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| Verfasst von: | Wies, Christoph [VerfasserIn]  |
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| | Schneider, Lucas [VerfasserIn] |
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| | Haggenmüller, Sarah [VerfasserIn] |
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| | Bucher, Tabea-Clara [VerfasserIn] |
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| | Hobelsberger, Sarah [VerfasserIn] |
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| | Heppt, Markus V. [VerfasserIn] |
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| | Ferrara, Gerardo [VerfasserIn] |
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| | Krieghoff-Henning, Eva I. [VerfasserIn] |
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| | Brinker, Titus Josef [VerfasserIn] |
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| Titel: | Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology |
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| Titelzusatz: | a three center study |
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| Verf.angabe: | Christoph Wies, Lucas Schneider, Sarah Haggenmüller, Tabea-Clara Bucher, Sarah Hobelsberger, Markus V. Heppt, Gerardo Ferrara, Eva I. Krieghoff-Henning, Titus J. Brinker |
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| E-Jahr: | 2024 |
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| Jahr: | January 19, 2024 |
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| Umfang: | 13 S. |
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| Illustrationen: | Illustrationen |
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| Fussnoten: | Gesehen am 23.09.2024 |
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| Titel Quelle: | Enthalten in: PLOS ONE |
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| Ort Quelle: | San Francisco, California, US : PLOS, 2006 |
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| Jahr Quelle: | 2024 |
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| Band/Heft Quelle: | 19(2024), 1, Artikel-ID e0297146, Seite 1-13 |
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| ISSN Quelle: | 1932-6203 |
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| Abstract: | Pathologists routinely use immunohistochemical (IHC)-stained tissue slides against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to improve their accuracy in diagnosing melanomas. The use of diagnostic Deep Learning (DL)-based support systems for automated examination of tissue morphology and cellular composition has been well studied in standard H&E-stained tissue slides. In contrast, there are few studies that analyze IHC slides using DL. Therefore, we investigated the separate and joint performance of ResNets trained on MelanA and corresponding H&E-stained slides. The MelanA classifier achieved an area under receiver operating characteristics curve (AUROC) of 0.82 and 0.74 on out of distribution (OOD)-datasets, similar to the H&E-based benchmark classification of 0.81 and 0.75, respectively. A combined classifier using MelanA and H&E achieved AUROCs of 0.85 and 0.81 on the OOD datasets. DL MelanA-based assistance systems show the same performance as the benchmark H&E classification and may be improved by multi stain classification to assist pathologists in their clinical routine. |
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| DOI: | doi:10.1371/journal.pone.0297146 |
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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/10.1371/journal.pone.0297146 |
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| | Volltext: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0297146 |
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| | DOI: https://doi.org/10.1371/journal.pone.0297146 |
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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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| | Cancer detection and diagnosis |
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| | Colorectal cancer |
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| | Cutaneous melanoma |
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| | Immunohistochemistry techniques |
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| | Lesions |
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| | Melanoma |
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| | Skin tumors |
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| K10plus-PPN: | 1903096111 |
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| Verknüpfungen: | → Zeitschrift |
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Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology / Wies, Christoph [VerfasserIn]; January 19, 2024 (Online-Ressource)
