Cancers of the Head and Neck Region (HNC) are the 6th more deadly cancers worldwide: in Europe roughly 150.000 new cases are detected and 70.000 patients die every year. The main reasons for high mortality are the fact that the majority of cases are diagnosed in advanced Stage and the intrinsic heterogeneity of such tumors, which may impact treatment options. At present the only adopted treatment decision method is based on TNM (Tumor-lymph-Nodes-Metastasis) prognostic system, that considers only a few risk factors such as smoking, alcohol abuse and more recently HPV. The TNM system is therefore inadequate to capture the patient-specific biomolecular characteristics of the tumor. HNC treatments can have hard impact on patient’s aesthetics and functionalities and, due to their toxicity, can cause severe morbidity and greatly deteriorate patient’s quality of life. A more precise prognostic prediction than the current TNM system is needed that allows implementing the first-line treatment that maximizes the therapeutic result and minimizes the impacts of therapy. BD2Decide develops, realizes and validates an Integrated Decision Support System (DSS) which provides clinicians with all the necessary information to tailor treatment and care delivery pathway to each and any HNC patient during their usual practice, in contrast to current "one-size-fits-all approach". The BD2Decide DSS links population-specific epidemiology and behavioral data, patient-specific genomic, pathology, clinical and imaging data with big data techniques, multiscale prognostic models. Advanced graphical visualization tools are also developed for prognostic data disclosure and patient co-participation to the selected treatment. BD2Decide will improve the clinical decision process, uncover new patient-specific patterns that can improve care and create a virtuous circle of learning. A multicentric clinical study with more than 1.000 patients will be used to validate the system. The Research group at DEIB leads the MRI scan Radiomics task in WP3 and is responsible for the identification of Radiomics signatures from MRI and DWI-MRI scans.
The project is closed.