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Wagner, S. ; Reisenbüchler, D. ; West, N.P.* ; Niehues, J.M.* ; Zhu, J.* ; Foersch, S.* ; Veldhuizen, G.P.* ; Quirke, P.* ; Grabsch, H.I.* ; van den Brandt, P.A.* ; Hutchins, G.G.A.* ; Richman, S.D.* ; Yuan, T.* ; Langer, R.* ; Jenniskens, J.C.A.* ; Offermans, K.* ; Mueller, W.* ; Gray, R.* ; Gruber, S.B.* ; Greenson, J.K.* ; Rennert, G.* ; Bonner, J.D.* ; Schmolze, D.* ; Jonnagaddala, J.* ; Hawkins, N.J.* ; Ward, R.L.* ; Morton, D.* ; Seymour, M.* ; Magill, L.* ; Nowak, M.* ; Hay, J.* ; Koelzer, V.H.* ; Church, D.N.* ; Church, D.* ; Domingo, E.* ; Edwards, J.* ; Glimelius, B.* ; Gogenur, I.* ; Harkin, A.* ; Iveson, T.* ; Jaeger, E.* ; Kelly, C.* ; Kerr, R.* ; Maka, N.* ; Morgan, H.* ; Oien, K.* ; Orange, C.* ; Palles, C.* ; Roxburgh, C.* ; Sansom, O.* ; Saunders, M.* ; Tomlinson, I.* ; Matek, C. ; Geppert, C.* ; Peng, C.* ; Zhi, C.* ; Ouyang, X.* ; James, J.A.* ; Loughrey, M.B.* ; Salto-Tellez, M.* ; Brenner, H.* ; Hoffmeister, M.* ; Truhn, D.* ; Schnabel, J.A. ; Boxberg, M.* ; Peng, T. ; Kather, J.N.*

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

Cancer Cell 41, 1650-1661.e4 (2023)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Artificial Intelligence ; Biomarker ; Colorectal Cancer ; Deep Learning ; Microsatellite Instability ; Multiple Instance Learning ; Transformer; Colon-cancer; Microsatellite Instability; Survival; Decision; Model
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 1535-6108
e-ISSN 1878-3686
Zeitschrift Cancer Cell
Quellenangaben Band: 41, Heft: 9, Seiten: 1650-1661.e4 Artikelnummer: , Supplement: ,
Verlag Cell Press
Verlagsort Cambridge, Mass.
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Institute of Computational Biology (ICB)
Institute of AI for Health (AIH)
Institute for Machine Learning in Biomed Imaging (IML)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-530006-001
G-503800-001
G-540007-001
G-507100-001
Förderungen NCI NIH HHS
Medical Research Council
Cancer Research UK
Department of Health
DOI 10.1016/j.ccell.2023.08.002
WOS ID 001084385700001
Scopus ID 85169513346
PubMed ID 37652006
Erfassungsdatum 2023-10-18
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