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Repeatability and Sample Size Assessment Associated with Computed Tomography-Based Lung Density Metrics.

ABSTRACT: Density-based metrics assess severity of lung disease but vary with lung inflation and method of scanning. The aim of this study was to evaluate the repeatability of single center, CT-based density metrics of the lung in a normal population and assess study sample sizes needed to detect meaningful changes in lung density metrics when scan parameters and volumes are tightly controlled.Thirty-seven subjects (normal smokers and non-smokers) gave consent to have randomly assigned repeated, breath-held scans at either inspiration (90% vital capacity: TLC) or expiration (20% vital capacity: FRC). Repeated scans were analyzed for: mean lung density (MLD), 15(th) percentile point of the density histogram (P15), low attenuation areas (LAA) and alpha (fractal measure of hole size distribution). Using inter-subject differences and previously reported bias, sample size was estimated from month or yearly change in density metrics obtained from published literature (i.e. meaningful change).Inter-scan difference measurements were small for density metrics (ICC > 0.80) and average ICCs for whole lung alpha-910 and alpha-950 were 0.57 and 0.64, respectively. Power analyses demonstrated that, under the control conditions with minimal extrinsic variation, population sizes needed to detect meaningful changes in density measures for TLC or FRC repeated scans ranged from a few (20-40) to a few hundred subjects, respectively.A meaningful sample size was predicted from this study using volume-controlled normal subjects in a controlled imaging environment. Under proper breath-hold conditions, high repeatability was obtained in cohorts of normal smokers and non-smokers.

SUBMITTER: Iyer KS

PROVIDER: S-EPMC4278434 | biostudies-other | 2014

REPOSITORIES: biostudies-other

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Project description:ObjectiveChest computed tomography (CT) imaging is being increasingly used for potential lung donor assessment. However, the efficacy of CT imaging in this setting remains unknown. We hypothesize that chest CT imaging independently affects the decision-making process in donor lung utilization.MethodsWe conducted a retrospective cohort study of all adult donation after brain death donors managed through our local organ procurement organization from June 2011 to November 2016. An experienced thoracic radiologist independently reviewed donor chest CT and chest x-ray images in a blinded, standardized manner to determine the presence of structural lung disease (eg, emphysema, interstitial lung disease [ILD]) and acute abnormalities (eg, traumatic lung injury [TLI]). Distinct models of lung utilization were fit to groups with initial partial pressure of oxygen (iPaO2) ≤300 mm Hg (suboptimal) and iPaO2 >300 mm Hg (optimal).ResultsThe organ procurement organization managed 753 donors during the study period, with a lung utilization rate ([lung donors/all organ donors] × 100) of 36.5% (275 of 753). Four hundred forty-five (59.1%) donors received chest CT imaging, revealing emphysema (13.7%), ILD (2.5%), and TLI (7.2%). In univariate analysis, findings of TLI (odds ratio [OR], 2.23; 95% confidence interval [CI], 1.08-4.61) were positively associated with lung utilization, whereas findings of emphysema (OR, 0.18; CI, 0.08-0.40) were negatively associated with utilization. In multivariate analysis, CT findings of emphysema (OR, 0.21; CI 0.08-0.54) remained negatively associated with utilization. No potential donors with CT findings of ILD became lung donors. After controlling for chest x-ray findings, chest CT imaging findings of structural lung disease remained negatively associated with utilization (P = .0001). Lung utilization rate in the suboptimal and optimal iPaO2 populations was 35.1% and 41.4%, respectively, and CT findings of emphysema had a significant association with nonutilization in both groups.ConclusionsIn the evaluation of potential lung donors, chest CT imaging findings of structural lung disease, such as emphysema and ILD, have a significant negative association with lung utilization. Our findings suggest that chest CT imaging might be an important adjunct to conventional lung donor assessment criteria.

Project description:Background and Objectives: Women with gynecological cancers constitute a high-risk cohort for loss of bone density. International guidance stipulates women undergoing cancer treatments associated with bone loss should have a quantitative assessment of bone density. Access to Dual-energy X-ray Absorptiometry (DXA) is limited. This study aimed to assess the accuracy of opportunistic bone density measurement on staging computed tomography (CT) scans for gynaecological malignancies, in comparison to the gold standard DXA. Materials and Methods: Women with a staging CT scan of the abdomen and pelvis for a new diagnosis of gynecological cancer were recruited. DXA was performed within 6 weeks of treatment for gynaecological cancer. Lumbar bone density was measured by CT attenuation values, in Hounsfield units (HU), of the anterior trabecular region. Correlations between CT and DXA parameters were analysed. Receiver Operating Characteristic(ROC) curves for diagnosis of low bone density and osteoporosis were analysed. Results: Final cohort included 48 of 50 women recruited. There was good diagnostic accuracy for abnormal bone density and osteoporosis, with areas under the ROC curve at L1 of 0.77 (p = 0.002) and 0.80 (p = 0.020) respectively. CT-HU of 170-190 yielded sensitivities of 87-90%, positive predictive values of 75-84% and negative predictive values of 71-75% for the diagnosis of low bone mineral density. CT-HU of 90-110 yielded specificities of 85-93% for the diagnosis of osteoporosis. Moderate correlations were found between CT-HU and both DXA T-scores and diagnostic categories. Conclusions: This is the first study to assess the opportunistic application of CT in the assessment of bone health in women with gynaecological cancer, a cohort at high-risk of osteoporosis. The correlation between bone density assessment in CT-HU and DXA, and strong AUC values for the diagnosis of low bone density (0.77) and osteoporosis (0.80) support this pragmatic solution in resolving the care-gap in cancer treatment-induced bone loss, often associated with poor access to DXA.

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Repeatability and Sample Size Assessment Associated with Computed Tomography-Based Lung Density Metrics.

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