Abstract
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 allows pathology labs to sort and prioritize melanoma cases in their workflow could improve turnaround time by prioritizing challenging cases and routing them directly to the appropriate subspecialist. We present a pathology deep learning system (PDLS) that performs hierarchical classification of digitized whole slide image (WSI) specimens into six classes defined by their morphological characteristics, including classification of "Melanocytic Suspect" specimens likely representing melanoma or severe dysplastic nevi. We trained the system on 7,685 images from a single lab (the reference lab), including the the largest set of triple-concordant melanocytic specimens compiled to date, and tested the system on 5,099 images from two distinct validation labs. We achieved Area Underneath the ROC Curve (AUC) values of 0.93 classifying Melanocytic Suspect specimens on the reference lab, 0.95 on the first validation lab, and 0.82 on the second validation lab. We demonstrate that the PDLS is capable of automatically sorting and triaging skin specimens with high sensitivity to Melanocytic Suspect cases and that a pathologist would only need between 30% and 60% of the caseload to address all melanoma specimens.
Abstract (translated)
URL
https://arxiv.org/abs/2109.07554
