OCT-BCC: Optical-Coherence Tomography for the Non-invasive Diagnosis and Subtyping of Basal Cell Carcinoma
Study Details
Study Description
Brief Summary
Rationale:
To date, the diagnosis and subtyping of basal cell carcinoma (BCC) is verified with histopathology which requires a biopsy. Because this technique is invasive, new non-invasive strategies have been developed, including Optical Coherence Tomography (OCT). This innovative technique enables microscopically detailed examination of lesions, which is useful for diagnosing and identification of various subtypes of BCC. The diagnostic value of the VIVOSIGHT OCT in daily clinical practice, has not been established to date.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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Detailed Description
Objective:
The aim of the study is to investigate the diagnostic value and usability of OCT in the diagnosis and subtyping of clinically suspect BCC.
Study design:
In this prospective observational trial, the VIVOSIGHT OCT device will be used on all patients attending the policlinic Dermatology of the MUMC and will undergo a skin biopsy. Information collected from OCT images will be compared with the clinical diagnosis by the specialist, including dermatoscopy, and the gold standard consisting of the histopathological diagnosis obtained from biopsy.
Study population:
All patients attending the policlinic Dermatology of the MUMC that will undergo a skin biopsy.
Intervention:
In this study, patients will be asked for informed consent to participate in this study before the planned biopsy is performed. The consent includes the undergoing of the imaging and extracting the pathology report from the patient's file. Imaging of the lesion will be performed subsequently, which is non-invasive and requires only several minutes of time. After the imaging, the patient is treated conform regular care: the following biopsy will be send for histopathologic examination by the pathologist, and the patient will hear the outcome of this investigation via his or her own physician. The histopathologic diagnosis and subtype will be obtained from the pathology report to compare with the diagnosis and subtype based on clinical diagnosis and OCT imaging.
Main study parameters/endpoints:
The main study parameters comprise the diagnostic value of OCT imaging for BCC, defined as the sensitivity, specificity, positive- and negative predictive value, compared with the clinical diagnosis and the gold standard of histopathological diagnosis. Secondary outcome is the value of OCT for subtyping BCC. Retrospectively, the necessity of a biopsy to confirm the diagnosis will be evaluated.
Nature and extent of the burden and risks associated with participation, benefit and group relatedness:
This research is conducted during regular patient care. Patients that are planned to have a skin biopsy for histopathological study will be asked to participate and provide informed consent. When a biopsy is planned, the OCT imaging will be performed while the patients are waiting for the biopsy to be prepared, the OCT imaging process is performed by the investigator. The outcome of the planned biopsy will be used as the gold standard for comparison with OCT. The results of this biopsy will be extracted from the pathology report in the electronic patient history file. Possible risks for biopsies are not influenced by the OCT imaging apparatus and include: post-operative bleeding, allergic reactions, wound infection and hematoma formation. With OCT imaging valuable information can be obtained without influencing the regular care procedure. This is benificial because in the future the non-invasive OCT might replace invasive skin biopsies.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Prospective data collection, retrospective OCT image evaluation From February 2017-June 2017 patients were included prospectively. OCT images were evaluated retrospectively in conjunction with clinical images. A deep learning algorithm is developed with use of this dataset including 676 OCT images. |
Device: Optical Coherence Tomography
OCT is an imaging technique, which is able to produce real-time, in vivo, cross-sectional images of lesions with a depth of 1,5-2 mm. OCT imaging is based on light-interferometry, calculating the interference of an optical beam reflected by the tissue with a reference. [2] In such ways, microscopic details of lesions and tissues can be visualized. This information could be used to identify a lesion as BCC, and further specify the subtype. Therefore, the use of the OCT can reduce the number of biopsies and the accompanying morbidity.
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| Prospective data collection and OCT image evaluation From January 2021-April 2021 patients were included prospectively. OCT images were evaluated prospectively in a clinical setting. The deep learning algorithm will be prospectively validated with use of this dataset including 287 OCT images. |
Device: Optical Coherence Tomography
OCT is an imaging technique, which is able to produce real-time, in vivo, cross-sectional images of lesions with a depth of 1,5-2 mm. OCT imaging is based on light-interferometry, calculating the interference of an optical beam reflected by the tissue with a reference. [2] In such ways, microscopic details of lesions and tissues can be visualized. This information could be used to identify a lesion as BCC, and further specify the subtype. Therefore, the use of the OCT can reduce the number of biopsies and the accompanying morbidity.
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Outcome Measures
Primary Outcome Measures
- Diagnostic accuracy of OCT in diagnosis and subtyping of BCC [February 2017-April 2021]
The main study parameter is the diagnostic value of OCT in diagnosis BCC defined as accuracy, sensitivity, specificity and negative- and positive diagnostic values. An increase of at least 10 percent in specificity and an equal sensitivity of OCT-based diagnosis is expected, compared with the clinical diagnosis and the golden-standard histopathology.
Secondary Outcome Measures
- Developing a deep learning algorithm for automated detection of basal cell carcinoma (BCC) and recognizing three different BCC subtypes in OCT images. [June 2020-August 2021]
The ability of a deep learning algorithm to classify BCC from other skin lesions within OCT images
Eligibility Criteria
Criteria
Inclusion Criteria:
- Adult patients (18 years or older) receiving a skin biopsy of a lesion clinically suspected for a non-melanoma skin cancer or premalignancy
Exclusion Criteria:
- Patients who were incompetent to sign informed consent were excluded
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Maastricht University Medical Centre+ | Maastricht | Limburg | Netherlands | 6229HX |
Sponsors and Collaborators
- Maastricht University Medical Center
- Maastricht University
Investigators
- Principal Investigator: Klara Mosterd, MD, PhD, Maastricht University Medical Center
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- DERMA_OCT_BCC01