PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers

Martí-Bonmatí, L. ; Alberich-Bayarri, Á. ; Ladenstein, R. ; Blanquer, I. ; Segrelles, J.D. ; Cerdá-Alberich, L. ; Gkontra, P. ; Hero, B. ; García-Aznar, J.M. (Universidad de Zaragoza) ; Keim, D. ; Jentner, W. ; Seymour, K. ; Jiménez-Pastor, A. ; González-Valverde, I. ; Martínez de Las Heras, B. ; Essiaf, S. ; Walker, D. ; Rochette, M. ; Bubak, M. ; Mestres, J. ; Viceconti, M. ; Martí-Besa, G. ; Cañete, A. ; Richmond, P. ; Wertheim, K.Y. ; Gubala, T. ; Kasztelnik, M. ; Meizner, J. ; Nowakowski, P. ; Gilpérez, S. ; Suárez, A. ; Aznar, M. ; Restante, G. ; Neri, E.
PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers
Financiación H2020 / H2020 Funds
Resumen: PRIMAGE is one of the largest and more ambitious research projects dealing with medical imaging, artificial intelligence and cancer treatment in children. It is a 4-year European Commission-financed project that has 16 European partners in the consortium, including the European Society for Paediatric Oncology, two imaging biobanks, and three prominent European paediatric oncology units. The project is constructed as an observational in silico study involving high-quality anonymised datasets (imaging, clinical, molecular, and genetics) for the training and validation of machine learning and multiscale algorithms. The open cloud-based platform will offer precise clinical assistance for phenotyping (diagnosis), treatment allocation (prediction), and patient endpoints (prognosis), based on the use of imaging biomarkers, tumour growth simulation, advanced visualisation of confidence scores, and machine-learning approaches. The decision support prototype will be constructed and validated on two paediatric cancers: neuroblastoma and diffuse intrinsic pontine glioma. External validation will be performed on data recruited from independent collaborative centres. Final results will be available for the scientific community at the end of the project, and ready for translation to other malignant solid tumours.
Idioma: Inglés
DOI: 10.1186/s41747-020-00150-9
Año: 2020
Publicado en: European radiology experimental 4, 1 (2020), 22
ISSN: 2509-9280

Factor impacto SCIMAGO:

Financiación: info:eu-repo/grantAgreement/EC/H2020/826494/EU/PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers/PRIMAGE
Tipo y forma: Article (Published version)
Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)

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