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Precision Medicine for L/GCMN and Melanoma 1

NCT06608420 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 6000

Last updated 2025-02-28

No results posted yet for this study

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. 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.

Conditions

  • Melanoma (Skin Cancer)
  • Nevi and Melanomas

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.

Sponsors & Collaborators

  • Hospital Clinic of Barcelona

    collaborator OTHER

  • Fundacion Clinic per a la Recerca Biomédica

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-03-01
Primary Completion
2026-03-01
Completion
2026-11-30

Countries

  • Spain

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06608420 on ClinicalTrials.gov