Machine-learning convergent melanocytic morphology despite noisy archival slides

Abstract Melanocytic atypia, ranging from benign to malignant, often leads to diagnostic discordance, complicating its prediction by machine learning models. To overcome this, we paired H&E-stained histology images with contiguous or serial sections immunohistochemically (IHC) stained for melanocytic cells via antibodies for MelanA, MelPro, or SOX10. We developed a deep-learning pipeline to identify melanocytic atypia by digitizing a real-world archival dataset of 122 paired whole slide images from 61 confirmed melanoma in situ (MIS) cases at two institutions. Only 37.7% of the cases contained tissue pairs that matched well enough for deep learning. Nonetheless, the MelanA+MelPro models achieved an average area under the receiver-operating characteristic (AUROC) of 0.948 and an average area under the precision-recall curve (AUPRC) of 0.611, while the SOX10 models had an average of 0.867 AUROC and 0.433 AUPRC. Despite learning from biologically different IHC stains, the convolutional neural network (CNN) models independently exhibited an intuitive convergent rationale by explainable AI saliency calculations. Different antibodies, with nuclear versus cytoplasmic staining, provided complementary yet consistent information, which the CNNs integrated effectively. The resulting multi-antibody virtual stains identified morphologic cytologic and small-scale architectural features directly from H&E-stained histology images, which can assist pathologists in assessing cutaneous MIS.