Back

A composite biomarker for esophageal cancer risk from automated analysis of a non-endoscopic device

Berman, A. G.; Fitzgerald, R. C.; Markowetz, F.

2021-08-25 gastroenterology
10.1101/2021.08.20.21262366 medRxiv
Show abstract

Barretts esophagus containing intestinal metaplasia predisposes to cancer, yet the majority of cases are undiagnosed. The length of a Barretts segment is a key indicator of cancer risk, but measuring it has so far relied on endoscopy, which is expensive and invasive. Cytosponge-TFF3 is a minimally-invasive test that identifies intestinal metaplasia for endoscopic confirmation. We report a machine learning technique to quantify the extent of intestinal metaplasia and predict Barretts segment length from whole-slide image tile counts automatically generated from Cytosponge-TFF3 histology slides. Utilizing data from 529 patients, our segment length prediction model achieves an average validation fold accuracy of 0.84. Applying this algorithm to an independent test set of 162 patients from a screening trial shows a precision of 0.90 for identifying short-segment disease. This advance will enable higher-risk patients to be prioritized for endoscopy while saving more than half of Cytosponge-TFF3-positive patients from endoscopy in the screening setting.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.