Clinical trial NCT05817279: AI-aided Optical Coherence Tomography for the Detection of Basal Cell Carcinoma
Official title: AI-aided Optical Coherence Tomography for the Detection of Basal Cell Carcinoma
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Basal Cell Carcinoma
Optical Coherence Tomography
Keywords: Basal cell carcinoma
BCC
Optical coherence tomography
OCT
Imaging
Artificial intelligence
Machine learning
Lead sponsor: Maastricht University Medical Center
Sponsor class: OTHER
First posted: 2023-04-18
Enrollment: 124
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Observational model: CASE_CONTROL
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Scans of patients with equivocal BCC lesions will be included. Patients previously underwent an OCT scan and punch biopsy for their lesion. Patients signed informed consent for the use of the OCT scans made and patient information for the sake of answering research questions regarding OCT.

Brief summary: Basal cell carcinoma (BCC) is the most common form of cancer among the Caucasian population. A BCC diagnosis is commonly establish by means of an invasive punch biopsy (golden standard). Optical coherence tomography (OCT) is a safe non-invasive diagnostic modality which may replace biopsy if an OCT assessor is able to establish a high confidence BCC diagnosis. Hence, for clinical implementation of OCT, diagnostic certainty should be as high as possible. Artificial intelligence in the form of a clinical decision support system (CDSS) may improve the diagnostic certainty of newly trained OCT assessors by highlighting suspicious areas on OCT scans and by providing diagnostic suggestions (classification). This study will evaluate the effect of a CDSS on the diagnostic certainty and accuracy of OCT assessors.

Detailed description: In this diagnostic case control design, OCT assessors will retrospectively evaluate OCT scans of equivocal BCC lesions twice (once with, and once without the help of the CDSS). A total of 124 scans (62 BCC/62 non-BCC) will be included in the study. Cases will be shuffled to prevent recall bias. AI-aided OCT scans and unaided OCT scans will be presented in alternating order. The assessors will express their certainty level on a 5-point confidence scale. The diagnostic certainty and diagnostic accuracy of OCT assessment with CDSS and without CDSS will be compared.

Research questions:

1. Does AI-aided OCT assessment result in an increase in high-confidence diagnoses compared to unaided OCT assessment?
2. Does AI-aided OCT assessment result in a significant increase in sensitivity for BCC detection without compromising specificity compared to unaided OCT assessment?
3. Does AI-aided OCT assessment result in more accurate BCC subtyping compared to unaided OCT assessment (explorative)

Eligibility criteria: Inclusion Criteria:

* Patients (18+ years)
* Patient underwent OCT scan and punch biopsy for an equivocal BCC lesion

Exclusion Criteria:

- Patient unable to sign informed consent

Primary outcomes:
measure: Proportion of high-confidence diagnoses
timeFrame: 31-12-2023
Secondary outcomes:
measure: Diagnostic accuracy of high-confidence diagnoses
timeFrame: 31-12-2023
measure: Diagnostic parameters for BCC subtyping
timeFrame: 31-12-2023
Other outcomes:
Locations:
city: Maastricht
country: Netherlands
facility: Maastricht University Medical Center+
state: Limbrug
Study officials:
References: