Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study.

Autor:	Niehues JM; Else Kroener Fresenius Center for Digital Health, Technical University Dresden, 01307 Dresden, Germany; Department of Medicine III, University Hospital RWTH Aachen, 52074 Aachen, Germany., Quirke P; Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds LS9 7TF, UK., West NP; Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds LS9 7TF, UK., Grabsch HI; Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds LS9 7TF, UK; Department of Pathology, GROW School for Oncology and Reproduction, Maastricht University Medical Center+, 6229 HX Maastricht, the Netherlands., van Treeck M; Else Kroener Fresenius Center for Digital Health, Technical University Dresden, 01307 Dresden, Germany; Department of Medicine III, University Hospital RWTH Aachen, 52074 Aachen, Germany., Schirris Y; Else Kroener Fresenius Center for Digital Health, Technical University Dresden, 01307 Dresden, Germany; Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands; University of Amsterdam, 1012 WP Amsterdam, the Netherlands., Veldhuizen GP; Else Kroener Fresenius Center for Digital Health, Technical University Dresden, 01307 Dresden, Germany; Department of Medicine III, University Hospital RWTH Aachen, 52074 Aachen, Germany., Hutchins GGA; Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds LS9 7TF, UK., Richman SD; Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds LS9 7TF, UK., Foersch S; Institute of Pathology, University Medical Center Mainz, 55131 Mainz, Germany., Brinker TJ; Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany., Fukuoka J; Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki 852-8523, Japan; Department of Pathology, Kameda Medical Center, Kamogawa 296-8602, Chiba, Japan., Bychkov A; Department of Pathology, Kameda Medical Center, Kamogawa 296-8602, Chiba, Japan., Uegami W; Department of Pathology, Kameda Medical Center, Kamogawa 296-8602, Chiba, Japan., Truhn D; Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, 52074 Aachen, Germany., Brenner H; Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany., Brobeil A; Institute of Pathology, University Hospital Heidelberg, 69120 Heidelberg, Germany; Tissue Bank, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, 69120 Heidelberg, Germany., Hoffmeister M; Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany., Kather JN; Else Kroener Fresenius Center for Digital Health, Technical University Dresden, 01307 Dresden, Germany; Department of Medicine III, University Hospital RWTH Aachen, 52074 Aachen, Germany; Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds LS9 7TF, UK; Department of Medicine I, University Hospital Dresden, 01307 Dresden, Germany; Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, 69120 Heidelberg, Germany. Electronic address: jakob-nikolas.kather@alumni.dkfz.de.
Jazyk:	angličtina
Zdroj:	Cell reports. Medicine [Cell Rep Med] 2023 Apr 18; Vol. 4 (4), pp. 100980. Date of Electronic Publication: 2023 Mar 22.
DOI:	10.1016/j.xcrm.2023.100980
Abstrakt:	Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other biomarkers with high performance and whether DL predictions generalize to external patient populations. Here, we acquire CRC tissue samples from two large multi-centric studies. We systematically compare six different state-of-the-art DL architectures to predict biomarkers from pathology slides, including MSI and mutations in BRAF, KRAS, NRAS, and PIK3CA. Using a large external validation cohort to provide a realistic evaluation setting, we show that models using self-supervised, attention-based multiple-instance learning consistently outperform previous approaches while offering explainable visualizations of the indicative regions and morphologies. While the prediction of MSI and BRAF mutations reaches a clinical-grade performance, mutation prediction of PIK3CA, KRAS, and NRAS was clinically insufficient. Competing Interests: Declaration of interests For transparency, we provide the following information: J.N.K. declares consulting services for Owkin, France; Panakeia, UK; and DoMore Diagnostics, Norway. P.Q. and N.P.W. declare research funding from Roche and PQ consulting and speaker services for Roche. P.Q. is a National Institute of Health Research senior investigator. (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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