Artificial intelligence for automatic detection of basal cell carcinoma from frozen tissue tangential biopsies

• Published in: Clinical and experimental dermatology 2024-06, Vol.49 (7), p.719-721
• Main Authors: Murphree, Dennis H, Kim, Yong-Hun, Sidey, Kirk A, Comfere, Nneka I, Vidal, Nahid Y
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Biopsy - methods; Carcinoma, Basal Cell - diagnosis; Carcinoma, Basal Cell - pathology; Carcinoma, Basal Cell - surgery; Frozen Sections; Humans; Mohs Surgery; Sensitivity and Specificity; Skin Neoplasms - diagnosis; Skin Neoplasms - pathology
• ISSN: 0307-6938; 1365-2230; 1365-2230
• DOI: 10.1093/ced/llad209

Evaluation of basal cell carcinoma (BCC) involves tangential biopsies of a suspicious lesion that is sent for frozen sections and evaluated by a Mohs micrographic surgeon. Advances in artificial intelligence (AI) have made possible the development of sophisticated clinical decision support systems to provide real-time feedback to clinicians that could have a role in optimizing the diagnostic workup of BCC. There were 287 annotated whole-slide images of frozen sections from tangential biopsies, of which 121 contained BCC, that were used to train and test an AI pipeline to recognize BCC. Regions of interest were annotated by a senior dermatology resident, an experienced dermatopathologist and an experienced Mohs surgeon, with concordance of annotations noted on final review. Final performance metrics included a sensitivity and specificity of 0.73 and 0.88, respectively. Our results on a relatively small dataset suggest the feasibility of developing an AI system to aid in the workup and management of BCC.