Longitudinal Follow-up Study of Smoking-induced Lung Density Changes by High-resolution Computed Tomography

Soejima, Kenzo; Yamaguchi, Kazuhiro; Kohda, Ehiichi; Takeshita, Kei; Ito, Yoko; Mastubara, Hiroaki; Oguma, Tsuyoshi; Inoue, Takashi; Okubo, Yasuyuki; Amakawa, Kazuhisa; Tateno, Hiroki; Shiomi, Tetsuya

doi:10.1164/ajrccm.161.4.9905040

Cited by 134 publications

(104 citation statements)

References 27 publications

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“…A few examples of such studies are: [32], [37]- [40]. In this paper, we have performed a similar evaluation and correlated the proposed quantitative measures with another marker for emphysema in Section III-E, namely, % , and in general the correlations were strong, up to .…”

Section: Discussionmentioning

confidence: 82%

Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns

Srensen

Shaker

Bruijne

2010

IEEE Trans. Med. Imaging

340

246

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Abstract-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 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 = 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.

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Section: Discussionmentioning

confidence: 82%

Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns

Srensen

Shaker

Bruijne

2010

IEEE Trans. Med. Imaging

340

246

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“…Lung density varies between individuals, especially in cases of respiratory disease. Even for healthy patients, lung density and corresponding attenuation values depend on respiratory and body position (24), age, or smoking habits (25).…”

Section: Discussionmentioning

confidence: 99%

MRI-Based Attenuation Correction for Whole-Body PET/MRI: Quantitative Evaluation of Segmentation- and Atlas-Based Methods

Hofmann¹,

Bezrukov²,

Mantlik³

et al. 2011

J Nucl Med

259

211

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PET/MRI is an emerging dual-modality imaging technology that requires new approaches to PET attenuation correction (AC). We assessed 2 algorithms for whole-body MRI-based AC (MRAC): a basic MR image segmentation algorithm and a method based on atlas registration and pattern recognition (AT&PR). Methods: Eleven patients each underwent a whole-body PET/ CT study and a separate multibed whole-body MRI study. The MR image segmentation algorithm uses a combination of image thresholds, Dixon fat-water segmentation, and component analysis to detect the lungs. MR images are segmented into 5 tissue classes (not including bone), and each class is assigned a default linear attenuation value. The AT&PR algorithm uses a database of previously aligned pairs of MRI/CT image volumes. For each patient, these pairs are registered to the patient MRI volume, and machine-learning techniques are used to predict attenuation values on a continuous scale. MRAC methods are compared via the quantitative analysis of AC PET images using volumes of interest in normal organs and on lesions. We assume the PET/CT values after CT-based AC to be the reference standard. Results: In regions of normal physiologic uptake, the average error of the mean standardized uptake value was 14.1% 6 10.2% and 7.7% 6 8.4% for the segmentation and the AT&PR methods, respectively. Lesion-based errors were 7.5% 6 7.9% for the segmentation method and 5.7% 6 4.7% for the AT&PR method. Conclusion: The MRAC method using AT&PR provided better overall PET quantification accuracy than the basic MR image segmentation approach. This better quantification was due to the significantly reduced volume of errors made regarding volumes of interest within or near bones and the slightly reduced volume of errors made regarding areas outside the lungs.

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“…Using clinical meaningful detectable change in lung density metrics (i.e. detectable based on current scanner resolution and a reasonable sample size of few hundred subjects) from published literature(14–16) and results of our repeatability analysis, we extrapolated to the number of subjects needed to achieve at least 80% power to detect a typical change in lung density metrics over a short period of time. For the low attenuation area variables that we considered (%LAA856, %LAA910, and %LAA950), the power analyses utilized relative percent difference between repeat scans.…”

Section: Methodsmentioning

confidence: 99%

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

Iyer¹,

Grout²,

Zamba³

et al. 2014

J COPD F

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Rationale and Objectives 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. Materials and Methods 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), 15th 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). Results 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. Conclusion 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.

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Longitudinal Follow-up Study of Smoking-induced Lung Density Changes by High-resolution Computed Tomography

Cited by 134 publications

References 27 publications

Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns

Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns

MRI-Based Attenuation Correction for Whole-Body PET/MRI: Quantitative Evaluation of Segmentation- and Atlas-Based Methods

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

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