Low- and High-Attenuation Lung Volume in Quantitative Chest CT in Children without Lung Disease.
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ABSTRACT:
SUBMITTER: Moutafidis D
PROVIDER: S-EPMC8700567 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Rationale and objectivesTo develop and evaluate an automated machine learning (ML) algorithm for segmenting the paraspinous muscles on chest computed tomography (CT) scans to evaluate for presence of sarcopenia.Materials and methodsA convolutional neural network based on the U-Net architecture was trained to perform muscle segmentation on a dataset of 1875 single slice CT images and was tested on 209 CT images of participants in the National Lung Screening Trial. Low-dose, noncontrast CT examinations were obtained at 33 clinical sites, using scanners from four manufacturers. The study participants had a mean age of 71.6 years (range, 70-74 years). Ground truth was obtained by manually segmenting the left paraspinous muscle at the level of the T12 vertebra. Muscle cross-sectional area (CSA) and muscle attenuation (MA) were recorded. Comparison between the ML algorithm and ground truth measures of muscle CSA and MA were obtained using Dice similarity coefficients and Pearson correlations.ResultsCompared to ground truth segmentation, the ML algorithm achieved median (standard deviation) Dice scores of 0.94 (0.04) in the test set. Mean (SD) muscle CSA was 14.3 (3.6) cm2 for ground truth and 13.7 (3.5) cm2 for ML segmentation. Mean (SD) MA was 41.6 (7.6) Hounsfield units (HU) for ground truth and 43.5 (7.9) HU for ML segmentation. There was high correlation between ML algorithm and ground truth for muscle CSA (r2 = 0.86; p < 0.0001) and MA (r2 = 0.95; p < 0.0001).ConclusionThe ML algorithm for measurement of paraspinous muscles compared favorably to manual ground truth measurements in the NLST. The algorithm generalized well to a heterogeneous set of low-dose CT images and may be capable of automated quantification of muscle metrics to screen for sarcopenia on routine chest CT examinations.
| S-EPMC6878160 | biostudies-literature
Project description:PurposeTo determine the accuracy of quantitative CT to diagnose pulmonary edema compared to qualitative CT and CXR and to determine a threshold Hounsfield unit (HU) measurement for pulmonary edema on CT examinations.MethodElectronic medical records were searched for patients with a billing diagnosis of heart failure and a Chest CT and CXR performed within three hours between 1/1/2016 to 10/1/2016, yielding 100 patients. CXR and CT examinations were scored for the presence and severity of edema, using a 0-5 scale, and CT HU measurements were obtained in each lobe. Polyserial correlation coefficients evaluated the association between CT HUs and CXR scores, and receiver operating characteristic (ROC) curve analysis determined a cutoff CT HU value for identification of pulmonary edema.ResultsCorrelation between CT HU and CXR score was moderately strong (r = 0.585-0.685) with CT HU measurements demonstrating good to excellent accuracy in differentiating between no edema (grade 0) and mild to severe edema (grades 1-5) in every lobe, with AUCs ranging between 0.869 and 0.995. The left upper lobe demonstrated the highest accuracy, using a cutoff value of -825 HU (AUC of 0.995, sensitivity = 100 % and specificity = 95.1 %). Additionally, qualitative CT evaluation was less sensitive (84 %) than portable CXR in identifying pulmonary edema. However, quantitative CT evaluation was as sensitive as portable CXR (100 %) and highly specific (95 %).ConclusionsQuantitative CT enables the identification of pulmonary edema with high accuracy and demonstrates a greater sensitivity than qualitative CT in assessment of pulmonary edema.
| S-EPMC7607389 | biostudies-literature
Project description:Rationale and objectivesEmphysema is characterized by lung tissue destruction and trapped gas. On computed tomographic (CT) images, this may be expressed by widespread areas with high specific gas volume (SV(g)). SV(g) is highly sensitive to very low attenuation values, which frequently occur in the CT images of patients with severe emphysema. The purpose of the present work was to study if and how different reconstruction settings and different scanners significantly influence SV(g) distribution, particularly in the very low attenuation range.Materials and methodsTwo sets of CT images taken from two different CT scanners at two different lung volumes in 10 healthy volunteers and 18 subjects with severe emphysema were analyzed. Images were reconstructed using two different settings of reconstruction parameters: (1) thin slice thickness and sharp filter and (2) thick slice thickness and smooth filter. For each set of images, average values of SV(g) and their variation (?SV(g)) from total lung capacity to residual volume were calculated in the whole lung.ResultsVery low attenuation values are always present in CT images when reconstructed with thin slice thickness and a sharp filter and in very large numbers in patients with severe emphysema. SV(g) values were in general significantly higher in patients with emphysema than in healthy subjects, at both total lung capacity and residual volume (P < .001), and were significantly influenced by the reconstruction filter (P < .001) and CT scanner (P < .001). ?SV(g) was lower in patients with emphysema than in healthy subjects (P < .001) and was significantly affected by the reconstruction setting but not by the CT scanner.ConclusionsThe disproportionate effect of low-attenuation pixels on SV(g) likely causes overestimation of the severity of emphysema and trapped gas. This can be significantly reduced, however, by using thick slices and a smooth filter for image reconstruction. ?SV(g) is generally robust for quantifying the functional impairment of the lung in severe emphysema.
| S-EPMC3167941 | biostudies-literature
Project description:ObjectivesThe purpose is to assess the ability of low-dose CT (LDCT) to determine lung involvement in SARS-CoV-2 pneumonia and to describe a COVID19-LDCT severity score.Materials and methodsPatients with SARS-CoV-2 infection confirmed by RT-PCR were retrospectively analysed. Clinical data, the National Early Warning Score (NEWS) and imaging features were recorded. Lung features included ground-glass opacities (GGO), areas of consolidation and crazy paving patterns. The COVID19-LDCT score was calculated by summing the score of each segment from 0 (no involvement) to 10 (severe impairment). Univariate analysis was performed to explore predictive factor of high COVID19-LDCT score. The nonparametric Mann-Whitney test was used to compare groups and a Spearman correlation used with p<0.05 for significance.ResultsEighty patients with positive RT-PCR were analysed. The mean age was 55 years ± 16, with 42 males (53%). The most frequent symptoms were fever (60/80, 75%) and cough (59/80, 74%), the mean NEWS was 1.7±2.3. All LDCT could be analysed and 23/80 (28%) were normal. The major imaging finding was GGOs in 56 cases (67%). The COVID19-LDCT score (mean value = 19±29) was correlated with NEWS (r = 0.48, p<0.0001). No symptoms were risk factor to have pulmonary involvement. Univariate analysis shown that dyspnea, high respiratory rate, hypertension and diabetes are associated to a COVID19-LDCT score superior to 50.ConclusionsCOVID19-LDCT score did correlate with NEWS. It was significantly different in the clinical low-risk and high-risk groups. Further work is needed to validate the COVID19-LDCT score against patient prognosis.
| S-EPMC7608883 | biostudies-literature
Project description:PurposeTo determine the impact of low tube voltage coronary CT angiography on detection of subclinical atherosclerosis.Materials and methodsRetrospective sampling of an emergency department coronary CT angiography registry was performed. All patients in the registry underwent a noncontrast coronary artery calcium (CAC) scoring scan at 120 kV before coronary CT angiography. The study sample (n = 264) constituted patients with subclinical atherosclerosis (Coronary Artery Disease Reporting and Data System™ [CAD-RADS] 1 or 2) randomly mixed one-to-one with patients without atherosclerosis (CAD-RADS 0). The patients with coronary CT angiography performed at 70-90 kV were considered the low tube voltage group (n = 159) and patients with coronary CT angiography performed at 100-120 kV were considered the standard tube voltage group (n = 105). The number of coronary plaques and overall CAD-RADS classification (per patient) were evaluated twice: initially, by reading coronary CT angiography alone, and then, by coronary CT angiography in combination with a CAC scan. Considering the combined reading (CT angiography plus CAC scan) as the reference standard, the performance of coronary CT angiography alone was assessed for plaque detection and appropriate CAD-RADS (per patient) classification. The comparisons were made between the low tube voltage and standard tube voltage groups by using a Fisher exact test and χ2 test for proportions and a Mann-Whitney test and Kruskal-Wallis test for means.ResultsIn total, 455 plaques were identified in 118 patients (70 of 159 patients in the low tube voltage group; 48 of 105 in the standard tube voltage group). When reading coronary CT angiographic images alone, 97 of 455 (21%) plaques were missed that led to an incorrect CAD-RADS classification in 16 of 264 (6%) studies (interpreted as CAD-RADS 0 instead of CAD-RADS 1 or 2). Missed plaques were significantly more frequent in the low tube voltage group versus the standard tube voltage group (41% [85 of 206] vs 5% [12 of 249], respectively; P < .001). Incorrect CAD-RADS classification was also seen more commonly in the low tube voltage group (8.8% [14 of 159] vs 2% [two of 105]; P = .01), typically at low plaque burden (median CAC score, 1; range, 1-4). Calcified plaques that appeared isodense to luminal contrast material attenuation were seen more frequently in the low tube voltage group compared with the standard tube voltage group (20% [32 of 159] vs 7.6% [eight of 105], respectively; P = .005).ConclusionCoronary artery plaques may be missed at low tube voltage coronary CT angiographic examination performed without a concomitant CAC scan.© RSNA, 2019Supplemental material is available for this article.See also the commentary by Truong in this issue.
| S-EPMC7977955 | biostudies-literature
Project description:Iodine material images (aka iodine basis images) generated from dual energy computed tomography (DECT) have been used to assess potential perfusion defects in the pulmonary parenchyma. However, iodine material images do not provide the needed absolute quantification of the pulmonary blood pool, as materials with effective atomic numbers (Zeff ) different from those of basis materials may also contribute to iodine material images, thus confounding the quantification of perfusion defects. (i) To demonstrate the limitations of iodine material images in pulmonary perfusion defect quantification and (ii) to develop and validate a new quantitative biomarker using effective atomic numbers derived from DECT images. The quantitative relationship between the perfusion blood volume (PBV) in pulmonary parenchyma and the effective atomic number (Zeff ) spatial distribution was studied to show that the desired quantitative PBV maps are determined by the spatial maps of Zeff as PBVZeff(x)=aZeffβ(x)+b , where a, b, and β are three constants. Namely, quantitative PBVZeff is determined by Zeff images instead of the iodine basis images. Perfusion maps were generated for four human subjects to demonstrate the differences between conventional iodine material image-based PBV (PBViodine ) derived from two-material decompositions and the proposed PBVZeff method. Among patients with pulmonary emboli, the proposed PBVZeff maps clearly show the perfusion defects while the PBViodine maps do not. Additionally, when there are no perfusion defects present in the derived PBV maps, no pulmonary emboli were diagnosed by an experienced thoracic radiologist. Effective atomic number-based quantitative PBV maps provide the needed sensitive and specific biomarker to quantify pulmonary perfusion defects.
| S-EPMC8595877 | biostudies-literature
Project description:BackgroundTobacco smoke exposure is associated with emphysema and pulmonary fibrosis, both of which are irreversible. We have developed a new objective CT analysis tool that combines densitometry with machine learning to detect high attenuation changes in visually normal appearing lung (NormHA) that may precede these diseases.MethodsWe trained the classification tool by placing 34,528 training points in chest CT scans from 297 COPDGene participants. The tool was then used to classify lung tissue in 9,038 participants as normal, emphysema, fibrotic/interstitial, or NormHA. Associations between the quartile of NormHA and plasma-based biomarkers, clinical severity, and mortality were evaluated using Jonckheere-Terpstra, pairwise Wilcoxon rank-sum tests, and multivariable linear and Cox regression.ResultsA higher percentage of lung occupied by NormHA was associated with higher C-reactive protein and intercellular adhesion molecule 1 (P for trend for both < .001). In analyses adjusted for multiple covariates, including high and low attenuation area, compared with those in the lowest quartile of NormHA, those in the highest quartile had a 6.50 absolute percent lower percent predicted lower FEV1 (P < .001), an 8.48 absolute percent lower percent predicted forced expiratory volume, a 10.78-meter shorter 6-min walk distance (P = .011), and a 56% higher risk of death (P = .003). These findings were present even in those individuals without visually defined interstitial lung abnormalities.ConclusionsA new class of NormHA on CT may represent a unique tissue class associated with adverse outcomes, independent of emphysema and fibrosis.
| S-EPMC6904856 | biostudies-literature
Project description:Background and objectiveNormative values for HAA-a quantitative, CT-based measure of subclinical ILD-in healthy adults are needed to improve interpretability in clinical and research settings.MethodsHAA was measured on full-lung CT in 3110 participants in the MESA study. Clinical prediction models were developed using a healthy never-smoker subset with normal spirometry (n = 696). RMSE on cross-validation was used as the primary criterion for model selection. Parametric and non-parametric methods were considered. z-Scores were calculated for the entire study sample. Associations between z-scores and several ILD features were estimated.ResultsIn the healthy never-smoker subset, the mean age was 69 years with a range of 54-93 years. The median HAA was 4.3% with a range of 2.7-17.8%. Linear regression had better predictive performance than other methods. The final model included race, height, weight, age and sex. The standard error of the estimate was 1.62 with a cross-validated RMSE of 1.64 and an adjusted R2 of 0.139. z-Scores were associated with several ILD outcomes in adjusted models, including ILA (OR: 1.40 per z-unit; 95% CI: 1.30, 1.52), exertional dyspnoea (OR: 1.08 per z-unit; 95% CI: 1.02, 1.15) and FVC (expected increase per z-unit: -2.49; 95% CI: -2.95, - 2.03).ConclusionWe present a reference equation and z-scores to define expected values of HAA on full-lung CT to aid HAA interpretation in middle-aged and older adults.
| S-EPMC8176866 | biostudies-literature
Project description:Chest CT scans detect structural abnormalities in children with cystic fibrosis (CF), even when pulmonary function tests (PFTs) are normal. The use of chest CT is limited in clinical practice, because of concerns over expense, increased resource utilization, and radiation exposure. Quantitative chest radiography scores are useful in detecting mild lung disease, but whether they are sensitive to the presence of CT scan abnormalities has not been evaluated.To determine in a cross-sectional study if quantitative chest radiography is a more sensitive marker of chest CT abnormalities than other lung disease surrogates.Brody chest CT scores were calculated for 81 children enrolled in the Wisconsin CF Neonatal Screening Project. We determined the sensitivity for Wisconsin (WCXR) and Brasfield (BCXR) chest radiography scores, PFTs, positive cultures for P. aeruginosa (PA), and parental report of symptoms to detect a Brody score worse than the median score for study participants.The mean FEV(1) for the study population was 91% predicted. Abnormal WCXR and BCXR scores had the highest sensitivity to detect a chest CT score worse than the median; abnormal PFTs, parental report of symptoms, and the presence of PA had much lower sensitivity (P?<?0.001).In this cross sectional study, quantitative chest radiography has excellent sensitivity to detect an abnormal chest CT and may have a role in monitoring lung disease progression in children with CF.
| S-EPMC3309112 | biostudies-literature
Project description:The goals of this study were to determine whether bone density measured using CT (CTBD) can show significant differences in bone loss according to smoking status and pack-years, and to examine the correlation between CTBD and bone mineral density when measured by dual-energy X-ray absorptiometry (DEXA-BMD) in males without chronic obstructive pulmonary disease (COPD). In this cross-sectional study, 1,011 males without airflow obstruction ?50 years old were included. CTBD and DEXA-BMD were compared among groups with different smoking statuses. The correlation between CTBD and DEXA-BMD and the association of CTBD with pack-years were also investigated. CTBD of all vertebral bodies (VBs) and DEXA-BMD of all VBs without L1 showed significant differences among never, former, and current smokers. CTBD was significantly lowest in ?30-pack-year smokers and was significantly lower in ?30-pack-year smokers than in <15-pack-year smokers (all P?<?0.05). There were significant correlations between DEXA-BMD and CTBD at all VB levels (correlation coefficient [r], 0.448~0.640; all P?<?0.01). A lower CTBD had a significant association with a 15 ? x < 30-pack-year smoking history and ?30-pack-year smoking history, while there was no association with never-smokers. In conclusion, CTBD demonstrated significant differences in bone quality according to smoking status and pack-years in males without COPD.
| S-EPMC6718668 | biostudies-literature