ArXiv Preprint
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.
Sean Grullon, Vaughn Spurrier, Jiayi Zhao, Corey Chivers, Yang Jiang, Kiran Motaparthi, Michael Bonham, Julianna Ianni
2022-10-10
