Precision Medicine for L/GCMN and Melanoma 1

Study Purpose

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.

Recruitment Criteria

Accepts Healthy Volunteers Healthy volunteers are participants who do not have a disease or condition, or related conditions or symptoms	No
Study Type An interventional clinical study is where participants are assigned to receive one or more interventions (or no intervention) so that researchers can evaluate the effects of the interventions on biomedical or health-related outcomes. An observational clinical study is where participants identified as belonging to study groups are assessed for biomedical or health outcomes. Searching Both is inclusive of interventional and observational studies.	Observational
Eligible Ages	N/A and Over
Gender	All

More Inclusion & Exclusion Criteria

Inclusion Criteria:

- Melanoma patients of any age with histopathological confirmed melanoma.

Exclusion Criteria:

- 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 Details

Trial ID: This trial id was obtained from ClinicalTrials.gov, a service of the U.S. National Institutes of Health, providing information on publicly and privately supported clinical studies of human participants with locations in all 50 States and in 196 countries.	NCT06608420
Phase Phase 1: Studies that emphasize safety and how the drug is metabolized and excreted in humans. Phase 2: Studies that gather preliminary data on effectiveness (whether the drug works in people who have a certain disease or condition) and additional safety data. Phase 3: Studies that gather more information about safety and effectiveness by studying different populations and different dosages and by using the drug in combination with other drugs. Phase 4: Studies occurring after FDA has approved a drug for marketing, efficacy, or optimal use.
Lead Sponsor The sponsor is the organization or person who oversees the clinical study and is responsible for analyzing the study data.	Fundacion Clinic per a la Recerca Biomédica
Principal Investigator The person who is responsible for the scientific and technical direction of the entire clinical study.	N/A
Principal Investigator Affiliation	N/A
Agency Class Category of organization(s) involved as sponsor (and collaborator) supporting the trial.	Other
Overall Status	Recruiting
Countries	Spain
Conditions The disease, disorder, syndrome, illness, or injury that is being studied.	Melanoma (Skin Cancer), Nevi and Melanomas

Additional Details

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.

Arms & Interventions

Arms

: 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.

Interventions

Other: - 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: - Concordance index

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.

Contact a Trial Team

If you are interested in learning more about this trial, find the trial site nearest to your location and contact the site coordinator via email or phone. We also strongly recommend that you consult with your healthcare provider about the trials that may interest you and refer to our terms of service below.

International Sites

Barcelona, Catalonia, Spain

Status

Recruiting

Address

Hospital Clínic de Barcelona (Dermatology service)

Barcelona, Catalonia, 08036

Site Contact

Susana Puig Sardà, PhD, MD

[email protected]

+34932275400

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