Computational Medicine for Prostate Cancer

Proponente Valeria Panebianco - Professore Ordinario
Sottosettore ERC del proponente del progetto
Componenti gruppo di ricerca
Componente Categoria
Daniele Izzi Dottorando/Assegnista/Specializzando componente non strutturato del gruppo di ricerca
Paola Paci Componenti strutturati del gruppo di ricerca
Antonio Ciardi Componenti strutturati del gruppo di ricerca
Componente Qualifica Struttura Categoria
Giuseppe Simone Dirigente Medico I livello, Facente Funzione II livello del Reparto di Urologia Ospedale Regina Elena - Istituti Fisioterapici Ospitalieri (IFO) Altro personale aggregato Sapienza o esterni, titolari di borse di studio di ricerca

Prostate Cancer (PCa) is the second most common cancer in men worldwide and it is universally acknowledged as a complex disease, with a multi-factorial etiology. For long time, the major issues of prostate cancer management heve been rapresented by the non-targeted approach that have caused a high number of overdiagnosis and overtreatment, with the detection and treatment of clinically insignificant PCa (ciPCa), increasing overall costs and complications' rate.
The pathway of PCa diagnosis has changed dramatically in the last few years, with the multiparametric Magnetic Resonance (mpMRI) and the MR-directed targeted biopsies, playing a starring role and constituting the recently introduced "MRI Pathway" , that allowed to lower the rate of ciPCa.
In this scenario the basic tenet of Computational Medicine (CM) that sees the disease as perturbation of a network of interconnected molecules and pathways, seems to fit perfectly with the challenges that PCa early detection must face to advance towards a more reliable technique. Integration of tests on body fluids, tissue samples, grading/staging classification, physiological parameters, MR multiparametric imaging, Artificial Intelligence and molecular profiling technologies must be integrated in a broader vision of "disease" and its complexity with a focus on early signs. PCa research can greatly benefit from CM vision since it provides a sound interpretation of data and a common language, facilitating exchange of ideas between clinicians and data analysts for exploring new research pathways in a rational, highly reliable, and reproducible way. Thanks to the possibility given by this comprehensive approach, the definitive end-point is to avoid useless diagnostic procedure and disease overtreatment.

LS7_1, PE6_11, PE7_8

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