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

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dc.contributor.author Macías Gordaliza, Pedro
dc.contributor.author Muñoz Barrutia, María Arrate
dc.contributor.author Via, L.E.
dc.contributor.author Sharpe, Sally
dc.contributor.author Desco Menéndez, Manuel
dc.contributor.author Vaquero López, Juan José
dc.date.accessioned 2020-11-12T11:48:54Z
dc.date.available 2020-11-12T11:48:54Z
dc.date.issued 2019-02-15
dc.identifier.bibliographicCitation Molecular imaging and Biology, 21(1), Feb. 2019, Pp. 19-24
dc.identifier.issn 1536-1632
dc.identifier.issn 1860-2002 (online)
dc.identifier.uri http://hdl.handle.net/10016/31377
dc.description.abstract Purpose: 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).
dc.description.sponsorship The 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)
dc.format.extent 6
dc.language.iso eng
dc.publisher World Molecular Imaging Society
dc.rights © World Molecular Imaging Society, 2018
dc.subject.other Tuberculosis
dc.subject.other Imaging biomarker
dc.subject.other Lung segmentation
dc.subject.other Computer tomography
dc.subject.other Macaque model
dc.subject.other Antibiotic agent
dc.subject.other Biological Marker
dc.subject.other Isoniazid
dc.subject.other Pyrazinamide
dc.subject.other Rifampicin
dc.subject.other Algorithm
dc.subject.other Animal experiment
dc.subject.other Animal model
dc.subject.other Animal tissue
dc.subject.other Antibiotic therapy
dc.subject.other Article
dc.subject.other Controlled study
dc.subject.other Disease Burden
dc.subject.other Lung parenchyma
dc.subject.other Lung tuberculosis
dc.subject.other Macaca
dc.subject.other Male
dc.subject.other Nonhuman
dc.subject.other Pathological tissue
dc.subject.other Priority journal
dc.subject.other Treatment response
dc.subject.other X-Ray computed tomography
dc.title Computed tomography-based biomarker for longitudinal assessment of disease Burden in pulmonary tuberculosis
dc.type article
dc.subject.eciencia Biología y Biomedicina
dc.identifier.doi https://doi.org/10.1007/s11307-018-1215-x
dc.rights.accessRights openAccess
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/115337/PREDICT-TB
dc.relation.projectID Gobierno de España. TEC2013-48552-C2-1-R
dc.relation.projectID Gobierno de España. RTC-2015-3772-1
dc.relation.projectID Gobierno de España. TEC2015-73064-EXP
dc.relation.projectID Gobierno de España. TEC2016-78052-R
dc.relation.projectID Comunidad de Madrid. S2013/MIT-3024/TOPUS
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 19
dc.identifier.publicationissue 1
dc.identifier.publicationlastpage 24
dc.identifier.publicationtitle MOLECULAR IMAGING AND BIOLOGY
dc.identifier.publicationvolume 21
dc.identifier.uxxi AR/0000024255
dc.contributor.funder Comunidad de Madrid
dc.contributor.funder European Commission
dc.contributor.funder Ministerio de Economía y Competitividad (España)
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