ArticleDermatology practical & conceptual2021
Variability in the Histopathological Diagnosis of Non-Melanocytic Lesions Excised to Exclude Melanoma.
Article in Dermatology practical & conceptual, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- VISGAB: Virtual staining-driven GAN benchmarking for optimizing skin tissue histology.Scientific reports · 2025Article
- Human Papillomavirus-Associated Giant Clear Cell Acanthoma and Squamous Cell Carcinoma: A Rare Case Report and Literature Review.Journal of clinical medicine · 2024Review
- Assessment of a Diagnostic Classification System for Management of Lesions to Exclude Melanoma.JAMA network open · 2021Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe differential diagnosis of lesions excised to exclude melanoma include a variety of benign and malignant melanocytic and non-melanocytic lesions.
objectivesWe examined the variability between pathologists in diagnosing non-melanocytic lesions.
methodsAs part of a larger study prospectively examining the diagnosis of lesions excised to exclude melanoma in 198 patients at a primary care skin cancer clinic in Newcastle, Australia, we compared diagnosis made by 5 experienced dermatopathologists, of 44 non-melanocytic lesions in 44 patients aged 22-90.
resultsForty-four lesions (out of 217 in total) were non-melanocytic. Among the 5 pathologists who examined each case there was marked variability in the terminology used to diagnose each case. The most common variability was found between seborrheic keratosis, large cell acanthoma, solar lentigo, and lichenoid keratosis. The diagnosis made by the majority of the pathologists was deemed to be the reference diagnosis. Versus majority diagnosis, 4% of benign lesions were considered malignant, and 7% of malignant diagnoses were considered as benign.
conclusionsThe different terminology adopted and lack of consensus in the diagnosis of these non-melanocytic lesions in this setting suggests that training AI systems using gold standards may be problematic. We propose a new management classification scheme called MOLEM (Management of Lesions Excised to exclude Melanoma) which expands the previously described MPATH-dx to include non-melanocytic lesions.
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