Clinical trial · Observational
Precision Medicine for L/GCMN and Melanoma 1
Precision Medicine for L/GCMN and Melanoma 1 (Precis-mel 1)
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Melanoma (Skin Cancer) | Melanoma | ONTOLOGY_EXACT | 0.85 |
| Nevi and Melanomas | — | UNRESOLVED | — |
Interventions
Interventions (2)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Concordance index | Other | — | UNRESOLVED |
| Gradient Boosting Survival Analysis (GBSA), | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Melanoma patients
- description
- 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.
- interventionNames
- Other: Gradient Boosting Survival Analysis (GBSA),
- Other: Concordance index
Primary outcomes (1)
- measure
- Patient prognosis curves
- timeFrame
- 24 months
- description
- 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).
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
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)
References
Publications (0)
Data not yet available