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Precision Medicine for L/GCMN and Melanoma 1
Sponsor: Fundacion Clinic per a la Recerca Biomédica
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.
Official title: Precision Medicine for L/GCMN and Melanoma 1 (Precis-mel 1)
Key Details
Gender
All
Age Range
Any - Any
Study Type
OBSERVATIONAL
Enrollment
6000
Start Date
2024-03-01
Completion Date
2026-11-30
Last Updated
2025-02-28
Healthy Volunteers
No
Conditions
Interventions
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.
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.
Locations (1)
Hospital Clínic de Barcelona (Dermatology service)
Barcelona, Catalonia, Spain