ArXiv Preprint
Background: Deep learning (DL) can extract predictive and prognostic
biomarkers from routine pathology slides in colorectal cancer. For example, a
DL test for the diagnosis of microsatellite instability (MSI) in CRC has been
approved in 2022. Current approaches rely on convolutional neural networks
(CNNs). Transformer networks are outperforming CNNs and are replacing them in
many applications, but have not been used for biomarker prediction in cancer at
a large scale. In addition, most DL approaches have been trained on small
patient cohorts, which limits their clinical utility. Methods: In this study,
we developed a new fully transformer-based pipeline for end-to-end biomarker
prediction from pathology slides. We combine a pre-trained transformer encoder
and a transformer network for patch aggregation, capable of yielding single and
multi-target prediction at patient level. We train our pipeline on over 9,000
patients from 10 colorectal cancer cohorts. Results: A fully transformer-based
approach massively improves the performance, generalizability, data efficiency,
and interpretability as compared with current state-of-the-art algorithms.
After training on a large multicenter cohort, we achieve a sensitivity of 0.97
with a negative predictive value of 0.99 for MSI prediction on surgical
resection specimens. We demonstrate for the first time that resection
specimen-only training reaches clinical-grade performance on endoscopic biopsy
tissue, solving a long-standing diagnostic problem. Interpretation: A fully
transformer-based end-to-end pipeline trained on thousands of pathology slides
yields clinical-grade performance for biomarker prediction on surgical
resections and biopsies. Our new methods are freely available under an open
source license.
Sophia J. Wagner, Daniel Reisenbüchler, Nicholas P. West, Jan Moritz Niehues, Gregory Patrick Veldhuizen, Philip Quirke, Heike I. Grabsch, Piet A. van den Brandt, Gordon G. A. Hutchins, Susan D. Richman, Tanwei Yuan, Rupert Langer, Josien Christina Anna Jenniskens, Kelly Offermans, Wolfram Mueller, Richard Gray, Stephen B. Gruber, Joel K. Greenson, Gad Rennert, Joseph D. Bonner, Daniel Schmolze, Jacqueline A. James, Maurice B. Loughrey, Manuel Salto-Tellez, Hermann Brenner, Michael Hoffmeister, Daniel Truhn, Julia A. Schnabel, Melanie Boxberg, Tingying Peng, Jakob Nikolas Kather
2023-01-23
