Many clinical diseases are characterized by extremely complex phenotypes, often determined by multiple variables. In such cases, the totality of the molecular complexity can be poorly explicated by a single element of causality, making necessary the integration of multiple omic groups. The aim of this project is to provide a proof of concepts on the advantage of multi-omics integration throughout an AI-based framework. Imaging, clinical and fluid biomarkers will be integrated, setting-up, fine-tuning and evaluating different machine learning methods for the prediction of the clinical outcome of the patient. Eventually, the most informative identified features will be transformed into tailored predictive models. This project is financed by the Institute of Informatics and Telematics of CNR and will be jointly conducted by SIENA Imaging S.R.L, with expertise in developing MRI biomarkers, located at Toscana Life Sciences (TLS).