Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study.

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine pathology slides in colorectal cancer (CRC). However, current approaches rely on convolutional neural networks (CNNs) and have mostly been validated on small patient cohorts. Here, we develop a new transformer-based pipeline for end-to-end biomarker prediction from pathology slides by combining a pre-trained transformer encoder with a transformer network for patch aggregation. Our transformer-based approach substantially improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training and evaluating on a large multicenter cohort of over 13,000 patients from 16 colorectal cancer cohorts, we achieve a sensitivity of 0.99 with a negative predictive value of over 0.99 for prediction of microsatellite instability (MSI) on surgical resection specimens. We demonstrate that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem.
Competing Interests: Declaration of interests J.N.K. reports consulting services for Owkin, France, Panakeia, UK, and DoMore Diagnostics, Norway and has received honoraria for lectures by M.S.D., Eisai, and Fresenius. N.W. has received fees for advisory board activities with BMS, Astellas, GSK, and Amgen, not related to this study. N.W. has received fees for advisory board activities with BMS, Astellas, and Amgen, not related to this study. P.Q. has received fees for advisory board activities with Roche and AMGEN and research funding from Roche through an Innovate UK National Pathology Imaging Consortium grant. H.I.G. has received fees for advisory board activities by AstraZeneca and BMS, not related to this study. M.S.T. is a scientific advisor to Mindpeak and Sonrai Analytics, and has received honoraria recently from BMS, MSD, Roche, Sanofi, and Incyte. He has received grant support from Phillips, Roche, MSD, and Akoya. None of these disclosures are related to this work. D.N.C. has participated in advisory boards for MSD and has received research funding on behalf of the TransSCOT consortium from HalioDx for analyses independent of this study. V.H.K. has served as an invited speaker on behalf of Indica Labs and has received project-based research funding from The Image Analysis Group and Roche outside of the submitted work. No other potential disclosures are reported by any of the authors.
(Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)