Intraoperative margin assessment for basal cell carcinoma with deep learning and histologic tumor mapping to surgical site
Open accessAbstract
Successful treatment of solid cancers relies on complete surgical excision of the tumor either for definitive treatment or before adjuvant therapy. Intraoperative and postoperative radial sectioning, the most common form of margin assessment, can lead to incomplete excision and increase the risk of recurrence and repeat procedures. Mohs Micrographic Surgery is associated with complete removal of basal cell and squamous cell carcinoma through real-time margin assessment of 100% of the peripheral and deep margins. Real-time assessment in many tumor types is constrained by tissue size, complexity, and specimen processing / assessment time during general anesthesia. We developed an artificial intelligence platform to reduce the tissue preprocessing and histological assessment time through automated grossing recommendations, mapping and orientation of tumor to the surgical specimen. Using basal cell carcinoma as a model system, results demonstrate that this approach can address surgical laboratory efficiency bottlenecks for rapid and complete intraoperative margin assessment.
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The integrated ArcticAI approach improves the speed and completeness of intraoperative margin evaluations for BCC excision.
“This integrated approach improves the speed and completeness of intraoperative margin evaluations for BCC excision.”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [24]
The Results (‘Margin assessment speed’; page unavailable) report 78 seconds per case, and the Discussion compares this favorably with previously reported processing times. The Results (‘Histological assessment results’; page unavailable) show that ArcticAI detects holes and tears in tissue sections, but the Discussion describes improved completeness of margin review as an expected benefit of integrating that algorithm, not a demonstrated improvement. (LLM, conf=0.84)
Experimental — accuracy not yet measuredArcticAI, an artificial intelligence platform introduced in a 2024 study, is designed to enhance intraoperative margin assessment during Mohs micrographic surgery for BCC.
“a 2024 study introduced an artificial intelligence platform, ArcticAI, which is designed to enhance intraoperative margin assessment during Mohs micrographic surgery for BCC [24].”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [24]
ArcticAI automates tissue grossing recommendations, accurately identifies tumor presence on whole slide images, and maps these findings back to the surgical site.
“It automates tissue grossing recommendations, accurately identifies tumor presence on whole slide images, and maps these findings back to the surgical site.”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [24]
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Current Issues in the Performance of Mohs Micrographic Surgery
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Burnout and resiliency in Mohs surgeons: A survey study
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Pathogenesis of Plantar Epidermal Cyst: Three-Dimensional Reconstruction Analysis
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A deep‐learning algorithm to localize basal cell carcinoma foci on Mohs surgery frozen sections
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Flagged first, then by citations
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- Authors
- Levy, J. J., Davis, M. J., Chacko, R. S., Davis, M. J., Fu, L. J., Goel, T., Pamal, A., Nafi, I., Angirekula, A., Suvarna, A., Vempati, R., Christensen, B. C., Hayden, M. S., Vaickus, L. J., & LeBoeuf, M. R.
- Affiliations
- Dartmouth College (US)
- Massachusetts Institute of Technology (US)
- University of Virginia (US)
- Stanford University (US)
- University of Illinois Urbana-Champaign (US)
- Dartmouth–Hitchcock Medical Center (US)
- Corresponding author
- Levy, Joshua J
- ORCID
- Venue
- npj Precision Oncology
- Published
- 2024-01-03
- Volume
- 8
- Issue
- 1
- Pages
- 2-2
- ISSN
- 2397-768X (ISSN-L)
- PMC
- PMC10764333
- OpenAlex
- W4390542364
- Cited by
- 20
- References
- 100
- Citations by year
- 2026: 7 · 2025: 7 · 2024: 6
- FWCI
- 2.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Precision Oncology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Department of Health & Human Services | National Institutes of Health (P20GM104416, P20GM130454)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Epidemiology › Nonmelanoma Skin Cancer Studies
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- basal cell carcinoma, intraoperative margin assessment, Mohs micrographic surgery, deep learning, skin cancer
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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