{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T18:57:33Z","timestamp":1790621853881,"version":"4.1.0"},"reference-count":53,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T00:00:00Z","timestamp":1666569600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","physicamedica.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Physica Medica"],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1016\/j.ejmp.2022.10.009","type":"journal-article","created":{"date-parts":[[2022,10,27]],"date-time":"2022-10-27T00:19:01Z","timestamp":1666829941000},"page":"138-146","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":13,"special_numbering":"C","title":["Pre-trial quality assurance of diffusion-weighted MRI for radiomic analysis and the role of harmonisation"],"prefix":"10.1016","volume":"103","author":[{"given":"Zelda","family":"Paquier","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shih-Li","family":"Chao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giacomo","family":"Bregni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ana Veron","family":"Sanchez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Guiot","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer","family":"Dhont","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akos","family":"Gulyban","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hugo","family":"Levillain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Sclafani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nick","family":"Reynaert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Antonietta","family":"Bali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ejmp.2022.10.009_b0005","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1007\/s13244-018-0624-3","article-title":"Clinical applications of diffusion weighted imaging in neuroradiology","volume":"9","author":"Drake-P\u00e9rez","year":"2018","journal-title":"Insights Imaging"},{"issue":"1","key":"10.1016\/j.ejmp.2022.10.009_b0010","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.eururo.2015.08.052","article-title":"PI-RADS prostate imaging \u2013 reporting and data system: 2015, Version 2","volume":"69","author":"Weinreb","year":"2016","journal-title":"Eur Urol"},{"key":"10.1016\/j.ejmp.2022.10.009_b0015","doi-asserted-by":"crossref","first-page":"27","DOI":"10.4103\/2156-7514.81294","article-title":"Diffusion-weighted magnetic resonance imaging: what makes water run fast or slow?","volume":"1","author":"Fornasa","year":"2011","journal-title":"J Clin Imaging Sci"},{"key":"10.1016\/j.ejmp.2022.10.009_b0020","doi-asserted-by":"crossref","first-page":"1622","DOI":"10.2214\/AJR.06.1403","article-title":"Diffusion-weighted MRI in the body: applications and challenges in oncology","volume":"188","author":"Koh","year":"2007","journal-title":"Am J Roentgenol"},{"issue":"2","key":"10.1016\/j.ejmp.2022.10.009_b0025","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1593\/neo.81328","article-title":"Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations","volume":"11","author":"Padhani","year":"2009","journal-title":"Neoplasia"},{"issue":"2","key":"10.1016\/j.ejmp.2022.10.009_b0030","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s10238-020-00612-9","article-title":"Systemic treatment of the metastatic renal cell carcinoma: usefulness of the apparent diffusion coefficient of diffusion-weighted MRI in prediction of early therapeutic response","volume":"20","author":"Mytsyk","year":"2020","journal-title":"Clin Exp Med"},{"key":"10.1016\/j.ejmp.2022.10.009_b0035","doi-asserted-by":"crossref","first-page":"e3458","DOI":"10.1002\/nbm.3458","article-title":"Diffusion MRI in early cancer therapeutic response assessment","volume":"30","author":"Galb\u00e1n","year":"2017","journal-title":"NMR Biomed"},{"key":"10.1016\/j.ejmp.2022.10.009_b0040","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: images are more than pictures, they are data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"issue":"3","key":"10.1016\/j.ejmp.2022.10.009_b0045","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1148\/radiol.2017170273","article-title":"Radiomics based on adapted diffusion kurtosis imaging helps to clarify most mammographic findings suspicious for cancer","volume":"287","author":"Bickelhaupt","year":"2018","journal-title":"Radiology"},{"key":"10.1016\/j.ejmp.2022.10.009_b0050","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.ejmp.2021.05.010","article-title":"Artificial Intelligence in magnetic Resonance guided Radiotherapy: medical and physical considerations on state of art and future perspectives","volume":"85","author":"Cusumano","year":"2021","journal-title":"Phys Med"},{"key":"10.1016\/j.ejmp.2022.10.009_b0055","doi-asserted-by":"crossref","first-page":"21","DOI":"10.4329\/wjr.v8.i1.21","article-title":"Diffusion-weighted magnetic resonance imaging in cancer: reported apparent diffusion coefficients, in-vitro and in-vivo reproducibility","volume":"8","author":"Jafar","year":"2016","journal-title":"World J Radiol"},{"key":"10.1016\/j.ejmp.2022.10.009_b0060","doi-asserted-by":"crossref","first-page":"R150","DOI":"10.1088\/0031-9155\/61\/13\/R150","article-title":"Applications and limitations of radiomics","volume":"61","author":"Yip","year":"2016","journal-title":"Phys Med Biol"},{"issue":"3","key":"10.1016\/j.ejmp.2022.10.009_b0065","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1038\/nrclinonc.2016.162","article-title":"Imaging biomarker roadmap for cancer studies","volume":"14","author":"O'Connor","year":"2017","journal-title":"Nat Rev Clin Oncol"},{"issue":"7","key":"10.1016\/j.ejmp.2022.10.009_b0070","doi-asserted-by":"crossref","first-page":"e101","DOI":"10.1002\/jmri.26518","article-title":"Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials","volume":"49","author":"Shukla\u2010Dave","year":"2019","journal-title":"J Magn Reson Imaging"},{"issue":"8","key":"10.1016\/j.ejmp.2022.10.009_b0075","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.2967\/jnumed.117.199935","article-title":"A postreconstruction harmonization method for multicenter radiomic studies in PET","volume":"59","author":"Orlhac","year":"2018","journal-title":"J Nucl Med"},{"key":"10.1016\/j.ejmp.2022.10.009_b0080","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1148\/radiol.2019182023","article-title":"Validation of A method to compensate multicenter effects affecting CT radiomics","volume":"291","author":"Orlhac","year":"2019","journal-title":"Radiology"},{"issue":"4","key":"10.1016\/j.ejmp.2022.10.009_b0085","doi-asserted-by":"crossref","first-page":"2272","DOI":"10.1007\/s00330-020-07284-9","article-title":"How can we combat multicenter variability in MR radiomics? Validation of a correction procedure","volume":"31","author":"Orlhac","year":"2021","journal-title":"Eur Radiol"},{"issue":"3","key":"10.1016\/j.ejmp.2022.10.009_b0090","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1007\/s10334-020-00892-y","article-title":"A radiomics pipeline dedicated to Breast MRI: validation on a multi-scanner phantom study","volume":"34","author":"Saint Martin","year":"2021","journal-title":"Magn Reson Mater Physics, Biol Med"},{"key":"10.1016\/j.ejmp.2022.10.009_b0095","doi-asserted-by":"crossref","unstructured":"Ibrahim A, Refaee T, Leijenaar RTH, Primakov S, Hustinx R, Mottaghy FM, et al. The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset. PLoS One 2021;16:e0251147. doi: 10.1371\/journal.pone.0251147.","DOI":"10.1371\/journal.pone.0251147"},{"issue":"4","key":"10.1016\/j.ejmp.2022.10.009_b0100","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1080\/0284186X.2020.1871067","article-title":"Rationale and design of REGINA, a phase II trial of neoadjuvant regorafenib, nivolumab, and short-course radiotherapy in stage II and III rectal cancer","volume":"60","author":"Bregni","year":"2021","journal-title":"Acta Oncol (Madr)"},{"key":"10.1016\/j.ejmp.2022.10.009_b0105","unstructured":"Quantitative Imaging Biomarkers Alliance. QIBA Profile\u00a0: Diffusion-Weighted Magnetic Resonance Imaging (DWI) 2019."},{"key":"10.1016\/j.ejmp.2022.10.009_b0110","doi-asserted-by":"crossref","first-page":"S837","DOI":"10.1016\/S0167-8140(15)41525-7","article-title":"EP-1533: ICE-Studio \u2013 an interactive visual research tool for image analysis","volume":"115","author":"Nyholm","year":"2015","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.ejmp.2022.10.009_b0115","unstructured":"Zwanenburg A, Leger S, Valli\u00e8res M, L\u00f6ck S. Image biomarker standardisation initiative."},{"key":"10.1016\/j.ejmp.2022.10.009_b0120","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.neuroimage.2017.08.047","article-title":"Harmonization of multi-site diffusion tensor imaging data","volume":"161","author":"Fortin","year":"2017","journal-title":"Neuroimage"},{"key":"10.1016\/j.ejmp.2022.10.009_b0125","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.neuroimage.2017.11.024","article-title":"Harmonization of cortical thickness measurements across scanners and sites","volume":"167","author":"Fortin","year":"2018","journal-title":"Neuroimage"},{"key":"10.1016\/j.ejmp.2022.10.009_b0130","doi-asserted-by":"crossref","first-page":"130","DOI":"10.21037\/tcr.2019.01.14","article-title":"Variability of radiomic features extracted from multi-b-value diffusion-weighted images in hepatocellular carcinoma","volume":"8","author":"Zhang","year":"2019","journal-title":"Transl Cancer Res"},{"key":"10.1016\/j.ejmp.2022.10.009_b0135","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.clinimag.2022.01.002","article-title":"Repeatability, robustness, and reproducibility of texture features on 3 Tesla liver MRI","volume":"83","author":"Prabhu","year":"2022","journal-title":"Clin Imaging"},{"issue":"3","key":"10.1016\/j.ejmp.2022.10.009_b0140","doi-asserted-by":"crossref","first-page":"2030","DOI":"10.1007\/s00330-021-08282-1","article-title":"Precision of MRI radiomics features in the liver and hepatocellular carcinoma","volume":"32","author":"Carbonell","year":"2022","journal-title":"Eur Radiol"},{"issue":"03","key":"10.1016\/j.ejmp.2022.10.009_b0145","doi-asserted-by":"crossref","DOI":"10.1117\/1.JMI.8.3.033505","article-title":"Quality control of radiomic features using 3D-printed CT phantoms","volume":"8","author":"Mahmood","year":"2021","journal-title":"J Med Imaging"},{"key":"10.1016\/j.ejmp.2022.10.009_b0150","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.21105\/joss.01026","article-title":"Pingouin: statistics in Python","volume":"3","author":"Vallat","year":"2018","journal-title":"J Open Source Softw"},{"issue":"3","key":"10.1016\/j.ejmp.2022.10.009_b0155","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1002\/jmri.25196","article-title":"Diffusion-weighted imaging outside the brain: Consensus statement from an ISMRM-sponsored workshop","volume":"44","author":"Taouli","year":"2016","journal-title":"J Magn Reson Imaging"},{"issue":"4","key":"10.1016\/j.ejmp.2022.10.009_b0160","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1002\/jmri.22363","article-title":"Diffusion coefficient measurement using a temperature-controlled fluid for quality control in multicenter studies","volume":"34","author":"Chenevert","year":"2011","journal-title":"J Magn Reson Imaging"},{"key":"10.1016\/j.ejmp.2022.10.009_b0165","doi-asserted-by":"crossref","first-page":"102963","DOI":"10.1016\/j.ebiom.2020.102963","article-title":"Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging","volume":"59","author":"Roy","year":"2020","journal-title":"EBioMedicine"},{"key":"10.1016\/j.ejmp.2022.10.009_b0170","first-page":"2","article-title":"Robustness of radiomic features in magnetic resonance imaging: review and a phantom study","author":"Cattell","year":"2019","journal-title":"Vis Comput Ind Biomed Art"},{"key":"10.1016\/j.ejmp.2022.10.009_b0175","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.radonc.2019.01.011","article-title":"Feasibility and accuracy of quantitative imaging on a 1.5 T MR-linear accelerator","volume":"133","author":"Kooreman","year":"2019","journal-title":"Radiother Oncol"},{"issue":"1","key":"10.1016\/j.ejmp.2022.10.009_b0180","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1002\/jmri.24956","article-title":"Quality assurance multicenter comparison of different MR scanners for quantitative diffusion-weighted imaging","volume":"43","author":"Belli","year":"2016","journal-title":"J Magn Reson Imaging"},{"issue":"3","key":"10.1016\/j.ejmp.2022.10.009_b0185","doi-asserted-by":"crossref","first-page":"537","DOI":"10.3174\/ajnr.A5025","article-title":"Toward precision and reproducibility of diffusion tensor imaging: a multicenter diffusion phantom and traveling volunteer study","volume":"38","author":"Palacios","year":"2017","journal-title":"Am J Neuroradiol"},{"issue":"6Part1","key":"10.1016\/j.ejmp.2022.10.009_b0190","doi-asserted-by":"crossref","first-page":"2998","DOI":"10.1118\/1.4948997","article-title":"Development of a temperature-controlled phantom for magnetic resonance quality assurance of diffusion, dynamic, and relaxometry measurements","volume":"43","author":"Jerome","year":"2016","journal-title":"Med Phys"},{"key":"10.1016\/j.ejmp.2022.10.009_b0195","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.mri.2020.12.018","article-title":"Quantitative MRI: defining repeatability, reproducibility and accuracy for prostate cancer imaging biomarker development","volume":"77","author":"Wang","year":"2021","journal-title":"Magn Reson Imaging"},{"issue":"4","key":"10.1016\/j.ejmp.2022.10.009_b0200","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1002\/nbm.3269","article-title":"Multi-centre reproducibility of diffusion MRI parameters for clinical sequences in the brain","volume":"28","author":"Grech\u2010Sollars","year":"2015","journal-title":"NMR Biomed"},{"issue":"5","key":"10.1016\/j.ejmp.2022.10.009_b0205","doi-asserted-by":"crossref","first-page":"1238","DOI":"10.1002\/jmri.23825","article-title":"Multi-system repeatability and reproducibility of apparent diffusion coefficient measurement using an ice-water phantom","volume":"37","author":"Malyarenko","year":"2013","journal-title":"J Magn Reson Imaging"},{"issue":"7","key":"10.1016\/j.ejmp.2022.10.009_b0210","doi-asserted-by":"crossref","first-page":"4508","DOI":"10.1002\/mp.15645","article-title":"Conformance of a 3T radiotherapy MRI scanner to the QIBA diffusion profile","volume":"49","author":"Carr","year":"2022","journal-title":"Med Phys"},{"issue":"3","key":"10.1016\/j.ejmp.2022.10.009_b0215","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1002\/mrm.25754","article-title":"Demonstration of nonlinearity bias in the measurement of the apparent diffusion coefficient in multicenter trials","volume":"75","author":"Malyarenko","year":"2016","journal-title":"Magn Reson Med"},{"key":"10.1016\/j.ejmp.2022.10.009_b0220","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.ejmp.2018.09.007","article-title":"Dependence of apparent diffusion coefficient measurement on diffusion gradient direction and spatial position \u2013 a quality assurance intercomparison study of forty-four scanners for quantitative diffusion-weighted imaging","volume":"55","author":"Fedeli","year":"2018","journal-title":"Phys Med"},{"key":"10.1016\/j.ejmp.2022.10.009_b0225","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.ejmp.2021.04.020","article-title":"On the dependence of quantitative diffusion-weighted imaging on scanner system characteristics and acquisition parameters: a large multicenter and multiparametric phantom study with unsupervised clustering analysis","volume":"85","author":"Fedeli","year":"2021","journal-title":"Phys Med"},{"key":"10.1016\/j.ejmp.2022.10.009_b0230","doi-asserted-by":"crossref","first-page":"1870","DOI":"10.21037\/qims-20-865","article-title":"Quantitative assessment of acquisition imaging parameters on MRI radiomics features: a prospective anthropomorphic phantom study using a 3D\u2013T2W-TSE sequence for MR-guided-radiotherapy","volume":"11","author":"Yuan","year":"2021","journal-title":"Quant Imaging Med Surg"},{"key":"10.1016\/j.ejmp.2022.10.009_b0235","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1002\/mp.13322","article-title":"Repeatability of 18 F- FDG PET radiomic features: A phantom study to explore sensitivity to image reconstruction settings, noise, and delineation method","volume":"46","author":"Pfaehler","year":"2019","journal-title":"Med Phys"},{"key":"10.1016\/j.ejmp.2022.10.009_b0240","doi-asserted-by":"crossref","first-page":"798.e13","DOI":"10.1016\/j.crad.2020.06.024","article-title":"Radiomics in diffusion data: a test\u2013retest, inter- and intra-reader DWI phantom study","volume":"75","author":"Dreher","year":"2020","journal-title":"Clin Radiol"},{"issue":"9","key":"10.1016\/j.ejmp.2022.10.009_b0245","doi-asserted-by":"crossref","first-page":"842","DOI":"10.3390\/jpm11090842","article-title":"Making radiomics more reproducible across scanner and imaging protocol variations: a review of harmonization methods","volume":"11","author":"Mali","year":"2021","journal-title":"J Pers Med"},{"key":"10.1016\/j.ejmp.2022.10.009_b0250","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.ejmp.2021.03.003","article-title":"Current applications of deep-learning in neuro-oncological MRI","volume":"83","author":"Zegers","year":"2021","journal-title":"Phys Med"},{"issue":"11","key":"10.1016\/j.ejmp.2022.10.009_b0255","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1097\/RLI.0000000000000180","article-title":"Measuring computed tomography scanner variability of radiomics features","volume":"50","author":"Mackin","year":"2015","journal-title":"Invest Radiol"},{"issue":"3","key":"10.1016\/j.ejmp.2022.10.009_b0260","doi-asserted-by":"crossref","first-page":"1648","DOI":"10.1002\/mp.15491","article-title":"The impact of phantom design and material-dependence on repeatability and reproducibility of CT-based radiomics features","volume":"49","author":"Li","year":"2022","journal-title":"Med Phys"},{"key":"10.1016\/j.ejmp.2022.10.009_b0265","doi-asserted-by":"crossref","first-page":"2023","DOI":"10.1002\/mp.14045","article-title":"Physical imaging phantoms for simulation of tumor heterogeneity in PET, CT, and MRI: an overview of existing designs","volume":"47","author":"Valladares","year":"2020","journal-title":"Med Phys"}],"container-title":["Physica Medica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1120179722020701?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1120179722020701?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T03:52:45Z","timestamp":1761105165000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1120179722020701"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11]]},"references-count":53,"alternative-id":["S1120179722020701"],"URL":"https:\/\/doi.org\/10.1016\/j.ejmp.2022.10.009","relation":{},"ISSN":["1120-1797"],"issn-type":[{"value":"1120-1797","type":"print"}],"subject":[],"published":{"date-parts":[[2022,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Pre-trial quality assurance of diffusion-weighted MRI for radiomic analysis and the role of harmonisation","name":"articletitle","label":"Article Title"},{"value":"Physica Medica","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejmp.2022.10.009","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}]}}