Clinical Research Directory

CongenItal Naevus Cohort for Longitudinal Evaluation

Congenital Nevus (CN) is a pigmented skin lesion present at birth, which grows in size as the child grows. It can vary in appearance and is classified by its size, from small (less than 1.5 cm) to giant (greater than 40 cm). CN is associated with genetic mutations, mainly in the NRAS/BRAF genes. A large CN can lead to several clinical issues, including: Risk of neurological disorders: Large CN can be associated with neurological abnormalities such as neuro-meningeal melanosis, hydrocephalus, or brain malformations. These conditions may cause early neuro-developmental delays. The risk is not well understood and requires further studies. Risk of melanoma: The risk of developing melanoma is higher for a large CN but remains low for smaller ones. Increased monitoring is necessary during the early years for large and giant CN. Psycho-social impact: Parents often experience significant anxiety at birth due to the cancer risk and social stigma. As the child grows, a visible CN may impact their quality of life, particularly socially at school. Management of CN remains controversial, especially for those of medium to giant size or with multiple satellites. There is an urgent need for further research to clarify best practices in monitoring and treatment, including the need for routine brain imaging and criteria for surgical intervention. Ultimately, this study aims to deepen our understanding of CN, its associated neurological and melanoma risks, and the psycho-social challenges it poses, while striving to establish clear, evidence-based guidelines for monitoring and treatment to enhance patient outcomes and quality of life.

Development of an Aid to Melanoma Detection Using Artificial Intelligence Algorithms Based on Images From the VECTRA 3D System.

The background to this research is that frequent medical screening of the general population for melanoma is not feasible. The real challenge of this project is to develop an automatic process for detecting any potential melanoma. To this end, the project aims to design an algorithm to build a novel diagnostic aid that makes use of the similarity and disparity of pigmented lesions in the same patient. To achieve this, we need to obtain and structure a large database of images grouping all pigmented lesions per patient according to their similarities as perceived by dermatologists.