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| Verfasst von: | Marmé, Frederik [VerfasserIn]  |
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| | Krieghoff-Henning, Eva [VerfasserIn] |
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| | Gerber, Bernd [VerfasserIn] |
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| | Schmitt, Max [VerfasserIn] |
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| | Zahm, Dirk-Michael [VerfasserIn] |
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| | Bauerschlag, Dirk Olaf [VerfasserIn] |
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| | Forstbauer, Helmut [VerfasserIn] |
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| | Hildebrandt, Guido [VerfasserIn] |
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| | Ataseven, Beyhan [VerfasserIn] |
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| | Brodkorb, Tobias [VerfasserIn] |
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| | Denkert, Carsten Michael [VerfasserIn] |
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| | Stachs, Angrit [VerfasserIn] |
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| | Krug, David [VerfasserIn] |
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| | Heil, Jörg [VerfasserIn] |
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| | Golatta, Michael [VerfasserIn] |
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| | Kühn, Thorsten [VerfasserIn] |
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| | Nekljudova, Valentina [VerfasserIn] |
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| | Gaiser, Timo [VerfasserIn] |
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| | Schönmehl, Rebecca [VerfasserIn] |
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| | Brochhausen, Christoph [VerfasserIn] |
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| | Loibl, Sibylle [VerfasserIn] |
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| | Reimer, Toralf [VerfasserIn] |
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| | Brinker, Titus Josef [VerfasserIn] |
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| Titel: | Deep learning to predict breast cancer sentinel lymph node status on INSEMA histological images |
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| Verf.angabe: | Frederik Marmé, Eva Krieghoff-Henning, Bernd Gerber, Max Schmitt, Dirk-Michael Zahm, Dirk Bauerschlag, Helmut Forstbauer, Guido Hildebrandt, Beyhan Ataseven, Tobias Brodkorb, Carsten Denkert, Angrit Stachs, David Krug, Jörg Heil, Michael Golatta, Thorsten Kühn, Valentina Nekljudova, Timo Gaiser, Rebecca Schönmehl, Christoph Brochhausen, Sibylle Loibl, Toralf Reimer, Titus J. Brinker |
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| E-Jahr: | 2023 |
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| Jahr: | December 2023 |
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| Umfang: | 6 S. |
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| Illustrationen: | Diagramme |
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| Fussnoten: | Available online 18 October 2023, Version of Record 25 October 2023 ; Gesehen am 16.04.2024 |
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| Titel Quelle: | Enthalten in: European journal of cancer |
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| Ort Quelle: | Amsterdam [u.a.] : Elsevier, 1992 |
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| Jahr Quelle: | 2023 |
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| Band/Heft Quelle: | 195(2023), Artikel-ID 113390, Seite 1-6 |
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| ISSN Quelle: | 1879-0852 |
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| Abstract: | Background - Sentinel lymph node (SLN) status is a clinically important prognostic biomarker in breast cancer and is used to guide therapy, especially for hormone receptor-positive, HER2-negative cases. However, invasive lymph node staging is increasingly omitted before therapy, and studies such as the randomised Intergroup Sentinel Mamma (INSEMA) trial address the potential for further de-escalation of axillary surgery. Therefore, it would be helpful to accurately predict the pretherapeutic sentinel status using medical images. - Methods - Using a ResNet 50 architecture pretrained on ImageNet and a previously successful strategy, we trained deep learning (DL)-based image analysis algorithms to predict sentinel status on hematoxylin/eosin-stained images of predominantly luminal, primary breast tumours from the INSEMA trial and three additional, independent cohorts (The Cancer Genome Atlas (TCGA) and cohorts from the University hospitals of Mannheim and Regensburg), and compared their performance with that of a logistic regression using clinical data only. Performance on an INSEMA hold-out set was investigated in a blinded manner. - Results - None of the generated image analysis algorithms yielded significantly better than random areas under the receiver operating characteristic curves on the test sets, including the hold-out test set from INSEMA. In contrast, the logistic regression fitted on the Mannheim cohort retained a better than random performance on INSEMA and Regensburg. Including the image analysis model output in the logistic regression did not improve performance further on INSEMA. - Conclusions - Employing DL-based image analysis on histological slides, we could not predict SLN status for unseen cases in the INSEMA trial and other predominantly luminal cohorts. |
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| DOI: | doi:10.1016/j.ejca.2023.113390 |
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| 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.
kostenfrei: Volltext: https://doi.org/10.1016/j.ejca.2023.113390 |
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| | kostenfrei: Volltext: https://www.sciencedirect.com/science/article/pii/S0959804923006925 |
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| | DOI: https://doi.org/10.1016/j.ejca.2023.113390 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | Breast cancer |
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| | Deep learning |
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| | Digital biomarker |
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| | Lymph node status |
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| | Sentinel |
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| K10plus-PPN: | 188592609X |
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| Verknüpfungen: | → Zeitschrift |
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Deep learning to predict breast cancer sentinel lymph node status on INSEMA histological images / Marmé, Frederik [VerfasserIn]; December 2023 (Online-Ressource)
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