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

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 Paediatri...

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Published inEuropean radiology experimental Vol. 4; no. 1; p. 22
Main Authors Martí-Bonmatí, Luis, Alberich-Bayarri, Ángel, Ladenstein, Ruth, Blanquer, Ignacio, Segrelles, J. Damian, Cerdá-Alberich, Leonor, Gkontra, Polyxeni, Hero, Barbara, García-Aznar, J. M., Keim, Daniel, Jentner, Wolfgang, Seymour, Karine, Jiménez-Pastor, Ana, González-Valverde, Ismael, Martínez de las Heras, Blanca, Essiaf, Samira, Walker, Dawn, Rochette, Michel, Bubak, Marian, Mestres, Jordi, Viceconti, Marco, Martí-Besa, Gracia, Cañete, Adela, Richmond, Paul, Wertheim, Kenneth Y., Gubala, Tomasz, Kasztelnik, Marek, Meizner, Jan, Nowakowski, Piotr, Gilpérez, Salvador, Suárez, Amelia, Aznar, Mario, Restante, Giuliana, Neri, Emanuele
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 03.04.2020
Springer Nature B.V
SpringerOpen
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Summary: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.
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ISSN:2509-9280
2509-9280
DOI:10.1186/s41747-020-00150-9