- -

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

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

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

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: Martí-Bonmatí;Alb ...
Tamaño: 1.096Mb
Formato: PDF
Descripción: Versión editorial
Abrir
dc.contributor.author Martí-Bonmatí, Luis es_ES
dc.contributor.author Alberich Bayarri, Ángel es_ES
dc.contributor.author Ladenstein, Ruth es_ES
dc.contributor.author Blanquer Espert, Ignacio es_ES
dc.contributor.author Segrelles Quilis, José Damián es_ES
dc.contributor.author Cerdá-Alberich, Leonor es_ES
dc.contributor.author Gkontra, Polyxeni es_ES
dc.contributor.author Hero, Barbara es_ES
dc.contributor.author García-Aznar, J. M. es_ES
dc.contributor.author Keim, Daniel es_ES
dc.contributor.author Jentner, Wolfgang es_ES
dc.contributor.author Seymour, Karine es_ES
dc.contributor.author Jiménez-Pastor, Ana es_ES
dc.contributor.author González-Valverde, Ismael es_ES
dc.contributor.author Martínez de las Heras, Blanca es_ES
dc.contributor.author Essiaf, Samira es_ES
dc.date.accessioned 2021-02-11T04:31:56Z
dc.date.available 2021-02-11T04:31:56Z
dc.date.issued 2020-04 es_ES
dc.identifier.uri http://hdl.handle.net/10251/161038
dc.description.abstract [EN] 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. es_ES
dc.description.sponsorship Horizon 2020 project (RIA, topic SC1-DTH-07-2018) es_ES
dc.language Inglés es_ES
dc.publisher Springer es_ES
dc.relation.ispartof European Radiology Experimental es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Artificial intelligence es_ES
dc.subject Biomarkers (tumour) es_ES
dc.subject Cloud computing es_ES
dc.subject Diffuse intrinsic pontine glioma es_ES
dc.subject Neuroblastoma es_ES
dc.subject.classification CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL es_ES
dc.title PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1186/s41747-020-00150-9 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/826494/EU/PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Martí-Bonmatí, L.; Alberich Bayarri, Á.; Ladenstein, R.; Blanquer Espert, I.; Segrelles Quilis, JD.; Cerdá-Alberich, L.; Gkontra, P.... (2020). PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers. European Radiology Experimental. 4(22):1-11. https://doi.org/10.1186/s41747-020-00150-9 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1186/s41747-020-00150-9 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 11 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 4 es_ES
dc.description.issue 22 es_ES
dc.identifier.eissn 2509-9280 es_ES
dc.relation.pasarela S\407131 es_ES
dc.contributor.funder European Commission es_ES
dc.description.references Porter ME, Teisberg EO (2006) Redefining health care : creating value-based competition on results. Harvard Business School Press, Boston es_ES
dc.description.references Hernán MA, Robins JM (2016) Using big data to emulate a target trial when a randomized trial is not available. Am J Epidemiol 183:758–764. https://doi.org/10.1093/aje/kwv254 es_ES
dc.description.references Wang SV, Schneeweiss S, Berger ML et al (2017) Reporting to improve reproducibility and facilitate validity assessment for healthcare database studies V1.0. Pharmacoepidemiol Drug Saf 26:1018–1032. https://doi.org/10.1002/pds.4295 es_ES
dc.description.references Viceconti M, Henney A, Morley-Fletcher E (2016) In silico clinical trials: how computer simulation will transform the biomedical industry. Int J Clin Trials 3:37. https://doi.org/10.18203/2349-3259.ijct20161408 es_ES
dc.description.references Martí-Bonmatí L, Alberich-Bayarri A (eds) (2018) Imaging biomarkers : development and clinical integration. Springer, Heidelberg es_ES
dc.description.references Kazem MA (2017) Predictive models in cancer management: a guide for clinicians. Surgeon 15:93–97. https://doi.org/10.1016/j.surge.2016.06.002 es_ES
dc.description.references Martí-Bonmatí L, Ruiz-Martínez E, Ten A, Alberich-Bayarri A (2018) How to integrate quantitative information into imaging reports for oncologic patients. Radiologia 60:43–52. https://doi.org/10.1016/j.rx.2018.02.005 es_ES
dc.description.references Bernsen MR, Kooiman K, Segbers M, Van Leeuween FWB, Jong M (2015) Biomarkers in preclinical cancer imaging. Eur J Nucl Med Mol Imaging 42:579–596. https://doi.org/10.1007/s00259-014-2980-7 es_ES
dc.description.references Alberich-Bayarri A, Neri E, Martí-Bonmatí L (2019) Imaging biomarkers and imaging biobanks. In: Ranschaert E, Morozov S, Algra P (eds) Artificial intelligence in medical imaging. Springer, Heidelberg, pp 119–126 es_ES
dc.description.references O’Connor JPB, Aboagye EO, Adams JE et al (2017) Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 14:169–186. https://doi.org/10.1038/nrclinonc.2016.162 es_ES
dc.description.references Escribano J, Chen MB, Moeendarbary E et al (2019) Balance of mechanical forces drives endothelial gap formation and may facilitate cancer and immune-cell extravasation. PLoS Comput Biol 15:e1006395. https://doi.org/10.1371/journal.pcbi.1006395 es_ES
dc.description.references Enderling H, Chaplain MAJ (2014) Mathematical modeling of tumor growth and treatment. Curr Pharm Des 20:4934–4940. https://doi.org/10.1111/2041-210X.12500 es_ES
dc.description.references Bhatnagar SN (2012) An audit of malignant solid tumors in infants and neonates. J Neonatal Surg 1:5 PMID: 26023364; PMCID: PMC4420305 es_ES
dc.description.references London WB, Castleberry RP, Matthay KK et al (2005) Evidence for an age cutoff greater than 365 days for neuroblastoma risk group stratification in the Children’s Oncology Group. J Clin Oncol 23:6459–6465. https://doi.org/10.1200/JCO.2005.05.571 es_ES
dc.description.references Cohn SL, Pearson ADJ, London WB et al (2009) The international neuroblastoma risk group (INRG) classification system: an INRG task force report. J Clin Oncol 27:289–297. https://doi.org/10.1200/JCO.2008.16.6785 es_ES
dc.description.references Morgenstern DA, Pötschger U, Moreno L et al (2018) Risk stratification of high-risk metastatic neuroblastoma: a report from the HR-NBL-1/SIOPEN study. Pediatr Blood Cancer 65:e27363. https://doi.org/10.1002/pbc.27363 es_ES
dc.description.references Rubie H, De Bernardi B, Gerrard M et al (2011) Excellent outcome with reduced treatment in infants with nonmetastatic and unresectable neuroblastoma without MYCN amplification: results of the prospective INES 99.1. J Clin Oncol 29:449–455. https://doi.org/10.1200/JCO.2010.29.5196 es_ES
dc.description.references De Bernardi B, Gerrard M, Boni L et al (2009) Excellent outcome with reduced treatment for infants with disseminated neuroblastoma without MYCN gene amplification. J Clin Oncol 27:1034–1040. https://doi.org/10.1200/JCO.2008.17.5877 es_ES
dc.description.references Kohler JA, Rubie H, Castel V et al (2013) Treatment of children over the age of one year with unresectable localised neuroblastoma without MYCN amplification: results of the SIOPEN study. Eur J Cancer 49:3671–3679. https://doi.org/10.1016/j.ejca.2013.07.002 es_ES
dc.description.references Canete A, Gerrard M, Rubie H et al (2009) Poor survival for infants with MYCN-amplified metastatic neuroblastoma despite intensified treatment: the International Society of Paediatric Oncology European Neuroblastoma Experience. J Clin Oncol 27:1014–1019. https://doi.org/10.1200/JCO.2007.14.5839 es_ES
dc.description.references Avanzini S, Pio L, Erminio G et al (2017) Image-defined risk factors in unresectable neuroblastoma: SIOPEN study on incidence, chemotherapy-induced variation, and impact on surgical outcomes. Pediatr Blood Cancer 64. https://doi.org/10.1002/pbc.26605 es_ES
dc.description.references Monclair T, Mosseri V, Cecchetto G, De Bernardi B, Michon J, Holmes K (2015) Influence of image-defined risk factors on the outcome of patients with localised neuroblastoma. A report from the LNESG1 study of the European International Society of Paediatric Oncology Neuroblastoma Group. Pediatr Blood Cancer 62:1536–1542. https://doi.org/10.1002/pbc.25460 es_ES
dc.description.references Ladenstein R, Pötschger U, Pearson ADJ et al (2017) Busulfan and melphalan versus carboplatin, etoposide, and melphalan as high-dose chemotherapy for high-risk neuroblastoma (HR-NBL1/SIOPEN): an international, randomised, multi-arm, open-label, phase 3 trial. Lancet Oncol 18:500–514. https://doi.org/10.1016/S1470-2045(17)30070-0 es_ES
dc.description.references Ladenstein R, Pötschger U, Valteau-Couanet D et al (2018) Interleukin 2 with anti-GD2 antibody ch14.18/CHO (dinutuximab beta) in patients with high-risk neuroblastoma (HR-NBL1/SIOPEN): a multicentre, randomised, phase 3 trial. Lancet Oncol 19:1617–1629. https://doi.org/10.1016/S1470-2045(18)30578-3 es_ES
dc.description.references Johung TB, Monje M (2017) Diffuse intrinsic pontine glioma: new pathophysiological insights and emerging therapeutic targets. Curr Neuropharmacol 15:88–97. https://doi.org/10.2174/1570159x14666160509123229 es_ES
dc.description.references Sacha D, Stoffel A, Stoffel F, Kwon BC, Ellis G, Keim DA (2014) Knowledge generation model for visual analytics. IEEE Trans Vis Comput Graph 20:1604–1603. https://doi.org/10.1109/TVCG.2014.2346481 es_ES
dc.description.references Rimrock. Robust Remote Process Controller. https://submit.plgrid.pl/. Accessed 28 Sept 2019 es_ES
dc.description.references PL-Grid Data service. https://data.plgrid.pl/?locale=en. Accessed 28 Sept 2019 es_ES
dc.description.references ATMOSPHERE Project. Adaptive, trustworthy, manageable, orchestrated, secure, privacy-assuring, hybrid ecosystem for resilient cloud computing. ATMOSPHERE Project. https://cordis.europa.eu/project/rcn/211963/factsheet/en. Accessed 8 Sept 2019 es_ES
dc.description.references Model Execution Environment. https://mee.cyfronet.pl/. Accessed 30 Sept 2019 es_ES
dc.description.references Kasztelnik M, Coto E, Bubak M et al (2017) Support for Taverna workflows in the VPH-Share cloud platform. Comput Methods Programs Biomed 146:37–46. https://doi.org/10.1016/j.cmpb.2017.05.006 es_ES
dc.description.references Nowakowski P, Bubak M, Bartyński T et al (2018) Cloud computing infrastructure for the VPH community. J Comput Sci 24:169–179. https://doi.org/10.1016/j.jocs.2017.06.012 es_ES
dc.description.references Bubak M, Gubała T, Hose DR et al (2019) Processing complex medical workflows in the EurValve environment. Proceedings of the CompBioMed Conference, 25-27 September, 2019, London, UK. https://www.compbiomed-conference.org/wp-content/uploads/2019/07/CBMC19_paper_62.pdf es_ES
dc.description.references Martí Bonmatí L, Alberich-Bayarri A, García-Martí G et al (2012) Imaging biomarkers, quantitative imaging, and bioengineering. Radiologia 54:269–278. https://doi.org/10.1016/j.rx.2010.12.013 es_ES
dc.description.references Ingham-Dempster T, Walker DC, Corfe BM (2017) An agent-based model of anoikis in the colon crypt displays novel emergent behaviour consistent with biological observations. R Soc Open Sci 4:160858. https://doi.org/10.1098/rsos.160858 es_ES
dc.description.references Richmond P, Walker D, Coakley S, Romano D (2010) High performance cellular level agent-based simulation with FLAME for the GPU. Brief Bioinform 11:334–347. https://doi.org/10.1093/bib/bbp073 es_ES
dc.description.references An EC Research and Innovation Action. http://www.eurvalve.eu/. Accessed 28 Sept 2019 es_ES
dc.description.references Weir P, Ellerweg R, Payne S et al (2018) Go-smart: open-ended, web-based modelling of minimally invasive cancer treatments via a clinical domain approach. arXiv:1803.09166. https://doi.org/10.13140/RG.2.2.30828.46726 es_ES
dc.description.references GoSmart - Generic open-end simulation environment for minimaly invasive cancer treatment. https://gosmart-project.eu/. Accessed 28 Sept 2019 es_ES
dc.description.references Simon T, Hero B, Schulte JH et al (2017) 2017 GPOH guidelines for diagnosis and treatment of patients with neuroblastic tumors. Klin Padiatr 229:147–167. https://doi.org/10.1055/s-0043-103086 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem