Scientific thinking is the only type of thinking that allows us to be critical and advance in improving the health of patients. It is our duty and our responsibility to carry out applied research
Skin cancer is the most common in humans and, among the various common skin tumours, melanoma is the most aggressive and the one with the worst prognosis. Sun exposure habits and the aging population are, nowadays, a growing problem in western societies.
The group focuses on the study of melanoma and non-melanoma skin cancer, as well as other skin conditions related to the risk of developing these cancers. The purpose is to improve prevention, early diagnosis and the achievement of a more personalised treatment of melanoma and non-melanoma skin cancer.
The research of the group is multidisciplinary: it consists of dermatologists, pathologists, nurses, biologists, biotechnologists, engineers, physicists, technicians and other specialists.
The research is translational. That is to say, it combines clinical studies with more molecular studies and works that combine the two aspects with the aim of identifying improvements both in prevention and in the diagnosis, management, monitoring and treatment of patients. These improvements are transferred directly to patients, improving their quality of life and that of their families. It uses combined new image technologies, molecular studies and artificial intelligence tools.
Thanks to the studies the group has carried out, it has been able to improve the precocious diagnosis of skin cancer, reducing the number of benign lesions removed and diagnosing melanomas that are very thin and of a good prognosis.
In addition, it has described the main genes of susceptibility of melanoma in the population and it has identified several molecular prognostic markers useful in clinical practice. With the group’s new research lines it intends to improve the classification of patients in specific prognosis groups to improve their management and treatment. The group wants to identify prognostic and predictive biomarkers.