Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology: a three center study - Wies, Christoph; Schneider, Lucas; Haggenmüller, Sarah; Bucher, Tabea-Clara; Hobelsberger, Sarah; Heppt, Markus V.; Ferrara, Gerardo; Krieghoff-Henning, Eva I.; Brinker, Titus Josef (PLOS ONE, 2024)
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.
Gesehen am 23.09.2024