Melanoma (Skin Cancer)Precis-mel 1NCT06608420
Precision Medicine for L/GCMN and Melanoma 1
An observational study of Melanoma (Skin Cancer) and Nevi and Melanomas, run by Fundacion Clinic per a la Recerca Biomédica at 1 site in Spain.
Trial summary
The primary objective of this study is to create a highly multidimensional and multicentric database for melanoma that encompasses cohorts of children, adolescent and young adults. This database will be used to perform survival analysis and evaluate sentinel lymph node (SLNB) positivity in CAYA. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Melanoma (Skin Cancer), Nevi and Melanomas
- What is being tested?
- Gradient Boosting Survival Analysis (GBSA), and Concordance index
- How many people
- 6,000 participants
- Where
- 1 siteSpain
- Study dates
- 1 Mar 2024to 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
- No healthy volunteers
- Child, Adult, Older adult
Study population: Review and/or analysis of pre-existing medical records, biological samples and data collected from patients that have been visited at our hospital.
Who may be able to join
- Melanoma patients of any age with histopathological confirmed melanoma
Who cannot join
- Not having a melanoma diagnosis
- Not having signed the informed consent
- Records prior to the year 2012 (as data might not accurately reflect current practices and treatment outcomes)
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Adrián López Canosa, PhD contact
Spain1 site, 1 recruiting
- Recruiting
Hospital Clínic de Barcelona (Dermatology service)
Barcelona, Catalonia, 08036
Susana Puig Sardà, PhD, MD, +34932275400, spuig@clinic.cat
About this study
Official title: Precision Medicine for L/GCMN and Melanoma 1 (Precis-mel 1)
The primary objective of this study is to create a highly multidimensional and multicentric database for melanoma that encompasses cohorts of children, adolescent and young adults. This database will be used to perform survival analysis and evaluate sentinel lymph node (SLNB) positivity in CAYA. The secondary objectives to be met are the following:
- Adaptation and optimization of algorithms: work on optimizing existing precision medicine algorithms, which are currently being used in adult patient care, for their application within pediatric and young adult populations.
- Implementation of transfer learning: given the limitations associated with pediatric and young adult data, the investigators intend to utilize transfer learning techniques. The study will employ a sequential waterfall methodology, whereby machine learning models trained on adult patient data will be fine-tuned using the more limited data from younger cohorts.
- Integration of expert medical opinion: to integrate physician's scientific domain knowledge into the decision support system. This will be facilitated through the comprehensive examination of existing literature, as well as the evaluation of variable risk contributions within each patient group.
- AI-based prognostic models: to develop artificial intelligence-based models for the quantitative prognosis of melanoma across the three age groups: adults, young adults, and children.
Read the detailed description
Precis-Mel 1 is a unicentric observational study using retrospectively collected data. The proposed procedure is to start using data including demographic and family data, genetic data, medical procedures and cancer treatment, cutaneous biopsy, etc. to build a multidimensional dataset and apply AI algorithms that can produce survival curves and sentinel lymph node (SLNB) positivity in CAYA. The approach to be used is presented in the following sub-sections:
- Data engineering: the multidimensional dataset is meticulously integrated via DBT and SQL queries on a PostgreSQL database. This results in a model-ready comprehensive table, maintaining the crucial temporal dimension of patient histories. Identifiers are assigned to maintain the integrity of the data trail and the connection between various patient events such as metastasis and death. Python-based transformations ensure that sequential patient events are contextually enriched by preceding occurrences. Operations include arithmetic aggregations, extremum calculations and string manipulations. Events are discretized over a standardized temporal frame (1-3 months) for uniform staging reference, also serving to consolidate any misaligned data instances.
- Model development: our approach employs survival analysis to address the unique challenges of our dataset, particularly censoring, where an event of interest, like death, does not occur within the observation window. Based on our previous experience in modelling this problem, the investigators prefer to use Gradient Boosting Survival Analysis (GBSA), a non-deep learning method, as it effectively addresses data scarcity issues. GBSA adapts the gradient boosting machine algorithm for survival analysis, particularly accommodating censored data. In survival analysis, patients are represented by a triplet (xi, δi, Ti), where xi is the feature vector, Ti is the time to event, and δi indicates whether the observation is censored. Our goal is to estimate the survival function S(t), representing the probability of a patient surviving beyond time t, and the hazard function λ(t), indicating the instantaneous probability of an event occurring at time t. To adapt it for the survival modelling domain, our model utilizes the gradient boosting approach with a modified loss function, the negative log partial likelihood. This allows us to effectively estimate the survival function.
- Performance metrics: the investigators measure model performance using the concordance index (c-index), a metric particularly suited for survival analysis. The c-index assesses the predictive accuracy of our model by comparing predicted and observed event times. A high c-index indicates that our model effectively predicts the order of patient hazard given its input features.
- Conditions
- Melanoma (Skin Cancer), Nevi and Melanomas
- Being tested
- Gradient Boosting Survival Analysis (GBSA),, Concordance index
- Keywords
- Melanoma, Artificial intelligence, Patient stratification, Machine learning, Children, Adolescents, Young Adults, Survival analysis
- Medical subject headings
- Melanoma, Nevi and Melanomas
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Gradient Boosting Survival Analysis (GBSA),Other
It is a non-deep learning method that effectively addresses data scarcity issues. GBSA adapts the gradient boosting machine algorithm for survival analysis, particularly accommodating censored data. In survival analysis, patients are represented by a triplet (xi, δi, Ti), where xi is the feature vector, Ti is the time to event, and δi indicates whether the observation is censored. Our goal is to estimate the survival function S(t), representing the probability of a patient surviving beyond time t, and the hazard function λ(t), indicating the instantaneous probability of an event occurring at time t.
Concordance indexOther
The survival model performance will be evaluated using the concordance index (c-index), a metric particularly suited for survival analysis. The c-index assesses the predictive accuracy of our model by comparing predicted and observed event times. A high c-index indicates that our model effectively predicts the order of patient hazard given its input features.
| Study group | Role | What they receive |
|---|---|---|
| Melanoma patients | The training dataset will consist of 6000 adult melanoma patients while the adaptation dataset for children, adolescents and young adults (CAYA) will be of N = 120. Receives: Other: Gradient Boosting Survival Analysis (GBSA),; Other: Concordance index |
What is being measured
The primary outcome is the main question the study is designed to answer.
Patient prognosis curves
The main outcome of the study will be to obtain prognosis indicators, mainly survival curves and sentinel lymph node (SLNB) positivity, by training artificial intelligence-based models using tabular clinical data in children, adolescents and young adults (CAYA).
Measured: 24 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 Sep 2024
- Study start1 Mar 2024
- Primary completion1 Mar 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
- Other
- Time perspective
- Retrospective
- Enrolment
- 6,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Fundacion Clinic per a la Recerca Biomédica
- Sponsor type
- University, hospital or other
- Collaborators
- Hospital Clinic of Barcelona
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06608420
- Sponsor's protocol ID
- HCB/2023/1033
- Other IDs
- HORIZON-MISS-2021-CANCER-02-03
- Acronym
- Precis-mel 1
- Status last verified
- Sep 2024
- First submitted
- 19 Sep 2024
- Last updated
- 28 Feb 2025
- 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 28 Feb 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Precision Medicine for L/GCMN and Melanoma 1 (Precis-mel 1)
- ClinicalTrials.gov ID
- NCT06608420
- Acronym / sponsor ID
- Precis-mel 1 / HCB/2023/1033
- Conditions
- Melanoma (Skin Cancer), Nevi and Melanomas
- Keywords
- Melanoma, Artificial intelligence, Patient stratification, Machine learning, Children, Adolescents, Young Adults, Survival analysis
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 6,000 participants (estimated)
- Lead sponsor
- Fundacion Clinic per a la Recerca BiomédicaUniversity, hospital or other
- Collaborators
- Hospital Clinic of Barcelona
- Study start
- 1 Mar 2024 (actual)
- Primary completion
- 1 Mar 2026 (estimated)
- Study completion
- 30 Nov 2026 (estimated)
- First posted
- 23 Sep 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 primary objective of this study is to create a highly multidimensional and multicentric database for melanoma that encompasses cohorts of children, adolescent and young adults. This database will be used to perform survival analysis and evaluate sentinel lymph node (SLNB) positivity in CAYA. The secondary objectives to be met are the following:
- Adaptation and optimization of algorithms: work on optimizing existing precision medicine algorithms, which are currently being used in adult patient care, for their application within pediatric and young adult populations.
- Implementation of transfer learning: given the limitations associated with pediatric and young adult data, the investigators intend to utilize transfer learning techniques. The study will employ a sequential waterfall methodology, whereby machine learning models trained on adult patient data will be fine-tuned using the more limited data from younger cohorts.
- Integration of expert medical opinion: to integrate physician's scientific domain knowledge into the decision support system. This will be facilitated through the comprehensive examination of existing literature, as well as the evaluation of variable risk contributions within each patient group.
- AI-based prognostic models: to develop artificial intelligence-based models for the quantitative prognosis of melanoma across the three age groups: adults, young adults, and children.
Detailed description
Precis-Mel 1 is a unicentric observational study using retrospectively collected data. The proposed procedure is to start using data including demographic and family data, genetic data, medical procedures and cancer treatment, cutaneous biopsy, etc. to build a multidimensional dataset and apply AI algorithms that can produce survival curves and sentinel lymph node (SLNB) positivity in CAYA. The approach to be used is presented in the following sub-sections:
- Data engineering: the multidimensional dataset is meticulously integrated via DBT and SQL queries on a PostgreSQL database. This results in a model-ready comprehensive table, maintaining the crucial temporal dimension of patient histories. Identifiers are assigned to maintain the integrity of the data trail and the connection between various patient events such as metastasis and death. Python-based transformations ensure that sequential patient events are contextually enriched by preceding occurrences. Operations include arithmetic aggregations, extremum calculations and string manipulations. Events are discretized over a standardized temporal frame (1-3 months) for uniform staging reference, also serving to consolidate any misaligned data instances.
- Model development: our approach employs survival analysis to address the unique challenges of our dataset, particularly censoring, where an event of interest, like death, does not occur within the observation window. Based on our previous experience in modelling this problem, the investigators prefer to use Gradient Boosting Survival Analysis (GBSA), a non-deep learning method, as it effectively addresses data scarcity issues. GBSA adapts the gradient boosting machine algorithm for survival analysis, particularly accommodating censored data. In survival analysis, patients are represented by a triplet (xi, δi, Ti), where xi is the feature vector, Ti is the time to event, and δi indicates whether the observation is censored. Our goal is to estimate the survival function S(t), representing the probability of a patient surviving beyond time t, and the hazard function λ(t), indicating the instantaneous probability of an event occurring at time t. To adapt it for the survival modelling domain, our model utilizes the gradient boosting approach with a modified loss function, the negative log partial likelihood. This allows us to effectively estimate the survival function.
- Performance metrics: the investigators measure model performance using the concordance index (c-index), a metric particularly suited for survival analysis. The c-index assesses the predictive accuracy of our model by comparing predicted and observed event times. A high c-index indicates that our model effectively predicts the order of patient hazard given its input features.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Melanoma patients | – | Other: Gradient Boosting Survival Analysis (GBSA),, Other: Concordance indexThe training dataset will consist of 6000 adult melanoma patients while the adaptation dataset for children, adolescents and young adults (CAYA) will be of N = 120. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Review and/or analysis of pre-existing medical records, biological samples and data collected from patients that have been visited at our hospital.
Key inclusion criteria (1 listed)
- Melanoma patients of any age with histopathological confirmed melanoma
Key exclusion criteria (3 listed)
- Not having a melanoma diagnosis
- Not having signed the informed consent
- Records prior to the year 2012 (as data might not accurately reflect current practices and treatment outcomes)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Gradient Boosting Survival Analysis (GBSA),It is a non-deep learning method that effectively addresses data scarcity issues. GBSA adapts the gradient boosting machine algorithm for survival analysis, particularly accommodating censored data. In survival analysis, patients are represented by a triplet (xi, δi, Ti), where xi is the feature vector, Ti is the time to event, and δi indicates whether the observation is censored. Our goal is to estimate the survival function S(t), representing the probability of a patient surviving beyond time t, and the hazard function λ(t), indicating the instantaneous probability of an event occurring at time t. | Other | Melanoma patients |
| Concordance indexThe survival model performance will be evaluated using the concordance index (c-index), a metric particularly suited for survival analysis. The c-index assesses the predictive accuracy of our model by comparing predicted and observed event times. A high c-index indicates that our model effectively predicts the order of patient hazard given its input features. | Other | Melanoma patients |
Primary outcome
Patient prognosis curves
Time frame: 24 months
The main outcome of the study will be to obtain prognosis indicators, mainly survival curves and sentinel lymph node (SLNB) positivity, by training artificial intelligence-based models using tabular clinical data in children, adolescents and young adults (CAYA).
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 28 Feb 2025.
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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.
Adrián López Canosa, PhD contact
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Melanoma (Skin Cancer) and Nevi and Melanomas, run by Fundacion Clinic per a la Recerca Biomédica at 1 site in Spain.
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 3 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 28 Feb 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in 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 Susana Puig Sardà, MD, PhD (+34932275400, spuig@clinic.cat), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Patient prognosis curves (time frame: 24 months). 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 28 Feb 2025.