Artificial Intelligence Based Melanoma Early Diagnosis and Risk Prediction in Children, Adolescents and Young Adults

NCT06621810 | Recruiting

• 1 other identifier: MELCAYA-AI-MEL
• Study Type: observational
• Target: 3,000
• Locations: 3 countries
• Sites: 3

Brief Summary

The goal of this study is to develop supportive diagnostic artificial intelligence algorithms to distinguish melanoma from nevi or other benign pigmented skin lesions, especially in younger patients (below the age of 30). The main goals it aims to achieve are:

• development of an algorithm based on dermatoscopic images, targeting skin cancer screening in vulnerable populations
• development of another algorithm based on histological images, intended to be used by pathologists on lesions that are still suspicious of melanoma after dermatologic assessment
• implementation of explainability methods to enable the user to better comprehend the systems' decisions, avoid biases and increase trust in these applications There is no additional time commitment for the study participants for this study, as the data used in this project will be collected in routine clinical practice anyway.

Conditions

Melanoma (Skin Cancer); Artificial Intelligence (AI); Pediatric Cancer

Keywords

melanoma; AI; pediatric; image analysis; Children Adolescence Young Adults (CAYA)

Outcome Measures

Primary Outcomes (1)

• Area Under the Receiver Operator Curve (AUROC)

  The AUROC is used to measure and compare the diagnostic accuracy of different classifiers. Thereby, a higher value means better diagnostic performance, with an AUROC of 1 being a perfect score.

  First Assessment: Upon completion of the first training and testing cycle (approx. within 1.5 years from the start of the study). Reevaluations: at 6 and 12 months post-initial training for model improvement.

Secondary Outcomes (1)

• Balanced accuracy

  First Assessment: Upon completion of the first training and testing cycle (approx. within 1.5 years from the start of the study). Reevaluations: at 6 and 12 months post-initial training for model improvement.

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Mainly children (up to and including 15 years of age), adolescents (16-20) and young adults (from 21 to 30 years of age)

You may not qualify if:

• Patients without a melanoma or nevus diagnosis
• images with insufficient image quality

Sponsors & Collaborators

• German Cancer Research Center: lead
• Universität Tübingen: collaborator
• University of Florence: collaborator
• Fundacio Clinic Barcelona: collaborator
• Hospital Clinic of Barcelona: collaborator

Study Sites (3)

University of Tübingen

Tübingen, 72074, Germany

COMPLETED

University of Florence

Florence, 50121, Italy

COMPLETED

Hospital Clínic de Barcelona

Barcelona, 08036, Spain

RECRUITING

Related Links

• Homepage of the MELCAYA project

MeSH Terms

Conditions

Melanoma; Neoplasms

Condition Hierarchy (Ancestors)

Neuroendocrine Tumors; Neuroectodermal Tumors; Neoplasms, Germ Cell and Embryonal; Neoplasms by Histologic Type; Neoplasms, Nerve Tissue; Nevi and Melanomas; Skin Neoplasms; Neoplasms by Site; Skin Diseases; Skin and Connective Tissue Diseases

Study Officials

• Titus J Brinker, PD Dr. med

  German Cancer Research Center

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Titus J Brinker, PD Dr. med

CONTACT

+49 15175084347; titus.brinker@nct-heidelberg.de

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 24, 2024
• First Posted: October 1, 2024
• Study Start: December 1, 2022
• Primary Completion (Estimated): November 30, 2026
• Study Completion (Estimated): November 30, 2026
• Last Updated: October 1, 2024

Record last verified: 2024-09

Locations

DE (1); IT (1); ES (1)