@article{MartiBonmati2020-04-03PRIMA-49224,
  year={2020},
  doi={10.1186/s41747-020-00150-9},
  title={PRIMAGE project : predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers},
  number={1},
  volume={4},
  journal={European Radiology Experimental},
  author={Martí-Bonmatí, Luis and Alberich-Bayarri, Ángel and Ladenstein, Ruth and Blanquer, Ignacio and Segrelles, J. Damian and Cerdá-Alberich, Leonor and Gkontra, Polyxeni and Hero, Barbara and Keim, Daniel A. and Jentner, Wolfgang},
  note={Article Number: 22}
}

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    <dcterms:abstract xml:lang="eng">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.</dcterms:abstract>
    <dc:creator>Alberich-Bayarri, Ángel</dc:creator>
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    <dc:contributor>Segrelles, J. Damian</dc:contributor>
    <dc:contributor>Cerdá-Alberich, Leonor</dc:contributor>
    <dc:creator>Ladenstein, Ruth</dc:creator>
    <dc:creator>Gkontra, Polyxeni</dc:creator>
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    <dc:creator>Jentner, Wolfgang</dc:creator>
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    <dc:contributor>Blanquer, Ignacio</dc:contributor>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-04-16T13:45:03Z</dc:date>
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    <dc:contributor>Keim, Daniel A.</dc:contributor>
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    <dc:contributor>Ladenstein, Ruth</dc:contributor>
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    <dc:creator>Cerdá-Alberich, Leonor</dc:creator>
    <dc:rights>Attribution 4.0 International</dc:rights>
    <dc:creator>Hero, Barbara</dc:creator>
    <dc:creator>Segrelles, J. Damian</dc:creator>
    <dc:creator>Blanquer, Ignacio</dc:creator>
    <dcterms:title>PRIMAGE project : predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers</dcterms:title>
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    <dc:contributor>Jentner, Wolfgang</dc:contributor>
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