelli, V.; Oktay, O.; Murthy, S.; Bordeaux, L.; Moore, L.; O'Donovan, M.; Thieme, A.; Nori, A.; Gehrung, M.; Alvarez-Valle, J.
Show abstract
Timely detection of Barretts 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 Barretts. However, it depends on pathologists 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. Deep learning can improve screening capacity by partly automating Barretts detection, allowing pathologists to prioritize higher risk cases. We propose a deep learning approach for detecting Barretts from routinely stained H&E slides using diagnostic labels, eliminating the need for expensive localized expert annotations. We train and independently validate our approach on two clinical trial datasets, totaling 1,866 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.
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