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Computed tomography-based biomarker for longitudinal assessment of disease Burden in pulmonary tuberculosis

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dc.affiliation.dptoUC3M. Departamento de Bioingenieríaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Biomedical Imaging and Instrumentation Groupes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: BSEL - Laboratorio de Ciencia e Ingeniería Biomédicaes
dc.contributor.authorMacías Gordaliza, Pedro
dc.contributor.authorMuñoz Barrutia, María Arrate
dc.contributor.authorVia, L.E.
dc.contributor.authorSharpe, Sally
dc.contributor.authorDesco Menéndez, Manuel
dc.contributor.authorVaquero López, Juan José
dc.contributor.funderComunidad de Madrides
dc.contributor.funderEuropean Commissionen
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2020-11-12T11:48:54Z
dc.date.available2020-11-12T11:48:54Z
dc.date.issued2019-02-15
dc.description.abstractPurpose: Computed tomography (CT) images enable capturing specific manifestations of tuberculosis (TB) that are undetectable using common diagnostic tests, which suffer from limited specificity. In this study, we aimed to automatically quantify the burden of Mycobacterium tuberculosis (Mtb) using biomarkers extracted from x-ray CT images. Procedures: Nine macaques were aerosol-infected with Mtb and treated with various antibiotic cocktails. Chest CT scans were acquired in all animals at specific times independently of disease progression. First, a fully automatic segmentation of the healthy lungs from the acquired chest CT volumes was performed and air-like structures were extracted. Next, unsegmented pulmonary regions corresponding to damaged parenchymal tissue and TB lesions were included. CT biomarkers were extracted by classification of the probability distribution of the intensity of the segmented images into three tissue types: (1) Healthy tissue, parenchyma free from infection; (2) soft diseased tissue, and (3) hard diseased tissue. The probability distribution of tissue intensities was assumed to follow a Gaussian mixture model. The thresholds identifying each region were automatically computed using an expectation-maximization algorithm. Results: The estimated longitudinal course of TB infection shows that subjects that have followed the same antibiotic treatment present a similar response (relative change in the diseased volume) with respect to baseline. More interestingly, the correlation between the diseased volume (soft tissue + hard tissue), which was manually delineated by an expert, and the automatically extracted volume with the proposed method was very strong (R2 ≈ 0.8).en
dc.description.sponsorshipThe research leading to these results received funding from the Innovative Medicines Initiative (www.imi.europa.eu) Joint Undertaking under grant agreement no. 115337, whose resources comprise funding from EU FP7/2007-2013 and EFPIA companies in-kind contribution. This study was partially funded by projects TEC2013-48552-C2-1-R, RTC-2015-3772-1, TEC2015-73064-EXP and TEC2016-78052-R from the Spanish Ministry of Economy, Industry and Competitiveness (MEIC), TOPUS S2013/MIT-3024 project from the regional government of Madrid and by the Department of Health, UK. LEV is funded by the Intramural Research Program of NIAID, NIH. The CNIC is supported by the MEIC and the Pro CNIC Foundation and is a Severo Ochoa Center of Excellence (SEV-2015-0505)es
dc.format.extent6es
dc.identifier.bibliographicCitationMolecular imaging and Biology, 21(1), Feb. 2019, Pp. 19-24en
dc.identifier.doihttps://doi.org/10.1007/s11307-018-1215-x
dc.identifier.issn1536-1632
dc.identifier.issn1860-2002 (online)
dc.identifier.publicationfirstpage19es
dc.identifier.publicationissue1es
dc.identifier.publicationlastpage24es
dc.identifier.publicationtitleMOLECULAR IMAGING AND BIOLOGYen
dc.identifier.publicationvolume21es
dc.identifier.urihttps://hdl.handle.net/10016/31377
dc.identifier.uxxiAR/0000024255
dc.language.isoengen
dc.publisherWorld Molecular Imaging Societyen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/FP7/115337/PREDICT-TBen
dc.relation.projectIDGobierno de España. TEC2013-48552-C2-1-Res
dc.relation.projectIDGobierno de España. RTC-2015-3772-1es
dc.relation.projectIDGobierno de España. TEC2015-73064-EXPes
dc.relation.projectIDGobierno de España. TEC2016-78052-Res
dc.relation.projectIDComunidad de Madrid. S2013/MIT-3024/TOPUSes
dc.rights© World Molecular Imaging Society, 2018en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaBiología y Biomedicinaes
dc.subject.otherTuberculosisen
dc.subject.otherImaging biomarkeren
dc.subject.otherLung segmentationen
dc.subject.otherComputer tomographyen
dc.subject.otherMacaque modelen
dc.subject.otherAntibiotic agenten
dc.subject.otherBiological Markeren
dc.subject.otherIsoniaziden
dc.subject.otherPyrazinamideen
dc.subject.otherRifampicinen
dc.subject.otherAlgorithmen
dc.subject.otherAnimal experimenten
dc.subject.otherAnimal modelen
dc.subject.otherAnimal tissueen
dc.subject.otherAntibiotic therapyen
dc.subject.otherArticleen
dc.subject.otherControlled studyen
dc.subject.otherDisease Burdenen
dc.subject.otherLung parenchymaen
dc.subject.otherLung tuberculosisen
dc.subject.otherMacacaen
dc.subject.otherMaleen
dc.subject.otherNonhumanen
dc.subject.otherPathological tissueen
dc.subject.otherPriority journalen
dc.subject.otherTreatment responseen
dc.subject.otherX-Ray computed tomographyen
dc.titleComputed tomography-based biomarker for longitudinal assessment of disease Burden in pulmonary tuberculosisen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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