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El Nahhas, O.S.M.* ; van Treeck, M.* ; Wölflein, G.* ; Unger, M.* ; Ligero, M.* ; Lenz, T.* ; Wagner, S. ; Hewitt, K.J.* ; Khader, F.* ; Foersch, S.* ; Truhn, D.* ; Kather, J.N.*

From whole-slide image to biomarker prediction: End-to-end weakly supervised deep learning in computational pathology.

Nat. Protoc., DOI: 10.1038/s41596-024-01047-2 (2024)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Hematoxylin- and eosin-stained whole-slide images (WSIs) are the foundation of diagnosis of cancer. In recent years, development of deep learning-based methods in computational pathology has enabled the prediction of biomarkers directly from WSIs. However, accurately linking tissue phenotype to biomarkers at scale remains a crucial challenge for democratizing complex biomarkers in precision oncology. This protocol describes a practical workflow for solid tumor associative modeling in pathology (STAMP), enabling prediction of biomarkers directly from WSIs by using deep learning. The STAMP workflow is biomarker agnostic and allows for genetic and clinicopathologic tabular data to be included as an additional input, together with histopathology images. The protocol consists of five main stages that have been successfully applied to various research problems: formal problem definition, data preprocessing, modeling, evaluation and clinical translation. The STAMP workflow differentiates itself through its focus on serving as a collaborative framework that can be used by clinicians and engineers alike for setting up research projects in the field of computational pathology. As an example task, we applied STAMP to the prediction of microsatellite instability (MSI) status in colorectal cancer, showing accurate performance for the identification of tumors high in MSI. Moreover, we provide an open-source code base, which has been deployed at several hospitals across the globe to set up computational pathology workflows. The STAMP workflow requires one workday of hands-on computational execution and basic command line knowledge.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Korrespondenzautor
Schlagwörter Microsatellite Instability; Artificial-intelligence; Survival Prediction; Cancer; Histopathology
ISSN (print) / ISBN 1754-2189
e-ISSN 1750-2799
Zeitschrift Nature Protocols
Verlag Nature Publishing Group
Verlagsort Heidelberger Platz 3, Berlin, 14197, Germany
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Förderungen European Union
German Federal Ministry of Health (DEEP LIVER)
German Cancer Aid (DECADE)
German Federal Ministry of Education and Research (PEARL)
German Academic Exchange Service (SECAI)
German Federal Joint Committee (TransplantKI)
European Union's Horizon Europe and innovation programme
National Institute for Health and Care Research (NIHR) Leeds Biomedical Research Centre
Lothian NHS
German Federal Ministry of Education and Research (SWAG)
German Research Foundation
Helmholtz Association under the joint research school 'Munich School for Data Science - MUDS'
Joachim Herz Foundation
German Federal Ministry of Education and Research (BMBF)