Variation of Densitometry on Computed Tomography in COPD – Influence of Different Software Tools

Objectives Quantitative multidetector computed tomography (MDCT) as a potential biomarker is increasingly used for severity assessment of emphysema in chronic obstructive pulmonary disease (COPD). Aim of this study was to evaluate the user-independent measurement variability between five different fully-automatic densitometry software tools. Material and Methods MDCT and full-body plethysmography incl. forced expiratory volume in 1s and total lung capacity were available for 49 patients with advanced COPD (age = 64±9 years, forced expiratory volume in 1s = 31±6% predicted). Measurement variation regarding lung volume, emphysema volume, emphysema index, and mean lung density was evaluated for two scientific and three commercially available lung densitometry software tools designed to analyze MDCT from different scanner types. Results One scientific tool and one commercial tool failed to process most or all datasets, respectively, and were excluded. One scientific and another commercial tool analyzed 49, the remaining commercial tool 30 datasets. Lung volume, emphysema volume, emphysema index and mean lung density were significantly different amongst these three tools (p<0.001). Limits of agreement for lung volume were [−0.195, −0.052l], [−0.305, −0.131l], and [−0.123, −0.052l] with correlation coefficients of r = 1.00 each. Limits of agreement for emphysema index were [−6.2, 2.9%], [−27.0, 16.9%], and [−25.5, 18.8%], with r = 0.79 to 0.98. Correlation of lung volume with total lung capacity was good to excellent (r = 0.77 to 0.91, p<0.001), but segmented lung volume (6.7±1.3 – 6.8±1.3l) were significantly lower than total lung capacity (7.7±1.7l, p<0.001). Conclusions Technical incompatibilities hindered evaluation of two of five tools. The remaining three showed significant measurement variation for emphysema, hampering quantitative MDCT as a biomarker in COPD. Follow-up studies should currently use identical software, and standardization efforts should encompass software as well.

[1]  Mulugeta Gebregziabher,et al.  Reproducibility of noncalcified coronary artery plaque burden quantification from coronary CT angiography across different image analysis platforms. , 2014, AJR. American journal of roentgenology.

[2]  D. Altman,et al.  STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.

[3]  J E Cotes,et al.  Lung volumes and forced ventilatory flows. Report Working Party Standardization of Lung Function Tests, European Community for Steel and Coal. Official Statement of the European Respiratory Society. , 1993, The European respiratory journal. Supplement.

[4]  David A Lynch,et al.  Quantitative Computed Tomography in Chronic Obstructive Pulmonary Disease , 2013, Journal of thoracic imaging.

[5]  H. Kauczor,et al.  Longitudinal quantitative low-dose CT in COPD: ready for use? , 2013, The Lancet. Respiratory medicine.

[6]  J. Gaughan,et al.  Total lung capacity by plethysmography and high-resolution computed tomography in COPD , 2012, International journal of chronic obstructive pulmonary disease.

[7]  H. Coxson,et al.  Quantitative Computed Tomography of Chronic Obstructive Pulmonary Disease1 , 2005 .

[8]  P. Paré,et al.  A quantification of the lung surface area in emphysema using computed tomography. , 1999, American journal of respiratory and critical care medicine.

[9]  Jason C Woods,et al.  Effects of CT section thickness and reconstruction kernel on emphysema quantification relationship to the magnitude of the CT emphysema index. , 2010, Academic radiology.

[10]  Benjamin M. Smith,et al.  Establishing Normal Reference Values in Quantitative Computed Tomography of Emphysema , 2013, Journal of thoracic imaging.

[11]  F. Martinez,et al.  Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. , 2007, American journal of respiratory and critical care medicine.

[12]  J. Hankinson,et al.  Standardisation of spirometry , 2005, European Respiratory Journal.

[13]  Bram van Ginneken,et al.  Monitoring of smoking-induced emphysema with CT in a lung cancer screening setting: detection of real increase in extent of emphysema. , 2007, Radiology.

[14]  M. Prokop,et al.  Lung nodule volumetry: segmentation algorithms within the same software package cannot be used interchangeably , 2010, European Radiology.

[15]  P. Gevenois,et al.  Pulmonary emphysema: effect of lung volume on objective quantification at thin-section CT. , 2010, Radiology.

[16]  Geoffrey McLennan,et al.  State of the Art. A structural and functional assessment of the lung via multidetector-row computed tomography: phenotyping chronic obstructive pulmonary disease. , 2006, Proceedings of the American Thoracic Society.

[17]  [Quantification of pulmonary emphysema in multislice-CT using different software tools]. , 2006, RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin.

[18]  D. Postma,et al.  Rate of progression of CT-quantified emphysema in male current and ex-smokers: a follow-up study , 2013, Respiratory Research.

[19]  Jin Mo Goo,et al.  Quantitative analysis of emphysema and airway measurements according to iterative reconstruction algorithms: comparison of filtered back projection, adaptive statistical iterative reconstruction and model-based iterative reconstruction , 2014, European Radiology.

[20]  Hans-Ulrich Kauczor,et al.  Multi-detector CT of the Chest: Influence of Dose Onto Quantitative Evaluation of Severe Emphysema A Simulation Study , 2006, Journal of computer assisted tomography.

[21]  A. Dirksen,et al.  Short‐term reproducibility of computed tomography‐based lung density measurements in alpha‐1 antitrypsin deficiency and smokers with emphysema , 2004, Acta radiologica.

[22]  H. Kauczor,et al.  Computed Tomographic Imaging of the Airways in COPD and Asthma , 2011, Journal of thoracic imaging.

[23]  Johan H C Reiber,et al.  Volume correction in computed tomography densitometry for follow-up studies on pulmonary emphysema. , 2008, Proceedings of the American Thoracic Society.

[24]  B. Ginneken,et al.  A comparison of six software packages for evaluation of solid lung nodules using semi-automated volumetry: What is the minimum increase in size to detect growth in repeated CT examinations , 2009, European Radiology.

[25]  E. K. Harris,et al.  Statistical principles underlying analytic goal-setting in clinical chemistry. , 1979, American journal of clinical pathology.

[26]  Christoph Düber,et al.  Automatic Lung Segmentation in MDCT Images , 2011 .

[27]  Jan-Martin Kuhnigk,et al.  Defining the intra-subject variability of whole-lung CT densitometry in two lung cancer screening trials. , 2011, Academic Radiology.

[28]  S. Holm A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

[29]  E. Regan,et al.  Genetic Epidemiology of COPD (COPDGene) Study Design , 2011, COPD.

[30]  G. McLennan,et al.  A randomized study of endobronchial valves for advanced emphysema. , 2010, The New England journal of medicine.

[31]  Hans-Ulrich Kauczor,et al.  About Objective 3-D Analysis of Airway Geometry in Computerized Tomography , 2008, IEEE Transactions on Medical Imaging.

[32]  Nicola A Hanania,et al.  Chronic obstructive pulmonary disease exacerbations in the COPDGene study: associated radiologic phenotypes. , 2011, Radiology.

[33]  Hans-Ulrich Kauczor,et al.  Automatic Airway Analysis on Multidetector Computed Tomography in Cystic Fibrosis: Correlation With Pulmonary Function Testing , 2013, Journal of thoracic imaging.

[34]  H. Kauczor,et al.  Fully automatic quantitative assessment of emphysema in computed tomography: comparison with pulmonary function testing and normal values , 2009, European Radiology.

[35]  S. Lam,et al.  New and current clinical imaging techniques to study chronic obstructive pulmonary disease. , 2009, American journal of respiratory and critical care medicine.

[36]  Jürgen Biederer,et al.  Pulmonary Emphysema in Cystic Fibrosis Detected by Densitometry on Chest Multidetector Computed Tomography , 2013, PloS one.

[37]  Courtney Crim,et al.  The presence and progression of emphysema in COPD as determined by CT scanning and biomarker expression: a prospective analysis from the ECLIPSE study. , 2013, The Lancet. Respiratory medicine.

[38]  H. Kauczor,et al.  Quantifizierung des Lungenemphysems in der Mehrschicht-CT mittels verschiedener Softwareverfahren , 2006 .

[39]  M. Prokop,et al.  The effect of iterative reconstruction on computed tomography assessment of emphysema, air trapping and airway dimensions , 2012, European Radiology.

[40]  H. Kauczor,et al.  Quantitative analysis of emphysema in 3D using MDCT: influence of different reconstruction algorithms. , 2008, European journal of radiology.

[41]  Stephen Lam,et al.  The effects of radiation dose and CT manufacturer on measurements of lung densitometry. , 2007, Chest.

[42]  H. Kauczor,et al.  Contrast enhanced CT-scans are not comparable to non-enhanced scans in emphysema quantification. , 2010, European journal of radiology.

[43]  A. Beckett,et al.  AKUFO AND IBARAPA. , 1965, Lancet.

[44]  P De Vuyst,et al.  Comparison of computed density and microscopic morphometry in pulmonary emphysema. , 1996, American journal of respiratory and critical care medicine.

[45]  P F Judy,et al.  Reference standard and statistical model for intersite and temporal comparisons of CT attenuation in a multicenter quantitative lung study. , 2012, Medical physics.

[46]  J E Cotes,et al.  Lung volumes and forced ventilatory flows , 1993, European Respiratory Journal.

[47]  E Kohda,et al.  Longitudinal follow-up study of smoking-induced lung density changes by high-resolution computed tomography. , 2000, American journal of respiratory and critical care medicine.

[48]  E. V. van Beek,et al.  Imaging phenotypes of chronic obstructive pulmonary disease , 2010, Journal of magnetic resonance imaging : JMRI.