Melanoma (Skin Cancer)AI-MELNCT06621810
Artificial Intelligence Based Melanoma Early Diagnosis and Risk Prediction in Children, Adolescents and Young Adults
An observational study of Melanoma (Skin Cancer) and Artificial Intelligence (AI), run by German Cancer Research Center at 3 sites in 3 countries.
Trial 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). Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Melanoma (Skin Cancer), Artificial Intelligence (AI), Pediatric Cancer
- How many people
- 3,000 participants
- Where
- 3 sitesin 3 countries
- Study dates
- 1 Dec 2022to 30 Nov 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
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)
Who may be able to join
- -
Who cannot join
- Patients without a melanoma or nevus diagnosis
- images with insufficient image quality
Trial locations and contacts
3 sites in 3 countries. Contact the study team or a site near you to ask about taking part.
3 sites in 3 countries
Spain1 site, 1 recruiting
- Recruiting
Hospital Clínic de Barcelona
Barcelona, 08036
Susana Puig, spuig@clinic.cat
About this study
Official title: AI-MEL: Image Analysis and Machine Learning for Early Diagnosis and Risk Prediction in Children, Adolescents and Young Adults
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.
- Keywords
- melanoma, AI, pediatric, image analysis, Children Adolescence Young Adults (CAYA)
- Medical subject headings
- Melanoma, Neoplasms
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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
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.
Measured: 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.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Oct 2024
- Study start1 Dec 2022
- Primary completion30 Nov 2026 (estimated)
- Study completion30 Nov 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Melanoma (Skin Cancer)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Other
- Enrolment
- 3,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- German Cancer Research Center
- Sponsor type
- University, hospital or other
- Collaborators
- Universität Tübingen, University of Florence, Fundacio Clinic Barcelona, Hospital Clinic of Barcelona
- Responsible party
- Sponsor
- Investigators
- Titus J Brinker, PD Dr. med, Principal investigator, German Cancer Research Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06621810
- Sponsor's protocol ID
- MELCAYA-AI-MEL
- Acronym
- AI-MEL
- Status last verified
- Sep 2024
- First submitted
- 24 Sep 2024
- Last updated
- 1 Oct 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 1 Oct 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-MEL: Image Analysis and Machine Learning for Early Diagnosis and Risk Prediction in Children, Adolescents and Young Adults
- ClinicalTrials.gov ID
- NCT06621810
- Acronym / sponsor ID
- AI-MEL / MELCAYA-AI-MEL
- Keywords
- melanoma, AI, pediatric, image analysis, Children Adolescence Young Adults (CAYA)
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 3,000 participants (estimated)
- Lead sponsor
- German Cancer Research CenterUniversity, hospital or other
- Collaborators
- Universität Tübingen, University of Florence, Fundacio Clinic Barcelona, Hospital Clinic of Barcelona
- Study start
- 1 Dec 2022 (actual)
- Primary completion
- 30 Nov 2026 (estimated)
- Study completion
- 30 Nov 2026 (estimated)
- First posted
- 1 Oct 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case only
- Time perspective
- Other
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- 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)
Key inclusion criteria (1 listed)
- -
Key exclusion criteria (2 listed)
- Patients without a melanoma or nevus diagnosis
- images with insufficient image quality
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Area Under the Receiver Operator Curve (AUROC)
Time frame: 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.
1 secondary outcome
Balanced accuracy
Time frame: 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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 Oct 2024.
- Homepage of the MELCAYA projectLink provided by the sponsor.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Titus J Brinker, PD Dr. med principal investigator
German Cancer Research Center
3 sites in 3 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Melanoma (Skin Cancer) and Artificial Intelligence (AI), run by German Cancer Research Center at 3 sites in 3 countries.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 1 Oct 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in Germany, Italy, Spain. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Titus J Brinker, PD Dr. med (+49 15175084347, titus.brinker@nct-heidelberg.de), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area Under the Receiver Operator Curve (AUROC) (time frame: 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.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 1 Oct 2024.