A deep learning algorithm to detect cutaneous squamous cell carcinoma on frozen sections in Mohs micrographic surgery: A retrospective assessment.
Mohs micrographic surgery
artificial intelligence
clinical research
general dermatology
medical dermatology
oncology
Journal
Experimental dermatology
ISSN: 1600-0625
Titre abrégé: Exp Dermatol
Pays: Denmark
ID NLM: 9301549
Informations de publication
Date de publication:
21 Oct 2023
21 Oct 2023
Historique:
revised:
13
09
2023
received:
22
07
2023
accepted:
30
09
2023
medline:
21
10
2023
pubmed:
21
10
2023
entrez:
21
10
2023
Statut:
aheadofprint
Résumé
Intraoperative margin analysis is crucial for the successful removal of cutaneous squamous cell carcinomas (cSCC). Artificial intelligence technologies (AI) have previously demonstrated potential for facilitating rapid and complete tumour removal using intraoperative margin assessment for basal cell carcinoma. However, the varied morphologies of cSCC present challenges for AI margin assessment. The aim of this study was to develop and evaluate the accuracy of an AI algorithm for real-time histologic margin analysis of cSCC. To do this, a retrospective cohort study was conducted using frozen cSCC section slides. These slides were scanned and annotated, delineating benign tissue structures, inflammation and tumour to develop an AI algorithm for real-time margin analysis. A convolutional neural network workflow was used to extract histomorphological features predictive of cSCC. This algorithm demonstrated proof of concept for identifying cSCC with high accuracy, highlighting the potential for integration of AI into the surgical workflow. Incorporation of AI algorithms may improve efficiency and completeness of real-time margin assessment for cSCC removal, particularly in cases of moderately and poorly differentiated tumours/neoplasms. Further algorithmic improvement incorporating surrounding tissue context is necessary to remain sensitive to the unique epidermal landscape of well-differentiated tumours, and to map tumours to their original anatomical position/orientation.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NIH HHS
ID : P20GM104416
Pays : United States
Organisme : NIH HHS
ID : P20GM130454
Pays : United States
Commentaires et corrections
Type : UpdateOf
Informations de copyright
© 2023 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
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