Precision Medicine for L/GCMN and Melanoma 1

NCT06608420 | Recruiting

• 2 other identifiers: HCB/2023/1033HORIZON-MISS-2021-CANCER-02-03
• Study Type: observational
• Target: 6,000
• Locations: 1 country
• Sites: 1

Brief 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

Keywords

Melanoma; Artificial intelligence; Patient stratification; Machine learning; Children; Adolescents; Young Adults; Survival analysis

Outcome Measures

Primary Outcomes (1)

• 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).

  24 months

Study Arms (1)

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.

Other: Gradient Boosting Survival Analysis (GBSA),; Other: Concordance index

Interventions

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.

Melanoma patients

Concordance index OTHER

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.

Melanoma patients

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• \- Melanoma patients of any age with histopathological confirmed melanoma

You may not qualify if:

• 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)

Sponsors & Collaborators

• Fundacion Clinic per a la Recerca Biomédica: lead
• Hospital Clinic of Barcelona: collaborator

Study Sites (1)

Hospital Clínic de Barcelona (Dermatology service)

Barcelona, Catalonia, 08036, Spain

RECRUITING

MeSH Terms

Conditions

Melanoma; Nevi and Melanomas

Condition Hierarchy (Ancestors)

Neuroendocrine Tumors; Neuroectodermal Tumors; Neoplasms, Germ Cell and Embryonal; Neoplasms by Histologic Type; Neoplasms; Neoplasms, Nerve Tissue; Skin Neoplasms; Neoplasms by Site; Skin Diseases; Skin and Connective Tissue Diseases

Central Study Contacts

Susana Puig Sardà, MD, PhD

CONTACT

+34932275400; spuig@clinic.cat

Adrián López Canosa, PhD

CONTACT

lopez64@recerca.clinic.cat

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 19, 2024
• First Posted: September 23, 2024
• Study Start: March 1, 2024
• Primary Completion: March 1, 2026
• Study Completion (Estimated): November 30, 2026
• Last Updated: February 28, 2025

Record last verified: 2024-09

Locations

ES (1)