Basal Cell Carcinoma of Skin
Conditions
Brief summary
Basal cell carcinomas (BCCs) are the most common human malignancy, affecting about 2 million Americans each year. Mohs micrographic surgery (MMS) removes tissue by sequential excision. Costs for MMS could be reduced if the number of necessary excision stages were decreased by a more accurate initial tumor margin assessment. The goal of this observational study is to learn if Optical Coherence Tomography (OCT) used in conjunction with artificial intelligence algorithms is accurate in the detection of superficial BCC margins prior to MMS. This study also aims to determine if AI-OCT guided margin delineation can reduce the number of stages in MMS. Researchers will first focus on validating AI-OCT as a method for accurately detecting BCCs. A follow-up study would then address the guided pre-surgical margin delineation.
Interventions
DIAGNOSTIC_TESTAI-OCT
Optical Coherence Tomography augmented by artificial intelligence software
Sponsors
Henry Ford Health System
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Male or Female, ages 18 or older * at least one biopsy proven superficial or nodular BCC * willingness to have photographs taken of the treatment area * ability to understand and willingness to sign a written informed consent document
Exclusion criteria
* infiltrative, micronodular, or morpheaform BCC * pregnant women * subjects not willing to have a biopsy taken from the treatment area * subjects with herpes simplex virus infection in the treatment area
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Validation of AI-OCT as an accurate method for detecting basal cell carcinomas | 2 years |
Outcome results
None listed