Melanoma Detection in Switzerland With VECTRA (MELVEC)

University Hospital Basel

Status: Completed

Conditions: Melanoma (Skin)

Treatments: Device: 2D imaging FotoFinder ATBM® Master imaging system; Other: Standard-of-care clinical assessment of the skin; Device: Smartphone application (SkinVision®); Device: 3D imaging Total Body Photography Vectra® WB360

Study type: Observational

Funder types: Other

Identifiers

NCT04605822

2020-02482 sp20Maul;

Details and patient eligibility

About

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.

Full description

This study is to compare the accuracy of combining human and artificial intelligence with its independent application in early melanoma detection. The Artificial Intelligence (AI)-powered 3D Total Body Photography (TBP) Vectra® WB360 system's utility and clinical performance in detecting melanoma in the real-world setting will be compared to the gold standard with clinical assessments by experienced dermatologists, to currently widespread used 2D imaging tools (FotoFinder ATBM® Master) and to the Smartphone-based algorithm application (e.g. SkinVision®). Here included are specific questions regarding the patients' subjective experience, acceptance and evaluation of modern technological examination.

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.

Enrollment: 455 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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