Wagner, S.J., Reisenbüchler, D., West, N.P. et al. (46 more authors) (2023) Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study. Cancer Cell, 41. ISSN 1535-6108
Abstract
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.
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Copyright, Publisher and Additional Information: | Ⓒ 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | ||||||||||
Keywords: | artificial intelligence; biomarker; colorectal cancer; deep learning; microsatellite instability; multiple instance learning; transformer | ||||||||||
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Institution: | The University of Leeds | ||||||||||
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Medical Research (LIMR) > Division of Oncology The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Medical Research (LIMR) > Division of Pathology and Data Analytics |
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Depositing User: | Symplectic Publications | ||||||||||
Date Deposited: | 06 Sep 2023 11:41 | ||||||||||
Last Modified: | 06 Sep 2023 11:41 | ||||||||||
Status: | Published | ||||||||||
Publisher: | Cell Press | ||||||||||
Identification Number: | https://doi.org/10.1016/j.ccell.2023.08.002 | ||||||||||
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