Saher B. Shaker

Exploring the role of CT densitometry: a randomised study of augmentation therapy in alpha-1 antitrypsin deficiency

Assessment of emphysema-modifying therapy is difficult, but newer outcome measures offer advantages over traditional methods. The EXAcerbations and Computed Tomography scan as Lung End-points (EXACTLE) trial explored the use of computed tomography (CT) densitometry and exacerbations for the assessment of the therapeutic effect of augmentation therapy in subjects with α1-antitrypsin (α1-AT) deficiency. In total, 77 subjects (protease inhibitor type Z) were randomised to weekly infusions of 60 mg·kg−1 human α1-AT (Prolastin®) or placebo for 2–2.5 yrs. The primary end-point was change in CT lung density, and an exploratory approach was adopted to identify optimal methodology, including two methods of adjustment for lung volume variability and two statistical approaches. Other end-points were exacerbations, health status and physiological indices. CT was more sensitive than other measures of emphysema progression, and the changes in CT and forced expiratory volume in 1 s were correlated. All methods of densitometric analysis concordantly showed a trend suggestive of treatment benefit (p-values for Prolastin® versus placebo ranged 0.049–0.084). Exacerbation frequency was unaltered by treatment, but a reduction in exacerbation severity was observed. In patients with α1-AT deficiency, CT is a more sensitive outcome measure of emphysema-modifying therapy than physiology and health status, and demonstrates a trend of treatment benefit from α1-AT augmentation.

Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns

We aim at improving quantitative measures of emphysema in computed tomography (CT) images of the lungs. Current standard measures, such as the relative area of emphysema (RA), rely on a single intensity threshold on individual pixels, thus ignoring any interrelations between pixels. Texture analysis allows for a much richer representation that also takes the local structure around pixels into account. This paper presents a texture classification-based system for emphysema quantification in CT images. Measures of emphysema severity are obtained by fusing pixel posterior probabilities output by a classifier. Local binary patterns (LBP) are used as texture features, and joint LBP and intensity histograms are used for characterizing regions of interest (ROIs). Classification is then performed using a k nearest neighbor classifier with a histogram dissimilarity measure as distance. A 95.2% classification accuracy was achieved on a set of 168 manually annotated ROIs, comprising the three classes: normal tissue, centrilobular emphysema, and paraseptal emphysema. The measured emphysema severity was in good agreement with a pulmonary function test (PFT) achieving correlation coefficients of up to |r| = 0.79 in 39 subjects. The results were compared to RA and to a Gaussian filter bank, and the texture-based measures correlated significantly better with PFT than did RA.

A combined pulmonary -radiology workshop for visual evaluation of COPD: study design, chest CT findings and concordance with quantitative evaluation

Abstract The purposes of this study were: to describe chest CT findings in normal non-smoking controls and cigarette smokers with and without COPD; to compare the prevalence of CT abnormalities with severity of COPD; and to evaluate concordance between visual and quantitative chest CT (QCT) scoring. Methods: Volumetric inspiratory and expiratory CT scans of 294 subjects, including normal non-smokers, smokers without COPD, and smokers with GOLD Stage I-IV COPD, were scored at a multi-reader workshop using a standardized worksheet. There were 58 observers (33 pulmonologists, 25 radiologists); each scan was scored by 9–11 observers. Interobserver agreement was calculated using kappa statistic. Median score of visual observations was compared with QCT measurements. Results: Interobserver agreement was moderate for the presence or absence of emphysema and for the presence of panlobular emphysema; fair for the presence of centrilobular, paraseptal, and bullous emphysema subtypes and for the presence of bronchial wall thickening; and poor for gas trapping, centrilobular nodularity, mosaic attenuation, and bronchial dilation. Agreement was similar for radiologists and pulmonologists. The prevalence on CT readings of most abnormalities (e.g. emphysema, bronchial wall thickening, mosaic attenuation, expiratory gas trapping) increased significantly with greater COPD severity, while the prevalence of centrilobular nodularity decreased. Concordances between visual scoring and quantitative scoring of emphysema, gas trapping and airway wall thickening were 75%, 87% and 65%, respectively. Conclusions: Despite substantial inter-observer variation, visual assessment of chest CT scans in cigarette smokers provides information regarding lung disease severity; visual scoring may be complementary to quantitative evaluation.

Variability in Densitometric Assessment of Pulmonary Emphysema With Computed Tomography.

Objectives:The objectives of this study were to investigate whether computed tomography (CT) densitometry can be applied consistently in different centers; and to evaluate the reproducibility of densitometric quantification of emphysema by assessment of different sources of variation, ie, intersite, interscan and inter- and intraobserver variability, in comparison with intersubject variability. Materials and Methods:In 5 different hospitals, 119 patients with emphysema were scanned using standardized protocols. In each site, an observer performed a quantitative densitometric analysis (including blood recalibration) on the corresponding patient group (n = 23–25) and one observer analyzed the entire group of 119 patients. After several months, the latter observer analyzed all data for a second time. Subsequently, different sources of variation were assessed by variance component analysis with and without volume correction of the data. Results:Inter- and intraobserver variability marginally contributes to the total variability (<0.001%). The interscan variability was 0.02% of the total variation after application of volume correction. The intersite variability was 48% as a result of one deviating CT scanner. Air recalibration normalized deviating air densities in CT scanners. Within sites, the intersubject variability ranged between 93% and 99% based on the analysis of 2 subsequent CT scans of the patients. Conclusions:Almost all variability in the density measurement of emphysema originates from differences between scanners and from differences in severity of emphysema between patients. Lung densitometry with multislice CT scanners is a highly reproducible measurement, especially if corrected for lung volume, because this reduces interscan variability.

Volume Adjustment of Lung Density by Computed Tomography Scans in Patients with Emphysema

Purpose: To determine how to adjust lung density measurements for the volume of the lung calculated from computed tomography (CT) scans in patients with emphysema. Material and Methods: Fifty patients with emphysema underwent 3 CT scans at 2‐week intervals. The scans were analyzed with a software package that detected the lung in contiguous images and subsequently generated a histogram of the pixel attenuation values. The total lung volume (TLV), lung weight, percentile density (PD), and relative area of emphysema (RA) were calculated from this histogram. RA and PD are commonly applied measures of pulmonary emphysema derived from CT scans. These parameters are markedly influenced by changes in the level of inspiration. The variability of lung density due to within‐subject variation in TLV was explored by plotting TLV against PD and RA. Results: The coefficients for volume adjustment for PD were relatively stable over a wide range from the 10th to the 80th percentile, whereas for RA the coefficients showed large variability especially in the lower range, which is the most relevant for quantitation of pulmonary emphysema. Conclusion: Volume adjustment is mandatory in repeated CT densitometry and is more robust for PD than for RA. Therefore, PD seems more suitable for monitoring the progression of emphysema.

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

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.

Short‐term reproducibility of computed tomography‐based lung density measurements in alpha‐1 antitrypsin deficiency and smokers with emphysema

Purpose: To study the short‐term reproducibility of lung density measurements by multi‐slice computed tomography (CT) using three different radiation doses and three reconstruction algorithms. Material and Methods: Twenty‐five patients with smokers emphysema and 25 patients with α1‐antitrypsin deficiency underwent 3 scans at 2‐week intervals. Low‐dose protocol was applied, and images were reconstructed with bone, detail, and soft algorithms. Total lung volume (TLV), 15th percentile density (PD‐15), and relative area at −910 Hounsfield units (RA‐910) were obtained from the images using Pulmo‐CMS software. Reproducibility of PD‐15 and RA‐910 and the influence of radiation dose, reconstruction algorithm, and type of emphysema were then analysed. Results: The overall coefficient of variation of volume adjusted PD‐15 for all combinations of radiation dose and reconstruction algorithm was 3.7%. The overall standard deviation of volume‐adjusted RA‐910 was 1.7% (corresponding to a coefficient of variation of 6.8%). Radiation dose, reconstruction algorithm, and type of emphysema had no significant influence on the reproducibility of PD‐15 and RA‐910. However, bone algorithm and very low radiation dose result in overestimation of the extent of emphysema. Conclusion: Lung density measurement by CT is a sensitive marker for quantitating both subtypes of emphysema. A CT‐protocol with radiation dose down to 16 mAs and soft or detail reconstruction algorithm is recommended.

A texton-based approach for the classification of lung parenchyma in CT images

In this paper, a texton-based classification system based on raw pixel representation along with a support vector machine with radial basis function kernel is proposed for the classification of emphysema in computed tomography images of the lung. The proposed approach is tested on 168 annotated regions of interest consisting of normal tissue, centrilobular emphysema, and paraseptal emphysema. The results show the superiority of the proposed approach to common techniques in the literature including moments of the histogram of filter responses based on Gaussian derivatives. The performance of the proposed system, with an accuracy of 96.43%, also slightly improves over a recently proposed approach based on local binary patterns.

Short-term effect of changes in smoking behaviour on emphysema quantification by CT

Background The effect of smoking cessation and smoking relapse on lung density was studied using low-dose CT. Methods Spiral, multidetector, low-dose CT was performed on 726 current and former smokers (>20 pack-years) recruited from a cancer screening trial. Lung density was quantified by calculating the 15th percentile density (PD15), which was adjusted to predicted total lung capacity. Data were analysed by linear regression models. Results At baseline mean PD15 was 45 g/l in former smokers (n=178) and 55 g/l in current smokers (n=548), representing a difference of 10 g/l (p<0.001). After smoking cessation (n=77) PD15 decreased by 6.2 g/l (p<0.001) in the first year, and by a further 3.6 g/l (p<0.001) in the second year, after which no further change could be detected. Moreover, the first year after relapse to smoking (n=18) PD15 increased by 3.7 g/l (p=0.02). Conclusions Current smoking status has a major influence on lung density assessed by CT, and the difference in lung density between current and former smokers observed in cross-sectional studies corresponds closely to the change in lung density seen in the years after smoking cessation. Current smoking status, and time since cessation or relapse, should be taken into account when assessing the severity of diseases such as emphysema by CT lung density.

Texture Classification in Lung CT Using Local Binary Patterns

In this paper we propose to use local binary patterns (LBP) as features in a classification framework for classifying different texture patterns in lung computed tomography. Image intensity is included by means of the joint LBP and intensity histogram, and classification is performed using the k nearest neighbor classifier with histogram similarity as distance measure. The proposed method is evaluated on a set of 168 regions of interest comprising normal tissue and different emphysema patterns, and compared to a filter bank based on Gaussian derivatives. The joint LBP and intensity histogram, achieving a classification accuracy of 95.2%, shows superior performance to using the common approach of taking moments of the filter response histograms as features, and slightly better performance than using the full filter response histograms instead. Classification results are better than some of those previously reported in the literature.