Optimization of personalized diagnosis and prognosis in patients with melanocytic tumors of uncertain malignant potential through the artificial intelligence validation of algorithms based on epigenetic information

Melanocytic tumors of uncertain malignant potential currently represent one of the main diagnostic challenges in dermatopathology due to the difficulty in predicting their clinical behavior and metastatic risk. The absence of objective and reproducible tools leads to high diagnostic variability, intensive clinical follow-up, and the performance of invasive procedures that, in many cases, may be unnecessary.

In this context, the project proposes the development and validation of Artificial Intelligence algorithms capable of analyzing epigenetic patterns obtained from DNA methylation data.

Through RRBS sequencing techniques, Deep Learning models, and advanced bioinformatics analysis methodologies, IMAGEN aims to identify molecular signatures associated with the biological behavior of ambiguous spitzoid tumors.

In addition, the project plans to externally validate these models using clinically accessible and lower-cost techniques, such as pyrosequencing, thereby facilitating their future integration into routine clinical practice.

IMAGEN seeks to contribute to a significant reduction in diagnostic subjectivity and interobserver variability, enabling optimized clinical follow-up and the adaptation of therapeutic decisions to the individual risk profile of each patient.

Likewise, the project aims to reduce the number of invasive tests and unnecessary procedures, improve patients’ quality of life, and decrease the economic impact associated with the diagnosis and monitoring of these tumors.

The combination of Artificial Intelligence and epigenetic biomarkers could also open new pathways for the development of prognostic tools applicable to other oncological diseases.

IMAGEN is being developed through a collaboration between the Universitat Politècnica de València (UPV) and INCLIVA, with the participation of EPIDISEASE SL as a collaborating company specialized in epigenetic analysis and RRBS data processing.

The project builds on the consortium’s previous experience in initiatives related to Artificial Intelligence applied to cancer, bioinformatics, and digital histopathological analysis.