  Although melanoma occurs more rarely than several other skin cancers,
patients' long term survival rate is extremely low if the diagnosis is missed.
Diagnosis is complicated by a high discordance rate among pathologists when
distinguishing between melanoma and benign melanocytic lesions. A tool that
provides potential concordance information to healthcare providers could help
inform diagnostic, prognostic, and therapeutic decision-making for challenging
melanoma cases. We present a melanoma concordance regression deep learning
model capable of predicting the concordance rate of invasive melanoma or
melanoma in-situ from digitized Whole Slide Images (WSIs). The salient features
corresponding to melanoma concordance were learned in a self-supervised manner
with the contrastive learning method, SimCLR. We trained a SimCLR feature
extractor with 83,356 WSI tiles randomly sampled from 10,895 specimens
originating from four distinct pathology labs. We trained a separate melanoma
concordance regression model on 990 specimens with available concordance ground
truth annotations from three pathology labs and tested the model on 211
specimens. We achieved a Root Mean Squared Error (RMSE) of 0.28 +/- 0.01 on the
test set. We also investigated the performance of using the predicted
concordance rate as a malignancy classifier, and achieved a precision and
recall of 0.85 +/- 0.05 and 0.61 +/- 0.06, respectively, on the test set. These
results are an important first step for building an artificial intelligence
(AI) system capable of predicting the results of consulting a panel of experts
and delivering a score based on the degree to which the experts would agree on
a particular diagnosis. Such a system could be used to suggest additional
testing or other action such as ordering additional stains or genetic tests.
