Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology: A three center study

• Published in: PloS one Vol. 19; no. 1; p. e0297146
• Main Authors: Wies, Christoph, Schneider, Lucas, Haggenmüller, Sarah, Bucher, Tabea-Clara, Hobelsberger, Sarah, Heppt, Markus V., Ferrara, Gerardo, Krieghoff-Henning, Eva I., Brinker, Titus J.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 19.01.2024; Public Library of Science (PLoS)
• Subjects: Analysis; Antigens; Benchmarks; Breast cancer; Care and treatment; Classification; Classifiers; Colorectal cancer; Datasets; Deep learning; Diagnosis; Digitization; Histology; Histology, Pathological; Immunohistochemistry; Investigations; Machine learning; Medical diagnosis; Melanoma; Methods; Skin cancer; Support systems; Italy; Germany; Taiwan
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0297146

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.