Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
455 participants
OBSERVATIONAL
2021-01-25
2024-01-31
Brief Summary
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Detailed Description
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Additionally, the overall psychological burden and worry of melanoma risk or disease, anxiety, depression will be compared in different groups of patients and psychological support need and real uptake of support and its predictors will be investigated in all participants.
To validate the MELVEC (Melanoma Detection in Switzerland with Vectra®) test procedure, an analysis of the measurement repeatability of computer-guided risk assessment scores for early melanoma detection will be performed. A potential benefit of this validation analysis is the optimization of study procedure for future follow-up visits and further enrolled patients in the MELVEC study. Additionally, results will shed light on the reliability of the convolutional neural networks (CNNs) investigated and help formulate recommendations for their current use. Furthermore, results will provide important data for the manufacturers regarding the systems' reliability in clinical application to help future improvement of the respective algorithms.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Interventions
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3D imaging Total Body Photography Vectra® WB360
3D Total Body Photography Vectra® WB360 (Canfield Scientific, Parsippany, New Jersey, USA) and its digital dermoscopic camera (VISIOMED® D200evo dermatoscope) and scoring of pigmented skin lesions. All participants of this study will undergo 3D TBP at baseline and the follow-up visits up to month 24.
2D imaging FotoFinder ATBM® Master imaging system
2D imaging with FotoFinder® Mole Analyzer and scoring of pigmented skin lesions. All participants of this study will undergo 2D imaging FotoFinder ATBM® Master imaging system at baseline and the follow-up visits up to month 24.
Smartphone application (SkinVision®)
Smartphone application for all dermatoscopically documented pigmented skin lesions in all study participations and record of risk assessment of the health application (low, medium or high risk) to compare the app's accuracy in risk assessment with the AI tools and the dermatologist. The SkinVision® smartphone app is CE certified.of skin lesions. All participants of this study will undergo Smartphone application (SkinVision®) at baseline and the follow-up visits up to month 12.
Standard-of-care clinical assessment of the skin
Clinical skin examination with dermatoscope by an experienced dermatologist and risk assessment of pigmented lesions (melanoma vs. naevus). All participants of this study will undergo Standard-of-care clinical assessment of the skin at baseline and the follow-up visits up to month 24.
Eligibility Criteria
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Inclusion Criteria
* Sufficient fluency in German language skills to complete all questionnaires of the study without external assistance
* High-risk criteria for melanoma. For "high risk" one of the following criteria needs to be fulfilled:
* At least one previous melanoma (including melanoma in situ)
* A diagnosis of ≥ 100 nevi
* A diagnosis of ≥ 5 atypical nevi
* A diagnosis of dysplastic nevus syndrome or known CDKN2A mutation
* A strong family history (≥ 1 first- and/or second-degree relatives)
Exclusion Criteria
* Fitzpatrick skin type V-VI.
* Acute psychiatric illness or acute crisis
18 Years
ALL
No
Sponsors
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University Hospital, Basel, Switzerland
OTHER
Responsible Party
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Principal Investigators
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Lara Valeska Maul, Dr. med.
Role: PRINCIPAL_INVESTIGATOR
Department of Dermatology, University Hospital Basel
Locations
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Department of Dermatology, University Hospital Basel
Basel, , Switzerland
Countries
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References
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Goessinger EV, Niederfeilner JC, Cerminara S, Maul JT, Kostner L, Kunz M, Huber S, Koral E, Habermacher L, Sabato G, Tadic A, Zimmermann C, Navarini A, Maul LV. Patient and dermatologists' perspectives on augmented intelligence for melanoma screening: A prospective study. J Eur Acad Dermatol Venereol. 2024 Dec;38(12):2240-2249. doi: 10.1111/jdv.19905. Epub 2024 Feb 27.
Goessinger EV, Cerminara SE, Mueller AM, Gottfrois P, Huber S, Amaral M, Wenz F, Kostner L, Weiss L, Kunz M, Maul JT, Wespi S, Broman E, Kaufmann S, Patpanathapillai V, Treyer I, Navarini AA, Maul LV. Consistency of convolutional neural networks in dermoscopic melanoma recognition: A prospective real-world study about the pitfalls of augmented intelligence. J Eur Acad Dermatol Venereol. 2024 May;38(5):945-953. doi: 10.1111/jdv.19777. Epub 2023 Dec 29.
Cerminara SE, Cheng P, Kostner L, Huber S, Kunz M, Maul JT, Bohm JS, Dettwiler CF, Geser A, Jakopovic C, Stoffel LM, Peter JK, Levesque M, Navarini AA, Maul LV. Diagnostic performance of augmented intelligence with 2D and 3D total body photography and convolutional neural networks in a high-risk population for melanoma under real-world conditions: A new era of skin cancer screening? Eur J Cancer. 2023 Sep;190:112954. doi: 10.1016/j.ejca.2023.112954. Epub 2023 Jun 24.
Other Identifiers
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2020-02482; sp20Maul
Identifier Type: -
Identifier Source: org_study_id
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