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Financement de l’UE (10,3 M €) : Analyse multi-échelle in silico pour soutenir le diagnostic et le pronostic personnalisés du cancer, renforcé par des biomarqueurs d’imagerie Hor01/12/2018 Programme de recherche et d'innovation de l'UE « Horizon »
Vue d’ensemble
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Analyse multi-échelle in silico pour soutenir le diagnostic et le pronostic personnalisés du cancer, renforcé par des biomarqueurs d’imagerie
PRIMAGE proposes a cloud-based platform to support decision making in the clinical management of malignant solid tumours, offering predictive tools to assist diagnosis, prognosis, therapies choice and treatment follow up, based on the use of novel imaging biomarkers, in-silico tumour growth simulation, advanced visualisation of predictions with weighted confidence scores and machine-learning based translation of this knowledge into predictors for the most relevant, disease-specific, Clinical End Points. PRIMAGE implements a hybrid cloud model, comprising the of use of open public cloud (based on EOSC services) and private clouds, enabling use by the scientific community (facilitating reuse of de-identified clinical curated data in Open Science) and also suitable for future commercial exploitation. The proposed data infrastructures, imaging biomarkers and models for in-silico medicine research will be validated in the application context of two paediatric cancers, Neuroblastoma (NB, the most frequent solid cancer of early childhood) and the Diffuse Intrinsic Pontine Glioma (DIPG, the leading cause of brain tumour-related death in children). These two paediatric cancers are relevant validation cases given their representativeness of cancer disease, and their high societal impact, as they affect the most vulnerable and loved family members. The European Society for Paediatric Oncology, two Imaging Biobanks and three of the most prominent European Paediatric oncology units are partners in this project, making retrospective clinical data (imaging, clinical, molecular and genetics) registries accessible to PRIMAGE, for training of machine learning algorithms and testing of the in-silico tools´ performance. Solutions to streamline and secure the data pseudonymisation, extraction, structuring, quality control and storage processes, will be implemented and validated also for use on prospective data, contributing European shared data infrastructures.
| Akademia Gorniczo-Hutnicza im. Stanislawa Staszica w Krakowie | 538 566 € |
| Alma Mater Studiorum - Universita Di Bologna | 192 314 € |
| Ansys France SAS | 810 874 € |
| BC Platforms | 384 806 € |
| Chemotargets SL | 661 326 € |
| Fundacion para La Investigacion del Hospital Universitario La Fe de La Comunidad Valenciana | 1 243 342 € |
| Klinikum DER Universitaet ZU Koeln | 751 375 € |
| Matical Innovation SL | 380 756 € |
| Quibim SL | 919 064 € |
| Siop Europe | 323 364 € |
| ST. Anna Kinderkrebsforschung GmbH | 842 512 € |
| ST Anna Kinderkrebsforschung Verein | 0,00 € |
| The University of Sheffield | 616 680 € |
| Universidad de Zaragoza | 796 184 € |
| Universita Di Pisa | 637 564 € |
| Universitat Konstanz | 756 066 € |
| Universitat Politecnica de Valencia | 457 126 € |
https://cordis.europa.eu/project/id/826494
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