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Status: Bibliographieeintrag

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Verfasst von:Echle, Amelie [VerfasserIn]   i
 Rindtorff, Niklas [VerfasserIn]   i
 Brinker, Titus Josef [VerfasserIn]   i
 Lüdde, Tom [VerfasserIn]   i
 Pearson, Alexander Thomas [VerfasserIn]   i
 Kather, Jakob Nikolas [VerfasserIn]   i
Titel:Deep learning in cancer pathology
Titelzusatz:a new generation of clinical biomarkers
Verf.angabe:Amelie Echle, Niklas Timon Rindtorff, Titus Josef Brinker, Tom Luedde, Alexander Thomas Pearson and Jakob Nikolas Kather
Jahr:2021
Umfang:11 S.
Illustrationen:Illustrationen
Fussnoten:Online veröffentlicht: 18. November 2020 ; Gesehen am 05.04.2024
Titel Quelle:Enthalten in: British journal of cancer
Ort Quelle:Edinburgh : Nature Publ. Group, 1999
Jahr Quelle:2021
Band/Heft Quelle:124(2021), 4, Seite 686-696
ISSN Quelle:1532-1827
Abstract:Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers. However, the growing number of these complex biomarkers tends to increase the cost and time for decision-making in routine daily oncology practice; furthermore, biomarkers often require tumour tissue on top of routine diagnostic material. Nevertheless, routinely available tumour tissue contains an abundance of clinically relevant information that is currently not fully exploited. Advances in deep learning (DL), an artificial intelligence (AI) technology, have enabled the extraction of previously hidden information directly from routine histology images of cancer, providing potentially clinically useful information. Here, we outline emerging concepts of how DL can extract biomarkers directly from histology images and summarise studies of basic and advanced image analysis for cancer histology. Basic image analysis tasks include detection, grading and subtyping of tumour tissue in histology images; they are aimed at automating pathology workflows and consequently do not immediately translate into clinical decisions. Exceeding such basic approaches, DL has also been used for advanced image analysis tasks, which have the potential of directly affecting clinical decision-making processes. These advanced approaches include inference of molecular features, prediction of survival and end-to-end prediction of therapy response. Predictions made by such DL systems could simplify and enrich clinical decision-making, but require rigorous external validation in clinical settings.
DOI:doi:10.1038/s41416-020-01122-x
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/s41416-020-01122-x
 Volltext: https://www.nature.com/articles/s41416-020-01122-x
 DOI: https://doi.org/10.1038/s41416-020-01122-x
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Cancer imaging
 Computational science
 Targeted therapies
 Tumour biomarkers
K10plus-PPN:188516906X
Verknüpfungen:→ Zeitschrift

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