Deep learning approach to predict sentinel lymph node status directly from routine histology of primary melanoma tumours

Article

Brinker, Titus J. , Kiehl, Lennard, Schmitt, Max, Jutzi, Tanja B., Krieghoff-Henning, Eva I., Krahl, Dieter, Kutzner, Heinz , Gholam, Patrick, Haferkamp, Sebastian , Klode, Joachim, Schadendorf, Dirk, Hekler, Achim, Fröhling, Stefan, Kather, Jakob N. , Haggenmüller, Sarah, von Kalle, Christof, Heppt, Markus, Hilke, Franz , Ghoreschi, Kamran , Tiemann, Markus, Wehkamp, Ulrike, Hauschild, Axel, Weichenthal, Michael and Utikal, Jochen S. (2021) Deep learning approach to predict sentinel lymph node status directly from routine histology of primary melanoma tumours. European Journal of Cancer 154, pp. 227-234.

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Involved Institutions

• Medicine > Lehrstuhl für Dermatologie und Venerologie

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Details

Item type	Article
Journal or Publication Title	European Journal of Cancer
Publisher	Elsevier
Place of Publication	OXFORD
Volume	154
Page Range	pp. 227-234
Date	2021
Date of publication	29 Feb 2024 12:35
Institutions	Medicine > Lehrstuhl für Dermatologie und Venerologie
Identification Number	Value Type 10.1016/j.ejca.2021.05.026 DOI
Keywords	CLASSIFICATION; RISK; STRATIFICATION; DERMATOLOGISTS; SUPERIOR; Melanoma; Skin cancer; Artificial intelligence; Neural network model; Lymph node biopsy; Sentinel; Histology; Machine learning; Biomarkers; Pathology
Dewey Decimal Classification	600 Technology > 610 Medical sciences Medicine
Status	Published
Refereed	Yes, this version has been refereed
Created at the University of Regensburg	Yes
Item ID	56647

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