Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology: a three center study
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 tissu...
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| Hauptverfasser: | , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
January 19, 2024
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| In: |
PLOS ONE
Year: 2024, Jahrgang: 19, Heft: 1, Pages: 1-13 |
| ISSN: | 1932-6203 |
| DOI: | 10.1371/journal.pone.0297146 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1371/journal.pone.0297146 Verlag, lizenzpflichtig, Volltext: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0297146 
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| Verfasserangaben: | Christoph Wies, Lucas Schneider, Sarah Haggenmüller, Tabea-Clara Bucher, Sarah Hobelsberger, Markus V. Heppt, Gerardo Ferrara, Eva I. Krieghoff-Henning, Titus J. Brinker |
MARC
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| 245 | 1 | 0 | |a Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology |b a three center study |c 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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| 520 | |a 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. | ||
| 650 | 4 | |a Breast cancer | |
| 650 | 4 | |a Cancer detection and diagnosis | |
| 650 | 4 | |a Colorectal cancer | |
| 650 | 4 | |a Cutaneous melanoma | |
| 650 | 4 | |a Immunohistochemistry techniques | |
| 650 | 4 | |a Lesions | |
| 650 | 4 | |a Melanoma | |
| 650 | 4 | |a Skin tumors | |
| 700 | 1 | |a Schneider, Lucas |e VerfasserIn |4 aut | |
| 700 | 1 | |a Haggenmüller, Sarah |e VerfasserIn |4 aut | |
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| 700 | 1 | |a Hobelsberger, Sarah |e VerfasserIn |4 aut | |
| 700 | 1 | |a Heppt, Markus V. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Ferrara, Gerardo |e VerfasserIn |4 aut | |
| 700 | 1 | |a Krieghoff-Henning, Eva I. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Brinker, Titus Josef |d 1990- |e VerfasserIn |0 (DE-588)1156309395 |0 (DE-627)1018860487 |0 (DE-576)502097434 |4 aut | |
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