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Accuracy of emphysema quantification performed with reduced numbers of CT sections.

ABSTRACT: OBJECTIVE:The purpose of this study was to assess the accuracy of emphysema measurements obtained from systematic samples of evenly spaced CT images compared with measurements obtained from the entire scan. MATERIALS AND METHODS:Evenly spaced transverse sections from the CT studies of 136 heavy smokers who underwent screening for lung cancer in the National Lung Screening Trial and of 112 subjects who underwent imaging before lung volume reduction surgery were sampled retrospectively. The samples were acquired both by selection of specific numbers of evenly spaced images and by selection of images at specific distance intervals. The percentage of lung pixels with attenuation below specific thresholds was used as an emphysema index. The image sample error was determined as the difference in emphysema index between the image samples and the entire scan. RESULTS:The largest absolute image sample errors in the National Lung Screening Trial cohort with image sample sizes of five, 10, and 20 were 2.2, 0.8, and 0.5 index percentage points, respectively, at 1-mm section thickness (-960 HU threshold), and 2.6, 1.1, and 0.5 index percentage points at 5-mm section thickness (-930 HU threshold). The largest errors in the lung volume reduction surgery cohort for image sample sizes of five and 10 were 5.6 and 2.3 index percentage points at 8- to 10-mm section thickness (-900 HU threshold). Image sample errors were equivalent for the two sampling methods. CONCLUSION:Systematic sampling resulted in very small errors in emphysema quantification and may be useful for decreasing radiation exposure in clinical research studies of emphysema.

SUBMITTER: Pilgram TK

PROVIDER: S-EPMC2838241 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature

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Accuracy of emphysema quantification performed with reduced numbers of CT sections.

Pilgram Thomas K TK Quirk James D JD Bierhals Andrew J AJ Yusen Roger D RD Lefrak Stephen S SS Cooper Joel D JD Gierada David S DS

AJR. American journal of roentgenology 20100301 3

<h4>Objective</h4>The purpose of this study was to assess the accuracy of emphysema measurements obtained from systematic samples of evenly spaced CT images compared with measurements obtained from the entire scan.<h4>Materials and methods</h4>Evenly spaced transverse sections from the CT studies of 136 heavy smokers who underwent screening for lung cancer in the National Lung Screening Trial and of 112 subjects who underwent imaging before lung volume reduction surgery were sampled retrospectiv ...[more]

PMID: 20173132

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Project description:RATIONALE AND OBJECTIVES:Many patients with chronic obstructive pulmonary disease (COPD) have marked discordance between forced expiratory volume in 1 second (FEV1) and degree of emphysema on computed tomography (CT). Biomechanical differences between these patients have not been studied. We aimed to identify reasons for the discordance between CT and spirometry in some patients with COPD. MATERIALS AND METHODS:Subjects with Global initiative for chronic Obstructive Lung Disease stages I-IV from a large multicenter study (The Genetic Epidemiology of COPD) were arranged by percentiles of %predicted FEV1 and emphysema on CT. Three categories were created using differences in percentiles: Catspir with predominant airflow obstruction/minimal emphysema, CatCT with predominant emphysema/minimal airflow obstruction, and Catmatched with matched FEV1 and emphysema. Image registration was used to derive Jacobian determinants, a measure of lung elasticity, anisotropy, and strain tensors, to assess biomechanical differences between groups. Regression models were created with the previously mentioned categories as outcome variable, adjusting for demographics, scanner type, quantitative CT-derived emphysema, gas trapping, and airway thickness (model 1), and after adding biomechanical CT metrics (model 2). RESULTS:Jacobian determinants, anisotropy, and strain tensors were strongly associated with FEV1. With Catmatched as control, model 2 predicted Catspir and CatCT better than model 1 (Akaike information criterion 255.8 vs. 320.8). In addition to demographics, the strongest independent predictors of FEV1 were Jacobian mean (??=?1.60,95%confidence intervals [CI]?=?1.16 to 1.98; P?<?0.001), coefficient of variation (CV) of Jacobian (??=?1.45,95%CI?=?0.86 to 2.03; P?<?0.001), and CV of strain (??=?1.82,95%CI?=?0.68 to 2.95; P?=?0.001). CVs of Jacobian and strain are both potential markers of biomechanical lung heterogeneity. CONCLUSIONS:CT-derived measures of lung mechanics improve the link between quantitative CT and spirometry, offering the potential for new insights into the linkage between regional parenchymal destruction and global decrement in lung function in patients with COPD.

Project description:BackgroundPattern of emphysema at chest CT, scored visually by using the Fleischner Society system, is associated with physiologic impairment and mortality risk.PurposeTo determine whether participant-level emphysema pattern could predict impairment and mortality when classified by using a deep learning method.Materials and MethodsThis retrospective analysis of Genetic Epidemiology of COPD (COPDGene) study participants enrolled between 2007 and 2011 included those with baseline CT, visual emphysema scores, and survival data through 2018. Participants were partitioned into nonoverlapping sets of 2407 for algorithm training, 100 for validation and parameter tuning, and 7143 for testing. A deep learning algorithm using convolutional neural network and long short-term memory architectures was trained to classify pattern of emphysema according to Fleischner criteria. Deep learning scores were compared with visual scores and clinical parameters including pulmonary function tests. Cox proportional hazard models were used to evaluate relationships between emphysema scores and survival. The algorithm was also tested by using CT and clinical data in 1962 participants enrolled in the Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE) study.ResultsA total of 7143 COPDGene participants (mean age ± standard deviation, 59.8 years ± 8.9; 3734 men and 3409 women) were evaluated. Deep learning emphysema classifications were associated with impaired pulmonary function tests, 6-minute walk distance, and St George's Respiratory Questionnaire at univariate analysis (P < .001 for each). Testing in the ECLIPSE cohort showed similar associations (P < .001). In the COPDGene test cohort, deep learning emphysema classification improved the fit of linear mixed models in the prediction of these clinical parameters compared with visual scoring (P < .001). Compared with participants without emphysema, mortality was greater in participants classified by the deep learning algorithm as having any grade of emphysema (adjusted hazard ratios were 1.5, 1.7, 2.9, 5.3, and 9.7, respectively, for trace, mild, moderate, confluent, and advanced destructive emphysema; P < .05).ConclusionDeep learning automation of the Fleischner grade of emphysema at chest CT is associated with clinical measures of pulmonary insufficiency and the risk of mortality.© RSNA, 2019Online supplemental material is available for this article.

Project description:BackgroundAlthough advanced medical imaging technologies give detailed diagnostic information, a low-dose, fast, and inexpensive option for early detection of respiratory diseases and follow-ups is still lacking. The novel method of x-ray dark-field chest imaging might fill this gap but has not yet been studied in living humans. Enabling the assessment of microstructural changes in lung parenchyma, this technique presents a more sensitive alternative to conventional chest x-rays, and yet requires only a fraction of the dose applied in CT. We studied the application of this technique to assess pulmonary emphysema in patients with chronic obstructive pulmonary disease (COPD).MethodsIn this diagnostic accuracy study, we designed and built a novel dark-field chest x-ray system (Technical University of Munich, Munich, Germany)-which is also capable of simultaneously acquiring a conventional thorax radiograph (7 s, 0·035 mSv effective dose). Patients who had undergone a medically indicated chest CT were recruited from the department of Radiology and Pneumology of our site (Klinikum rechts der Isar, Technical University of Munich, Munich, Germany). Patients with pulmonary pathologies, or conditions other than COPD, that might influence lung parenchyma were excluded. For patients with different disease stages of pulmonary emphysema, x-ray dark-field images and CT images were acquired and visually assessed by five readers. Pulmonary function tests (spirometry and body plethysmography) were performed for every patient and for a subgroup of patients the measurement of diffusion capacity was performed. Individual patient datasets were statistically evaluated using correlation testing, rank-based analysis of variance, and pair-wise post-hoc comparison.FindingsBetween October, 2018 and December, 2019 we enrolled 77 patients. Compared with CT-based parameters (quantitative emphysema ρ=-0·27, p=0·089 and visual emphysema ρ=-0·45, p=0·0028), the dark-field signal (ρ=0·62, p<0·0001) yields a stronger correlation with lung diffusion capacity in the evaluated cohort. Emphysema assessment based on dark-field chest x-ray features yields consistent conclusions with findings from visual CT image interpretation and shows improved diagnostic performance than conventional clinical tests characterising emphysema. Pair-wise comparison of corresponding test parameters between adjacent visual emphysema severity groups (CT-based, reference standard) showed higher effect sizes. The mean effect size over the group comparisons (absent-trace, trace-mild, mild-moderate, and moderate-confluent or advanced destructive visual emphysema grades) for the COPD assessment test score is 0·21, for forced expiratory volume in 1 s (FEV1)/functional vital capacity is 0·25, for FEV1% of predicted is 0·23, for residual volume % of predicted is 0·24, for CT emphysema index is 0·35, for dark-field signal homogeneity within lungs is 0·38, for dark-field signal texture within lungs is 0·38, and for dark-field-based emphysema severity is 0·42.InterpretationX-ray dark-field chest imaging allows the diagnosis of pulmonary emphysema in patients with COPD because this technique provides relevant information representing the structural condition of lung parenchyma. This technique might offer a low radiation dose alternative to CT in COPD and potentially other lung disorders.FundingEuropean Research Council, Deutsche Forschungsgemeinschaft, Royal Philips, and Karlsruhe Nano Micro Facility.

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Accuracy of emphysema quantification performed with reduced numbers of CT sections.

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Accuracy of emphysema quantification performed with reduced numbers of CT sections.

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