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

Recruiting

• Conditions: Melanoma (Skin Cancer); Artificial Intelligence (AI); Pediatric Cancer

• Registration Number: NCT06621810
• Lead Sponsor: German Cancer Research Center

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2022-12-01
• Status Verified: 2024-09
• Study First Submitted: 2024-09-24
• Study First Submitted QC: 2024-09-27
• Last Update Submitted: 2024-09-27
• Study First Posted: 2024-10-01
• Last Update Posted: 2024-10-01
• Primary Completion: 2026-11-30
• Study Completion: 2026-11-30

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 3000

Inclusion Criteria

Exclusion Criteria

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Area Under the Receiver Operator Curve (AUROC)	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.	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.

Secondary Outcome Measures

Name	Time	Method
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.	The balanced accuracy is used to measure and compare the diagnostic accuracy between classifier and physician. Thereby, a higher value means better diagnostic performance, with a balanced accuracy of 1 signifying perfect diagnostic capabilities.

Trial Locations

Locations (3)

University of Tübingen; 🇩🇪 Tübingen, Germany

University of Florence; 🇮🇹 Florence, Italy

Hospital Clínic de Barcelona; 🇪🇸 Barcelona, Spain