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Analysis of 18FDG PET/CT Imaging as a Tool for Studying Mycobacterium tuberculosis Infection and Treatment in Non-human Primates
Published on: September 5, 2017
A valuable computed tomography-based new diagnostic tool for severe chest lesions in active pulmonary tuberculosis:
Kui Li1,2, Zicheng Jiang2, Yanan Zhu3
1Department of Infectious Diseases, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, 710061, Shaanxi, China.
Abstract:
The objective of the present investigation was to explore the influencing factors and value of computed tomography (CT) for diagnosing severe chest lesions in active pulmonary tuberculosis (APTB). This retrospective investigation included 463 patients diagnosed with APTB. Relevant clinical features were collected. Patients were assigned to mild/moderate group or advanced group depending on the lesion severity on chest CT, severe chest CT lesion refers to the moderately dense or less diffuse lesion that exceeds the total volume of one lung, or the dense fusion lesion greater than one third of the volume of one lung, or the lesion with cavity diameter ≥4 cm. Independent risk factors for severe lesions were determined by univariate and multivariate logistic regression analyses, and the diagnostic efficiency of the risk factors was assessed by receiver operating characteristic curve (ROC). Chest CT demonstrated that there were 285 (61.56%) cases with severe lesions; multivariate Logistic regression analysis showed dust exposure [odds ratio (OR) = 4.108, 95% confidence interval (CI) 2.416-6.986], patient classification (OR = 1.792, 95% CI 1.067-3.012), age (OR = 1.018, 95% CI 1.005-1.030), and albumin-globulin ratio (OR = 0.179, 95% CI 0.084-0.383) to be independently correlated with severe lesions on chest CT. ROC curve analysis showed the cutoff values of age, albumin-globulin ratio and combined score to be 39 years, 0.918 and -0.085, respectively. The predictive value of combined score area under the curve 0.753 (95% CI 0.708-0.798) was higher than that of any single factor. The combined score of these four factors further improved the predictive efficacy.
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