Dataset Information

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

ACCESS DATA

Publications

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

Similar Datasets

Project description:BackgroundArtificial intelligence (AI)-aided analysis of chest CT expedites the quantification of abnormalities and may facilitate the diagnosis and assessment of the prognosis of subjects with COVID-19.ObjectivesThis study investigates the performance of an AI-aided quantification model in predicting the clinical outcomes of hospitalized subjects with COVID-19 and compares it with radiologists' performance.Subjects and methodsA total of 90 subjects with COVID-19 (men, n = 59 [65.6%]; age, 52.9±16.7 years) were recruited in this cross-sectional study. Quantification of the total and compromised lung parenchyma was performed by two expert radiologists using a volumetric image analysis software and compared against an AI-assisted package consisting of a modified U-Net model for segmenting COVID-19 lesions and an off-the-shelf U-Net model augmented with COVID-19 data for segmenting lung volume. The fraction of compromised lung parenchyma (%CL) was calculated. Based on clinical results, the subjects were divided into two categories: critical (n = 45) and noncritical (n = 45). All admission data were compared between the two groups.ResultsThere was an excellent agreement between the radiologist-obtained and AI-assisted measurements (intraclass correlation coefficient = 0.88, P < 0.001). Both the AI-assisted and radiologist-obtained %CLs were significantly higher in the critical subjects (P = 0.009 and 0.02, respectively) than in the noncritical subjects. In the multivariate logistic regression analysis to distinguish the critical subjects, an AI-assisted %CL ≥35% (odds ratio [OR] = 17.0), oxygen saturation level of <88% (OR = 33.6), immunocompromised condition (OR = 8.1), and other comorbidities (OR = 15.2) independently remained as significant variables in the models. Our proposed model obtained an accuracy of 83.9%, a sensitivity of 79.1%, and a specificity of 88.6% in predicting critical outcomes.ConclusionsAI-assisted measurements are similar to quantitative radiologist-obtained measurements in determining lung involvement in COVID-19 subjects.

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.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets