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Volume Correction in Computed Tomography Densitometry for Follow-up Studies on Pulmonary Emphysema

¹ Division of Image Processing, Department of Radiology, and ² Department of Medical Statistics, Leiden University Medical Center, Leiden, The Netherlands; ³ Department of Respiratory Medicine, Gentofte University Hospital, Hellerup, Denmark; ⁴ Department of Respiratory Medicine, University Hospital Birmingham, Birmingham, United Kingdom; ⁵ Department of Pulmonary Medicine, Malmö University Hospital, Malmö, Sweden; ⁶ Pulmonology Division, University Hospital Zürich, Zürich, Switzerland; and ⁷ Department of Pulmonology, Leiden University Medical Center, Leiden, The Netherlands

Correspondence and requests for reprints should be addressed to Berend C. Stoel, Ph.D., Leiden University Medical Center, Albinusdreef 2, 2333 AA Leiden, The Netherlands. E-mail: B.C.Stoel{at}lumc.nl

ABSTRACT

Lung densitometry in drug evaluation trials can be confounded by changes in inspiration levels between computed tomography (CT) scans, limiting its sensitivity to detect changes over time. Therefore our aim was to explore whether the sensitivity of lung densitometry could be improved by correcting the measurements for changes in lung volume, based on the estimated relation between density (as measured with the 15th percentile point) and lung volume. We compared four correction methods, using CT data of 143 patients from five European countries. Patients were scanned, generally twice per visit, at baseline and after 2.5 years. The methods included one physiological model and three linear mixed-effects models using a volume–density relation: (1) estimated over the entire population with one scan per visit (model A) and two scans per visit (model B); and (2) estimated for each patient individually (model C). Both log-transformed and original volume and density values were evaluated and the differences in goodness-of-fit between methods were tested. Model C fitted best (P < 0.0001, P < 0.0001, and P = 0.064), when two scans were available. The most consistent progression estimation was obtained between sites, when both volume and density were log-transformed. Sensitivity was improved using repeated CT scans by applying volume correction to individual patient data. Volume correction reduces the variability in progression estimation by a factor of two, and is therefore recommended.

Key Words: densitometry • computed tomography • statistical models • clinical trials

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