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Status: Bibliographieeintrag

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Verfasst von:El Nahhas, Omar S. M. [VerfasserIn]   i
 Treeck, Marko van [VerfasserIn]   i
 Wölflein, Georg [VerfasserIn]   i
 Unger, Michaela [VerfasserIn]   i
 Försch, Sebastian [VerfasserIn]   i
 Lenz, Tim [VerfasserIn]   i
 Wagner, Sophia J. [VerfasserIn]   i
 Hewitt, Katherine J. [VerfasserIn]   i
 Khader, Firas [VerfasserIn]   i
 Försch, Sebastian [VerfasserIn]   i
 Truhn, Daniel [VerfasserIn]   i
 Kather, Jakob Nikolas [VerfasserIn]   i
Titel:From whole-slide image to biomarker prediction
Titelzusatz:end-to-end weakly supervised deep learning in computational pathology
Verf.angabe:Omar S.M. El Nahhas, Marko van Treeck, Georg Wölflein, Michaela Unger, Marta Ligero, Tim Lenz, Sophia J. Wagner, Katherine J. Hewitt, Firas Khader, Sebastian Foersch, Daniel Truhn & Jakob Nikolas Kather
E-Jahr:2025
Jahr:January 2025
Umfang:24 S.
Illustrationen:Illustrationen
Fussnoten:Veröffentlicht: 16. September 2024 ; Gesehen am 24.06.2025
Titel Quelle:Enthalten in: Nature protocols
Ort Quelle:Basingstoke : Nature Publishing Group, 2006
Jahr Quelle:2025
Band/Heft Quelle:20(2025), 1 vom: Jan., Seite 293-316
ISSN Quelle:1750-2799
Abstract: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.
DOI:doi:10.1038/s41596-024-01047-2
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext: https://doi.org/10.1038/s41596-024-01047-2
 Volltext: https://www.nature.com/articles/s41596-024-01047-2
 DOI: https://doi.org/10.1038/s41596-024-01047-2
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Bioinformatics
 Cancer imaging
 Image processing
K10plus-PPN:1928951813
Verknüpfungen:→ Zeitschrift

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