Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology: A three center study
Wies C, Schneider L, Haggenmüller S, Bucher TC, Hobelsberger S, Heppt M, Ferrara G, Krieghoff-Henning EI, Brinker TJ (2024)
Publication Type: Journal article
Publication year: 2024
Journal
Book Volume: 19
Pages Range: e0297146-
Journal Issue: 1
DOI: 10.1371/journal.pone.0297146
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.
Authors with CRIS profile
Involved external institutions
Deutsches Krebsforschungszentrum (DKFZ)
Germany (DE)
Universitätsklinikum Carl Gustav Carus Dresden
Germany (DE)
Istituto Nazionale Tumori IRCCS "Fondazione G. Pascale"
Italy (IT)
Germany (DE)
Universitätsklinikum Carl Gustav Carus Dresden
Germany (DE)
Istituto Nazionale Tumori IRCCS "Fondazione G. Pascale"
Italy (IT)
How to cite
APA:
Wies, C., Schneider, L., Haggenmüller, S., Bucher, T.C., Hobelsberger, S., Heppt, M.,... Brinker, T.J. (2024). Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology: A three center study. PLoS ONE, 19(1), e0297146-. https://dx.doi.org/10.1371/journal.pone.0297146
MLA:
Wies, Christoph, et al. "Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology: A three center study." PLoS ONE 19.1 (2024): e0297146-.
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