Luis Martí-Bonmatí
PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers
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
Authors
Ángel Alberich-Bayarri
Ruth Ladenstein
Ignacio Blanquer
J. Damian Segrelles
Leonor Cerdá-Alberich
Polyxeni Gkontra
Barbara Hero
J. M. García-Aznar
Daniel Keim
Wolfgang Jentner
Karine Seymour
Ana Jiménez-Pastor
Ismael González-Valverde
Blanca Martínez de las Heras
Samira Essiaf
Dawn Walker
Michel Rochette
Marian Bubak
Jordi Mestres
Marco Viceconti
Gracia Martí-Besa
Adela Cañete
Paul Richmond
Dr Kenneth Y. Wertheim K.Y.Wertheim@hull.ac.uk
Lecturer and EDI Champion
Tomasz Gubala
Marek Kasztelnik
Jan Meizner
Piotr Nowakowski
Salvador Gilpérez
Amelia Suárez
Mario Aznar
Giuliana Restante
Emanuele Neri
Abstract
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.
Citation
Martí-Bonmatí, L., Alberich-Bayarri, Á., Ladenstein, R., Blanquer, I., Segrelles, J. D., Cerdá-Alberich, L., Gkontra, P., Hero, B., García-Aznar, J. M., 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., …Neri, E. (2020). PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers. European Radiology Experimental, 4(1), https://doi.org/10.1186/s41747-020-00150-9
Journal Article Type | Article |
---|---|
Publication Date | Dec 1, 2020 |
Deposit Date | Jan 23, 2023 |
Publicly Available Date | Jan 24, 2023 |
Journal | European Radiology Experimental |
Electronic ISSN | 2509-9280 |
Publisher | SpringerOpen |
Peer Reviewed | Peer Reviewed |
Volume | 4 |
Issue | 1 |
DOI | https://doi.org/10.1186/s41747-020-00150-9 |
Public URL | https://hull-repository.worktribe.com/output/4186836 |
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