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

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

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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

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Title: PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers
Author: Martí-Bonmatí, Luis Alberich Bayarri, Ángel Ladenstein, Ruth Blanquer Espert, Ignacio Segrelles Quilis, José Damián 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
UPV Unit: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Issued date:
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 ...[+]
Subjects: Artificial intelligence , Biomarkers (tumour) , Cloud computing , Diffuse intrinsic pontine glioma , Neuroblastoma
Copyrigths: Reconocimiento (by)
Source:
European Radiology Experimental. (eissn: 2509-9280 )
DOI: 10.1186/s41747-020-00150-9
Publisher:
Springer
Publisher version: https://doi.org/10.1186/s41747-020-00150-9
Project ID:
info:eu-repo/grantAgreement/EC/H2020/826494/EU/PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers/
Thanks:
Horizon 2020 project (RIA, topic SC1-DTH-07-2018)
Type: Artículo

References

Porter ME, Teisberg EO (2006) Redefining health care : creating value-based competition on results. Harvard Business School Press, Boston

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

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 [+]
Porter ME, Teisberg EO (2006) Redefining health care : creating value-based competition on results. Harvard Business School Press, Boston

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

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

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

Martí-Bonmatí L, Alberich-Bayarri A (eds) (2018) Imaging biomarkers : development and clinical integration. Springer, Heidelberg

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

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

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

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

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

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

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

Bhatnagar SN (2012) An audit of malignant solid tumors in infants and neonates. J Neonatal Surg 1:5 PMID: 26023364; PMCID: PMC4420305

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

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

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

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

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

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

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

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

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

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

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

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

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

Rimrock. Robust Remote Process Controller. https://submit.plgrid.pl/. Accessed 28 Sept 2019

PL-Grid Data service. https://data.plgrid.pl/?locale=en. Accessed 28 Sept 2019

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

Model Execution Environment. https://mee.cyfronet.pl/. Accessed 30 Sept 2019

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

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

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

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

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

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

An EC Research and Innovation Action. http://www.eurvalve.eu/. Accessed 28 Sept 2019

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

GoSmart - Generic open-end simulation environment for minimaly invasive cancer treatment. https://gosmart-project.eu/. Accessed 28 Sept 2019

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

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