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| Verfasst von: | Albrecht, Thomas [VerfasserIn]  |
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| | Rossberg, Annik [VerfasserIn] |
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| | Albrecht, Jana Dorothea [VerfasserIn] |
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| | Nicolay, Jan Peter [VerfasserIn] |
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| | Straub, Beate Katharina [VerfasserIn] |
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| | Gerber, Tiemo S. [VerfasserIn] |
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| | Albrecht, Michael [VerfasserIn] |
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| | Brinkmann, Fritz [VerfasserIn] |
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| | Charbel, Alphonse [VerfasserIn] |
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| | Schwab, Constantin [VerfasserIn] |
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| | Schreck, Johannes [VerfasserIn] |
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| | Brobeil, Alexander [VerfasserIn] |
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| | Flechtenmacher, Christa [VerfasserIn] |
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| | Winterfeld, Moritz von [VerfasserIn] |
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| | Köhler, Bruno Christian [VerfasserIn] |
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| | Springfeld, Christoph [VerfasserIn] |
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| | Mehrabi, Arianeb [VerfasserIn] |
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| | Singer, Stephan [VerfasserIn] |
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| | Vogel, Monika Nadja [VerfasserIn] |
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| | Neumann, Olaf [VerfasserIn] |
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| | Stenzinger, Albrecht [VerfasserIn] |
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| | Schirmacher, Peter [VerfasserIn] |
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| | Weis, Cleo-Aron Thias [VerfasserIn] |
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| | Rössler, Stephanie [VerfasserIn] |
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| | Kather, Jakob Nikolas [VerfasserIn] |
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| | Goeppert, Benjamin [VerfasserIn] |
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| Titel: | Deep learning-enabled diagnosis of liver adenocarcinoma |
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| Titelzusatz: | original research |
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| Verf.angabe: | Thomas Albrecht, Annik Rossberg, Jana Dorothea Albrecht, Jan Peter Nicolay, Beate Katharina Straub, Tiemo Sven Gerber, Michael Albrecht, Fritz Brinkmann, Alphonse Charbel, Constantin Schwab, Johannes Schreck, Alexander Brobeil, Christa Flechtenmacher, Moritz von Winterfeld, Bruno Christian Köhler, Christoph Springfeld, Arianeb Mehrabi, Stephan Singer, Monika Nadja Vogel, Olaf Neumann, Albrecht Stenzinger, Peter Schirmacher, Cleo-Aron Weis, Stephanie Roessler, Jakob Nikolas Kather, and Benjamin Goeppert |
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| E-Jahr: | 2023 |
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| Jahr: | November 2023 |
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| Umfang: | 14 S. |
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| Illustrationen: | Illustrationen |
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| Fussnoten: | Online verfügbar: 9. August 2023, Artikelversion: 19. Oktober 2023 ; Gesehen am 16.05.2024 |
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| Titel Quelle: | Enthalten in: Gastroenterology |
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| Ort Quelle: | New York, NY : Elsevier, 1949 |
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| Jahr Quelle: | 2023 |
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| Band/Heft Quelle: | 165(2023), 5 vom: Nov., Seite 1262-1275 |
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| ISSN Quelle: | 1528-0012 |
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| Abstract: | Background & Aims - Diagnosis of adenocarcinoma in the liver is a frequent scenario in routine pathology and has a critical impact on clinical decision making. However, rendering a correct diagnosis can be challenging, and often requires the integration of clinical, radiologic, and immunohistochemical information. We present a deep learning model (HEPNET) to distinguish intrahepatic cholangiocarcinoma from colorectal liver metastasis, as the most frequent primary and secondary forms of liver adenocarcinoma, with clinical grade accuracy using H&E-stained whole-slide images. - Methods - HEPNET was trained on 714,589 image tiles from 456 patients who were randomly selected in a stratified manner from a pool of 571 patients who underwent surgical resection or biopsy at Heidelberg University Hospital. Model performance was evaluated on a hold-out internal test set comprising 115 patients and externally validated on 159 patients recruited at Mainz University Hospital. - Results - On the hold-out internal test set, HEPNET achieved an area under the receiver operating characteristic curve of 0.994 (95% CI, 0.989-1.000) and an accuracy of 96.522% (95% CI, 94.521%-98.694%) at the patient level. Validation on the external test set yielded an area under the receiver operating characteristic curve of 0.997 (95% CI, 0.995-1.000), corresponding to an accuracy of 98.113% (95% CI, 96.907%-100.000%). HEPNET surpassed the performance of 6 pathology experts with different levels of experience in a reader study of 50 patients (P = .0005), boosted the performance of resident pathologists to the level of senior pathologists, and reduced potential downstream analyses. - Conclusions - We provided a ready-to-use tool with clinical grade performance that may facilitate routine pathology by rendering a definitive diagnosis and guiding ancillary testing. The incorporation of HEPNET into pathology laboratories may optimize the diagnostic workflow, complemented by test-related labor and cost savings. |
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| DOI: | doi:10.1053/j.gastro.2023.07.026 |
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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.
Volltext: https://doi.org/10.1053/j.gastro.2023.07.026 |
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| | Volltext: https://www.sciencedirect.com/science/article/pii/S0016508523048837 |
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| | DOI: https://doi.org/10.1053/j.gastro.2023.07.026 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | Artificial Intelligence |
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| | Biliary Tract Cancer |
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| | Digital Pathology |
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| | Intestinal Cancer |
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| K10plus-PPN: | 1888948310 |
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| Verknüpfungen: | → Zeitschrift |
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Deep learning-enabled diagnosis of liver adenocarcinoma / Albrecht, Thomas [VerfasserIn]; November 2023 (Online-Ressource)
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