Clinical trial · Observational
Melanoma Detection in Switzerland With VECTRA
Clinical Performance of the New Artificial-intelligence Powered 3D Total Body Photography System VECTRA® in Early Melanoma Detection and Its Impact on Patients' Burden of Disease: A Prospective Cohort Study in a Real-world Setting
NCT04605822CI-TRIAL-00075169MELVECcompletedClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
This study is to compare 2D- and 3D-imaging and routine clinical care in early melanoma detection in a prospective large-scale real-world data set.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Melanoma (Skin) | Melanoma | ONTOLOGY_EXACT | 0.85 |
Interventions
Interventions (4)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| 2D imaging FotoFinder ATBM® Master imaging system | Device | — | UNRESOLVED |
| 3D imaging Total Body Photography Vectra® WB360 | Device | — | UNRESOLVED |
| Smartphone application (SkinVision®) | Device | — | UNRESOLVED |
| Standard-of-care clinical assessment of the skin | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (5)
- measure
- Analyses of histopathology reports of all excised suspectable lesions
- timeFrame
- up to 24 months
- description
- The primary outcome, the sensitivity of human and artificial intelligence in detecting melanoma, will be measured at every study visit in case of a suspected melanoma by analysing histopathology reports of all excised suspectable lesions. The diagnosis of melanoma will be confirmed by histology. The biopsied pigmented skin lesions will be categorized as benign (melanocytic nevi / dysplastic nevi) or malignant (melanoma).
- measure
- Analyses of dermatologists' assessment of each pigmented skin lesion as benign (melanocytic nevi / dysplastic nevi) or malignant (melanoma) before and after (without and with knowledge of) computer-guided risk assessment scores
- timeFrame
- up to 24 months
- description
- Analyses of dermatologists' assessment of each pigmented skin lesion as benign (melanocytic nevi / dysplastic nevi) or malignant (melanoma) before and after computer-guided risk assessment scores by Vectra® WB360 and FotoFinder® Mole Analyzer and smartphone app.
- measure
- Analyses of 2D FotoFinder® Mole Analyzer scoring of pigmented skin lesions (0.0 - 1.0)
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * Written informed consent of the patient * 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: * Lack of informed consent for study participation. * Fitzpatrick skin type V-VI. * Acute psychiatric illness or acute crisis
References
Publications (3)
- DERIVEDGoessinger 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. PMID 38411348
- DERIVEDGoessinger 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. PMID 38158385
- DERIVEDCerminara 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. PMID 37453242