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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.
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| AI-OCT | Diagnostic Test | Optical Coherence Tomography augmented by artificial intelligence software |
| Measure | Description | Time Frame |
|---|---|---|
| Validation of AI-OCT as an accurate method for detecting basal cell carcinomas | 2 years |
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Inclusion Criteria:
Exclusion Criteria:
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Adult patients with biopsy-confirmed BCC who are scheduled to receive Mohs micrographic surgery
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| ID | Term |
|---|---|
| D002280 | Carcinoma, Basal Cell |
| ID | Term |
|---|---|
| D002277 | Carcinoma |
| D009375 | Neoplasms, Glandular and Epithelial |
| D009370 | Neoplasms by Histologic Type |
| D009369 | Neoplasms |
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| D018295 |
| Neoplasms, Basal Cell |