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

publication date

  • February 2019

start page

  • 19

end page

  • 24

issue

  • 1

volume

  • 21

International Standard Serial Number (ISSN)

  • 1536-1632

Electronic International Standard Serial Number (EISSN)

  • 1860-2002

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).

keywords

  • tuberculosis; imaging biomarker; lung segmentation; computer tomography; macaque model; antibiotic agent; biological marker; isoniazid; pyrazinamide; rifampicin; algorithm; animal experiment; animal model; animal tissue; antibiotic therapy; article; controlled study; disease burden; lung parenchyma; lung tuberculosis;macaca; male; nonhuman; pathological tissue; priority journal; treatment response; x-ray computed tomography