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. 2014 Jan;21(1):3-10.
doi: 10.1016/j.acra.2013.09.014.

Impact of body mass index on the detection of radiographic localized pleural thickening

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Impact of body mass index on the detection of radiographic localized pleural thickening

Theodore C Larson et al. Acad Radiol. 2014 Jan.

Abstract

Rationale and objectives: Subpleural fat can be difficult to distinguish from localized pleural thickening (LPT), a marker of asbestos exposure, on chest radiographs. The aims of this study were to examine the influence of body mass index (BMI) on the performance of radiograph readers when classifying LPT and to model the risk of false test results with varying BMI.

Materials and methods: Subjects (n = 200) were patients being screened or treated for asbestos-related health outcomes. A film chest radiograph, a digital chest radiograph, and a high-resolution computed tomography (HRCT) chest scan were collected from each subject. All radiographs were independently read by seven B readers and scored using the International Labour Office system. HRCT scans, read by three experienced thoracic radiologists, served as the gold standard for the presence of LPT. We calculated measures of radiograph reader performance, including sensitivity and specificity, for each image modality. We also used logistic regression to estimate the probability of a false-positive and a false-negative result while controlling for covariates.

Results: The proportion of false-positive readings correlated with BMI. While controlling for covariates, regression modeling showed the probability of a false-positive result increased with increasing BMI category, younger age, not having pleural calcification, and among subjects not reporting occupational or household contact asbestos exposure.

Conclusions: Clinicians should be cautious when evaluating radiographs of younger obese persons for the presence of asbestos-related pleural plaque, particularly in populations having an anticipated low or background prevalence of LPT.

Keywords: Radiograph; pneumoconiosis; predictive values.

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