Evaluating Deep Learning-based Melanoma Classification using Immunohistochemistry and Routine Histology: A Three Center Study

TL;DR

This study evaluates deep learning models for melanoma classification using immunohistochemistry and routine histology, demonstrating comparable performance to traditional methods and potential for clinical assistance.

Contribution

It introduces a deep learning approach for IHC-stained slides and assesses combined staining data, a novel application in melanoma diagnosis.

Findings

DL models achieved AUROCs of 0.82 and 0.74 on OOD datasets for MelanA.

Combined MelanA and H&E classifiers improved AUROCs to 0.85 and 0.81.

DL-based systems perform comparably to traditional H&E classification in melanoma detection.

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.