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VPH based predictive model for oral cancer reoccurrence in the clinical practice

Clinical oncology is undergoing a profound transformation, driven by the availability of new diagnostic technologies, novel techniques for bio-molecular and genomic tests, aimed at predictive markers discovery, smart mathematical and statistical algorithms for disease modelling and prediction simulation, powerful computing for data mining and data analysis on large datasets of heterogeneous data. The approach to care delivery is also shifting from treatment administration to treatment planning, supported by predictive modelling.

OraMod perfectly fits in this scenario: the project converges the technology and scientific breakthrough from research into clinical practice by developing a novel, modular and integrated ICT environment aimed at supporting key aspects of the clinical management of oral cavity cancer patients.

OraMod focuses on the integration of data, medical knowledge, multi-disciplinary collaborative best clinical practices and cutting-edge technologies, including modelling, in-silico simulation, and decision support aimed at early and precisely predict oral cancer reoccurrence. In so doing the project addresses urgent clinical and societal needs: better clinical decisions regarding treatment and follow-up, reduced costs, and benefits for patients from reduced post-treatment morbidity.

Building from previous ICT-224483 NeoMark, OraMod will combine a predictive model relying on a variety of data collected and managed in the system repository, with decision support and in-silico simulation tools, an interactive Knowledge Assisted Visualization in line with the "Digital Patient" paradigm, a collaborative working space to support multi-disciplinary decision-making, a sophisticated suite for diagnostic image analysis and features extraction, a qRT-PCR device and lab-on-chip for fast, precise, quantitative detection of the genomic markers included in the model. A clinical trial in four European pilot hospitals demonstrates tangible clinical benefits.

This project has received funding from the European Union’s Horizon 2020 Framework Programme.
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