Assessment of Treatment Response in Patients With Severe Asthma Using Visual and Quantitative Analysis of Chest CT.

OBJECTIVE: To evaluate the role of visual and quantitative chest CT parameters in assessing treatment response in patients with severe asthma. MATERIALS AND METHODS: Korean participants enrolled in a prospective multicenter study, named the Precision Medicine Intervention in Severe Asthma study, from May 2020 to August 2021, underwent baseline and follow-up chest CT scans (inspiration/expiration) 10-12 months apart, before and after biologic treatment. Two radiologists scored bronchiectasis severity and mucus plugging extent. Quantitative parameters were obtained from each CT scan as follows: normal lung area (normal), air trapping without emphysema (AT without emph), air trapping with emphysema (AT with emph), and airway (total branch count, Pi10). Clinical parameters, including pulmonary function tests (forced expiratory volume in 1 s [FEV1] and FEV1/forced vital capacity [FVC]), sputum and blood eosinophil count, were assessed at initial and follow-up stages. Changes in CT parameters were correlated with changes in clinical parameters using Pearson or Spearman correlation. RESULTS: Thirty-four participants (female:male, 20:14; median age, 50.5 years) diagnosed with severe asthma from three centers were included. Changes in the bronchiectasis and mucus plugging extent scores were negatively correlated with changes in FEV1 and FEV1/FVC (ρ = from -0.544 to -0.368, all P < 0.05). Changes in quantitative CT parameters were correlated with changes in FEV1 (normal, r = 0.373 [P = 0.030], AT without emph, r = -0.351 [P = 0.042]), FEV1/FVC (normal, r = 0.390 [P = 0.022], AT without emph, r = -0.370 [P = 0.031]). Changes in total branch count were positively correlated with changes in FEV1 (r = 0.349 [P = 0.043]). There was no correlation between changes in Pi10 and the clinical parameters (P > 0.05). CONCLUSION: Visual and quantitative CT parameters of normal, AT without emph, and total branch count may be effective for evaluating treatment response in patients with severe asthma.

Usefulness of CT Quantification-Based Assessment in Defining Progressive Pulmonary Fibrosis.

RATIONALE AND OBJECTIVES: To establish a quantitative CT threshold for radiological disease progression of progressive pulmonary fibrosis (PPF) and evaluate its feasibility in patients with connective tissue disease-related interstitial lung disease (CTD-ILD). MATERIALS AND METHODS: Between April 2007 and October 2022, patients diagnosed with CTD-ILD retrospectively evaluated. CT quantification was conducted using a commercial software by summing the percentages of ground-glass opacity, consolidation, reticular opacity, and honeycombing. The quantitative threshold for radiological progression was determined based on the highest discrimination on overall survival (OS). Two thoracic radiologists independently evaluated visual radiological progression, and the senior radiologist's assessment was used as the final result. Cox regression was used to assess prognosis of PPF based on the visual assessment and quantitative threshold. RESULTS: 97 patients were included and followed up for a median of 30.3 months (range, 4.7-198.1 months). For defining radiological disease progression, the optimal quantitative CT threshold was 4%. Using this threshold, 12 patients were diagnosed with PPF, while 14 patients were diagnosed with PPF based on the visual assessment, with an agreement rate of 97.9% (95/97). Worsening respiratory symptoms (hazard ratio [HR], 12.73; P < .001), PPF based on the visual assessment (HR, 8.86; P = .002) and based on the quantitative threshold (HR, 6.72; P = .009) were independent risk factors for poor OS. CONCLUSION: The quantitative CT threshold for radiological disease progression (4%) was feasible in defining PPF in terms of its agreement with PPF grouping and prognostic performance when compared to visual assessment.

Learning Curve for CT-Guided Percutaneous Transthoracic Needle Biopsy: Retrospective Evaluation Among 17 Thoracic Imaging Fellows at a Tertiary Referral Hospital.

BACKGROUND. CT-guided percutaneous transthoracic needle biopsy (PTNB) is widely used for evaluation of indeterminate pulmonary lesions, although guidelines are lacking regarding the experience needed to gain sufficient skill. OBJECTIVE. The purpose of our study was to investigate the learning curve among a large number of operators in a tertiary referral hospital and to determine the number of procedures required to obtain acceptable performance. METHODS. This retrospective study included CT-guided PTNBs with coaxial technique performed by 17 thoracic imaging fellows from March 2, 2011, to August 8, 2017, who were novices in the procedure. A maximum number of 200 consecutive procedures per operator were included. The cumulative summation method was used to assess learning curves for diagnostic accuracy, false-negative rate, pneumothorax rate, and hemoptysis rate. Operators were assessed individually and in a pooled analysis. Pneumothorax risk was also assessed in a model adjusting for risk factors. Acceptable failure rates were defined as 0.1 for diagnostic accuracy and false-negative rate, 0.45 for pneumothorax rate, and 0.05 for hemoptysis rate. RESULTS. The study included 3261 procedures in 3134 patients (1876 men, 1258 women; mean age, 67.7 ± 12.1 [SD] years). Overall diagnostic accuracy was 94.2% (2960/3141). All 17 operators achieved acceptable diagnostic accuracy (37 procedures required in the pooled analysis; median, 33 procedures required [range, 19-67 procedures required]). Overall false-negative rate was 7.6% (179/2370). All 17 operators achieved acceptable false-negative rate (52 procedures required in the pooled analysis; median, 33 procedures required [range, 19-95 procedures required]). Pneumothorax occurred in 32.6% of the procedures (1063/3261 procedures), and hemoptysis occurred in 2.7% of the procedures (89/3261 procedures). All 17 operators achieved acceptable pneumothorax rate (20 procedures required in the pooled analysis; median, 19 procedures required [range, 7-63 procedures required]). In the risk-adjusted model, 15 operators achieved acceptable pneumothorax rate (54 procedures required in the pooled analysis; median, 36 procedures required [range, 10-192 procedures required]). Sixteen operators achieved acceptable hemoptysis rate (67 procedures required in the pooled analysis; median, 55 procedures required [range, 41-152 procedures required]). CONCLUSION. For CT-guided PTNB, at least 37 and 52 procedures are required to achieve acceptable diagnostic accuracy and false-negative rate, respectively. Not all operators achieved acceptable complication rates. CLINICAL IMPACT. The findings may help set standards for training, supervision, and ongoing assessment of operator proficiency for this procedure.

New Method for Combined Quantitative Assessment of Air-Trapping and Emphysema on Chest Computed Tomography in Chronic Obstructive Pulmonary Disease: Comparison with Parametric Response Mapping.

OBJECTIVE: Emphysema and small-airway disease are the two major components of chronic obstructive pulmonary disease (COPD). We propose a novel method of quantitative computed tomography (CT) emphysema air-trapping composite (EAtC) mapping to assess each COPD component. We analyzed the potential use of this method for assessing lung function in patients with COPD. MATERIALS AND METHODS: A total of 584 patients with COPD underwent inspiration and expiration CTs. Using pairwise analysis of inspiration and expiration CTs with non-rigid registration, EAtC mapping classified lung parenchyma into three areas: Normal, functional air trapping (fAT), and emphysema (Emph). We defined fAT as the area with a density change of less than 60 Hounsfield units (HU) between inspiration and expiration CTs among areas with a density less than -856 HU on inspiration CT. The volume fraction of each area was compared with clinical parameters and pulmonary function tests (PFTs). The results were compared with those of parametric response mapping (PRM) analysis. RESULTS: The relative volumes of the EAtC classes differed according to the Global Initiative for Chronic Obstructive Lung Disease stages (p < 0.001). Each class showed moderate correlations with forced expiratory volume in 1 second (FEV1) and FEV1/forced vital capacity (FVC) (r = -0.659-0.674, p < 0.001). Both fAT and Emph were significant predictors of FEV1 and FEV1/FVC (R² = 0.352 and 0.488, respectively; p < 0.001). fAT was a significant predictor of mean forced expiratory flow between 25% and 75% and residual volume/total vital capacity (R² = 0.264 and 0.233, respectively; p < 0.001), while Emph and age were significant predictors of carbon monoxide diffusing capacity (R² = 0.303; p < 0.001). fAT showed better correlations with PFTs than with small-airway disease on PRM. CONCLUSION: The proposed quantitative CT EAtC mapping provides comprehensive lung functional information on each disease component of COPD, which may serve as an imaging biomarker of lung function.

Content-Based Image Retrieval of Chest CT with Convolutional Neural Network for Diffuse Interstitial Lung Disease: Performance Assessment in Three Major Idiopathic Interstitial Pneumonias.

OBJECTIVE: To assess the performance of content-based image retrieval (CBIR) of chest CT for diffuse interstitial lung disease (DILD). MATERIALS AND METHODS: The database was comprised by 246 pairs of chest CTs (initial and follow-up CTs within two years) from 246 patients with usual interstitial pneumonia (UIP, n = 100), nonspecific interstitial pneumonia (NSIP, n = 101), and cryptogenic organic pneumonia (COP, n = 45). Sixty cases (30-UIP, 20-NSIP, and 10-COP) were selected as the queries. The CBIR retrieved five similar CTs as a query from the database by comparing six image patterns (honeycombing, reticular opacity, emphysema, ground-glass opacity, consolidation and normal lung) of DILD, which were automatically quantified and classified by a convolutional neural network. We assessed the rates of retrieving the same pairs of query CTs, and the number of CTs with the same disease class as query CTs in top 1-5 retrievals. Chest radiologists evaluated the similarity between retrieved CTs and queries using a 5-scale grading system (5-almost identical; 4-same disease; 3-likelihood of same disease is half; 2-likely different; and 1-different disease). RESULTS: The rate of retrieving the same pairs of query CTs in top 1 retrieval was 61.7% (37/60) and in top 1-5 retrievals was 81.7% (49/60). The CBIR retrieved the same pairs of query CTs more in UIP compared to NSIP and COP (p = 0.008 and 0.002). On average, it retrieved 4.17 of five similar CTs from the same disease class. Radiologists rated 71.3% to 73.0% of the retrieved CTs with a similarity score of 4 or 5. CONCLUSION: The proposed CBIR system showed good performance for retrieving chest CTs showing similar patterns for DILD.

Artificial Intelligence in Health Care: Current Applications and Issues.

In recent years, artificial intelligence (AI) technologies have greatly advanced and become a reality in many areas of our daily lives. In the health care field, numerous efforts are being made to implement the AI technology for practical medical treatments. With the rapid developments in machine learning algorithms and improvements in hardware performances, the AI technology is expected to play an important role in effectively analyzing and utilizing extensive amounts of health and medical data. However, the AI technology has various unique characteristics that are different from the existing health care technologies. Subsequently, there are a number of areas that need to be supplemented within the current health care system for the AI to be utilized more effectively and frequently in health care. In addition, the number of medical practitioners and public that accept AI in the health care is still low; moreover, there are various concerns regarding the safety and reliability of AI technology implementations. Therefore, this paper aims to introduce the current research and application status of AI technology in health care and discuss the issues that need to be resolved.

Assessment of the Robustness of Convolutional Neural Networks in Labeling Noise by Using Chest X-Ray Images From Multiple Centers.

BACKGROUND: Computer-aided diagnosis on chest x-ray images using deep learning is a widely studied modality in medicine. Many studies are based on public datasets, such as the National Institutes of Health (NIH) dataset and the Stanford CheXpert dataset. However, these datasets are preprocessed by classical natural language processing, which may cause a certain extent of label errors. OBJECTIVE: This study aimed to investigate the robustness of deep convolutional neural networks (CNNs) for binary classification of posteroanterior chest x-ray through random incorrect labeling. METHODS: We trained and validated the CNN architecture with different noise levels of labels in 3 datasets, namely, Asan Medical Center-Seoul National University Bundang Hospital (AMC-SNUBH), NIH, and CheXpert, and tested the models with each test set. Diseases of each chest x-ray in our dataset were confirmed by a thoracic radiologist using computed tomography (CT). Receiver operating characteristic (ROC) and area under the curve (AUC) were evaluated in each test. Randomly chosen chest x-rays of public datasets were evaluated by 3 physicians and 1 thoracic radiologist. RESULTS: In comparison with the public datasets of NIH and CheXpert, where AUCs did not significantly drop to 16%, the AUC of the AMC-SNUBH dataset significantly decreased from 2% label noise. Evaluation of the public datasets by 3 physicians and 1 thoracic radiologist showed an accuracy of 65%-80%. CONCLUSIONS: The deep learning-based computer-aided diagnosis model is sensitive to label noise, and computer-aided diagnosis with inaccurate labels is not credible. Furthermore, open datasets such as NIH and CheXpert need to be distilled before being used for deep learning-based computer-aided diagnosis.

Assessment Of Changes In Regional Xenon-Ventilation, Perfusion, And Ventilation-Perfusion Mismatch Using Dual-Energy Computed Tomography After Pharmacological Treatment In Patients With Chronic Obstructive Pulmonary Disease: Visual And Quantitative Analysis.

Purpose: To assess changes in regional ventilation (V), perfusion (Q), and V-Q mismatch in patients with chronic obstructive pulmonary disease (COPD) after pharmacologic treatment using combined xenon-enhanced V and iodine-enhanced Q dual-energy CT (DECT). Patients and methods: Combined V and Q DECT were performed at baseline and after three-month pharmacologic treatment in 52 COPD patients. Anatomically co-registered virtual non-contrast images, V, Q, and V/Qratio maps were obtained. V/Q pattern was visually determined to be matched, mismatched, or reversed-mismatched and compared with the regional parenchymal disease patterns of each segment. DECT parameters for V, Q, and V-Q imbalance were quantified. Results: The parenchymal patterns on CT were not changed at follow-up. The segments with matched V/Q pattern were increased (80.2% to 83.6%) as the segments with reversed-mismatched V/Q pattern were decreased with improving ventilation (17.6% to 13.8%) after treatment. Changes of V/Q patterns were mostly observed in segments with bronchial wall thickening. Compared with patients without bronchial wall thickening, the quantified DECT parameters of V-Q imbalance were significantly improved in patients with bronchial wall thickening (p < 0.05). Changes in forced expiratory volume in one second after treatment were correlated with changes in the quantified DECT parameters (r = 0.327-0.342 or r = -0.406 and -0.303; p < 0.05). Conclusion: DECT analysis showed that the V-Q imbalance was improved after the pharmacological treatment in COPD patients, although the parenchymal disease patterns remained unchanged. This improvement of V-Q imbalance may occur mostly in the areas with bronchial wall thickening.

Quantitative assessment of pulmonary vascular alterations in chronic obstructive lung disease: Associations with pulmonary function test and survival in the KOLD cohort.

PURPOSE: Despite the high prevalence of pulmonary vascular alterations and their substantial impact on chronic obstructive pulmonary disease (COPD), tools for the direct in vivo assessment of pulmonary vascular alterations remain limited. Thus, the purpose of this study was to automatically extract pulmonary vessels from volumetric chest CT and evaluate the associations between the derived quantitative pulmonary vessel features and clinical parameters, including survival, in COPD patients. METHODS: This study included 344 adult COPD patients. Pulmonary vessels were automatically extracted from volumetric chest CT data. Quantitative pulmonary vessel features were obtained from various lung surface areas (LSAs), which are theoretical surface areas drawn at different depths from the pleural borders. The total number of vessels (Ntotal) and number of vessels with vessel area (VA) less than 5 mm2 (N<5mm) were counted as both robust values and as values per 10 cm2 of LSA (Ntotal/LSA; N<5mm/LSA). The average VA (VAmean) and percentage of measured VA in the corresponding LSA (%VA) were measured. Associations between quantitative pulmonary vessel features and clinical parameters, including survival and the pulmonary function test (PFT), were evaluated. RESULTS: The pulmonary vessels were automatically extracted with 100% technical success. Cox regression analysis showed Ntotal/LSA, N<5mm/LSA, VAmean, and %VA to be significant predictors of survival (hazard ratio (HR), 0.80, 0.75, 0.70, 0.49, respectively). Patients classified into high-risk groups by %VA18mm (cut-off = 3.258), chosen because it demonstrated the strongest statistical influence on survival in a univariate Cox analysis, were associated with worse overall survival before (HR, 4.83; p < 0.001) and after adjustment for patient age and BMI (HR, 2.18; p = 0.014). Of the quantitative pulmonary vessel features, Ntotal/LSA, N<5mm/LSA, and %VA were correlated with FEV1, FEV1/FVC, and DLCO in all LSAs. The strongest correlation with PFTs was noted at LSA9mm for both Ntotal (FEV1, r = 0.33; FEV1/FVC, r = 0.51) and N<5mm (FEV1, r = 0.35; FEV1/FVC, r = 0.52). For %VA, the association was most evident at LSA18mm (FEV1, r = 0.27; FEV1/FVC, r = 0.47). Significant moderate to strong correlations were consistently observed between the extent of emphysema and quantitative pulmonary vessel features (r = 0.44-0.66; all p < 0.001). CONCLUSIONS: The automated extraction of pulmonary vessels and their quantitative assessment are technically feasible. Various quantitative pulmonary vessel features demonstrated significant relationships with survival and PFT in COPD patients. Of the various quantitative features, the percentage of total VA measured at 18 mm depth from the pleural surface (%VA18mm) and the number of small vessels counted per 10 cm2 of LSA at 9 mm depth from the pleural surface (N<5mm/LSA9mm) had the strongest predictability for the clinical parameters.

MRI for solitary pulmonary nodule and mass assessment: Current state of the art.

Since the clinical introduction of magnetic resonance imaging (MRI), the chest has been one of its most challenging applications, and many physicists and radiologists have tried since the 1980s to use MR for assessment of different lung diseases as well as mediastinal and pleural diseases. Since then, however, technical advances in sequencing, scanners, and coils, adaptation of parallel imaging techniques, utilization of contrast media, and development of postprocessing tools have been reported by many basic and clinical researchers. As a result, state-of-the-art thoracic MRI is now substituted for traditional imaging techniques and/or plays a complementary role in the management of patients with various chest diseases, and especially in the detection of pulmonary nodules and in thoracic oncology. In addition, MRI has continued to be developed to help overcome the limitations of computed tomography (CT) and nuclear medicine examinations. It can currently provide not only morphological, but also functional, physiological, pathophysiological, and molecular information at 1.5T with a gradual shift from 1.5T to 3T MR systems. In this review, we focus on these recent advances in MRI for pulmonary nodule detection and pulmonary nodule and mass evaluation by using noncontrast-enhanced and contrast-enhanced techniques as well as new molecular imaging methods such as chemical exchange saturation transfer imaging for a comparison with other modalities such as single or multidetector row CT, 18F-fluoro-2-deoxyglucose positron emission tomography (FDG-PET), and/or PET/CT. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1437-1458.

Prediction of survival by texture-based automated quantitative assessment of regional disease patterns on CT in idiopathic pulmonary fibrosis.

OBJECTIVES: To retrospectively investigate whether the baseline extent and 1-year change in regional disease patterns on CT can predict survival of patients with idiopathic pulmonary fibrosis (IPF). METHODS: A total of 144 IPF patients with CT scans at the time of diagnosis and 1 year later were included. The extents of five regional disease patterns were quantified using an in-house texture-based automated system. The fibrosis score was defined as the sum of the extent of honeycombing and reticular opacity. The Cox proportional hazard model was used to determine the independent predictors of survival. RESULTS: A total of 106 patients (73.6%) died during the follow-up period. Univariate analysis revealed that age, baseline forced vital capacity, total lung capacity, diffusing capacity of the lung for carbon monoxide, six-minute walk distance, desaturation, honeycombing, reticular opacity, fibrosis score, and interval changes in honeycombing and fibrosis score were significantly associated with survival. Multivariate analysis revealed that age, desaturation, fibrosis score and interval change in fibrosis score were significant independent predictors of survival (p = 0.003, <0.001, 0.001 and <0.001). The C-index for the developed model was 0.768. CONCLUSION: Texture-based, automated CT quantification of fibrosis can be used as an independent predictor of survival in IPF patients. KEY POINTS: • Automated quantified fibrosis on CT was a significant predictor of survival. • Automated quantified interval change in fibrosis on CT was an independent predictor. • The predictive model showed comparable discriminative power with a C-index of 0.768. • Automated CT quantification can be considered to evaluate prognosis in routine practice.

Added value of prone CT in the assessment of honeycombing and classification of usual interstitial pneumonia pattern.

OBJECTIVE: To retrospectively investigate whether prone CT improves identification of honeycombing and classification of UIP patterns in terms of interobserver agreement and accuracy using pathological results as a reference standard. MATERIALS AND METHODS: Institutional review board approval with waiver of patients' informed consent requirement was obtained. HRCTs of 86 patients with pathologically proven UIP, NSIP and chronic HP between January 2011 and April 2015 were evaluated by 8 observers. Observers were asked to review supine only set and supine and prone combined set and determine the presence of honeycombing and UIP classification (UIP, possible UIP, inconsistent with UIP). The diagnosis was regarded as correct when UIP pattern on CT corresponded to pathological UIP. RESULTS: Interobserver agreement of honeycombing identification among radiologists was only fair on the supine and combined set (weighted κ=0.31 and 0.34). Additional review of prone images demonstrated a significant improvement in interobserver agreement (weighted κ) of UIP classification from 0.25 to 0.33. Prone CT conferred a significant improvement in interobserver agreement of UIP classification for trainee radiologists (from 0.10 to 0.34) while no improvement was found for board-certified radiologists (from 0.35 to 0.31). There were no significant differences in the accuracy of UIP pattern with reference to pathological results between the supine and combined set (78.8% (145/184) and 81.3% (179/220), P=0.612). CONCLUSION: Additional review of prone CT can improve overall interobserver agreement of UIP classification among radiologists with variable experiences, particularly for less experienced radiologists, while no improvement was found in honeycombing identification.

Assessment of regional emphysema, air-trapping and Xenon-ventilation using dual-energy computed tomography in chronic obstructive pulmonary disease patients.

OBJECTIVES: To compare the parenchymal attenuation change between inspiration/expiration CTs with dynamic ventilation change between xenon wash-in (WI) inspiration and wash-out (WO) expiration CTs. METHODS: 52 prospectively enrolled COPD patients underwent xenon ventilation dual-energy CT during WI and WO periods and pulmonary function tests (PFTs). The parenchymal attenuation parameters (emphysema index (EI), gas-trapping index (GTI) and air-trapping index (ATI)) and xenon ventilation parameters (xenon in WI (Xe-WI), xenon in WO (Xe-WO) and xenon dynamic (Xe-Dyna)) of whole lung and three divided areas (emphysema, hyperinflation and normal) were calculated on virtual non-contrast images and ventilation images. Pearson correlation, linear regression analysis and one-way ANOVA were performed. RESULTS: EI, GTI and ATI showed a significant correlation with Xe-WI, Xe-WO and Xe-Dyna (EI R = -.744, -.562, -.737; GTI R = -.621, -.442, -.629; ATI R = -.600, -.421, -.610, respectively, p < 0.01). All CT parameters showed significant correlation with PFTs except forced vital capacity (FVC). There was a significant difference in GTI, ATI and Xe-Dyna in each lung area (p < 0.01). CONCLUSIONS: The parenchymal attenuation change between inspiration/expiration CTs and xenon dynamic change between xenon WI- and WO-CTs correlate significantly. There are alterations in the dynamics of xenon ventilation between areas of emphysema. KEY POINTS: • The xenon ventilation change correlates with the parenchymal attenuation change. • The xenon ventilation change shows the difference between three lung areas. • The combination of attenuation and xenon can predict more accurate PFTs.

The role of dual-energy computed tomography in the assessment of pulmonary function.

The assessment of pulmonary function, including ventilation and perfusion status, is important in addition to the evaluation of structural changes of the lung parenchyma in various pulmonary diseases. The dual-energy computed tomography (DECT) technique can provide the pulmonary functional information and high resolution anatomic information simultaneously. The application of DECT for the evaluation of pulmonary function has been investigated in various pulmonary diseases, such as pulmonary embolism, asthma and chronic obstructive lung disease and so on. In this review article, we will present principles and technical aspects of DECT, along with clinical applications for the assessment pulmonary function in various lung diseases.

Texture-Based Automated Quantitative Assessment of Regional Patterns on Initial CT in Patients With Idiopathic Pulmonary Fibrosis: Relationship to Decline in Forced Vital Capacity.

OBJECTIVE: The aim of our study was to retrospectively determine predictive factors for a decline in forced vital capacity (FVC) on initial CT using texture-based automated quantification in patients with idiopathic pulmonary fibrosis (IPF). MATERIALS AND METHODS: For our study, 193 patients with IPF and 1-year follow-up pulmonary function tests were enrolled in our study. A texture-based automated system used in-house software to quantify six regional CT patterns: normal, ground-glass opacity (GGO), reticular opacity (RO), honeycombing, emphysema, and consolidation. A decline of FVC was defined as a decrease in the initial FVC of more than 10%. RESULTS: A decline of FVC occurred in 32 patients: The mean volume of the decline in FVC was 0.43 ± 0.18 (SD) L. The mean extents of GGO, RO, honeycombing, emphysema, and consolidation in all 193 patients were as follows: 12.3% ± 11.9%, 16.8% ± 9.8%, 7.1% ± 6.7%, 3.9% ± 5.5%, and 2.8% ± 0.8%, respectively. A multivariate analysis revealed that RO was the sole independent predictor for a decline in FVC (p = 0.012; adjusted odds ratio, 1.047). ROC analysis showed that the AUC of RO was 0.641 and that the optimal RO cutoff value was 22.05% (sensitivity, 50.0%; specificity, 81.4%; negative predictive value, 89.1%). CONCLUSION: RO of less than 22.05% in extent can accurately predict stable IPF at 1-year follow-up in terms of FVC.

Visual Assessment of Chest Computed Tomography Findings in Anti-cyclic Citrullinated Peptide Antibody Positive Rheumatoid Arthritis: Is it Associated with Airway Abnormalities?

INTRODUCTION: We aimed to evaluate the association between specific anti-cyclic citrullinated peptide antibody (ACCPA) and pulmonary abnormalities in rheumatoid arthritis (RA) subjects. METHODS: Computed tomography (CT) images of 83 subjects with RA were evaluated in a blind fashion. Enrolled subjects underwent autoantibody testing to determinate titer of ACCPA and rheumatoid factor, and pulmonary function testing. Visual CT assessment included lobar analysis for extent of semi-quantitative total interstitial lung disease score (ILDS) and each airway abnormality score (bronchiectasis, bronchial wall thickening, centrilobular nodules, and expiratory air trapping). Correlation tests, and simple and multiple regression analyses were performed to determine the relationship between the visual CT abnormalities, physiologic parameters, and autoantibody titers. RESULTS: ACCPA-positive subjects had a greater extent and higher prevalence of small airway abnormalities including centrilobular nodules and air trapping compared to ACCPA-negative subjects (all p < 0.05). Bronchiectasis and bronchial wall thickening correlated with the ratio of forced expiratory volume in 1 s and forced vital capacity (FVC) (r = -0.236 and r = -0.329, all p < 0.05), and ILDS correlated with FVC and the diffusing capacity of the lung for carbon monoxide (r = -0.218 and r = -0.366, all p < 0.05). Bronchial wall thickening and air trapping correlated with ACCPA titers (r = 0.235 and r = 0.264, all p < 0.05). Air trapping and bronchial wall thickening were significantly associated with ACCPA titers. CONCLUSION: In ACCPA (+) RA, visual CT assessment of large and small airways beyond RA-ILD, which is attributable to RA-related autoimmunity, can provide valuable information regarding airway abnormalities, regardless of the patients' physiologic airflow limitations.

Assessment of Regional Xenon Ventilation, Perfusion, and Ventilation-Perfusion Mismatch Using Dual-Energy Computed Tomography in Chronic Obstructive Pulmonary Disease Patients.

OBJECTIVES: The aim of this study was to assess the feasibility of combined xenon-enhanced ventilation (V) and iodine-enhanced perfusion (Q) dual-energy computed tomography (DECT) to evaluate regional V and Q status in patients with chronic obstructive pulmonary disease (COPD). MATERIALS AND METHODS: Combined V and Q DECT imaging was performed in 52 prospectively enrolled male COPD patients. Virtual noncontrast images, V maps, and Q maps were anatomically coregistered with deformable registration and evaluated using in-house software. After normalization of the V and Q values of each pixel, normalized V and Q, V/Qratio, and VQmin (ie, the smaller of the V and Q in each pixel) maps were generated. For visual analysis, the V/Qratio pattern was determined to be matched, mismatched, or reversed mismatched and compared with the regional disease patterns--emphysema with/without bronchial wall thickening, bronchial wall thickening, or normal parenchyma--in each segment. The mean V, Q, V/Qratio, and VQmin values and the standard deviation of the V/Qratio (V/QSD) of each patient were quantified and compared with pulmonary function test (PFT) parameters using the Pearson correlation test. RESULTS: Segments with normal parenchyma showed a matched V/Qratio pattern, whereas segments with bronchial wall thickening commonly showed a reversed mismatched V/Qratio pattern. In the emphysema areas, the matched, mismatched, and reversed mismatched patterns were mixed without a dominant pattern. In quantitative analysis, the mean V, Q, VQmin, and V/Qratio values were significantly and positively correlated with PFT parameters (r = 0.290-0.819; P < 0.05). The V/QSD was significantly and negatively correlated with PFT parameters (r = -0.439 to -0.736; P < 0.001). VQmin values showed the best correlation with PFT parameters (r = 0.483-0.819; P < 0.001). CONCLUSIONS: Visual and quantitative assessment of the regional V, Q, V/Qratio, and VQmin is feasible with combined V and Q DECT imaging and significantly correlate with PFT results in COPD patients. Assessing disease patterns using conventional computed tomography images may not provide correct evaluation of regional V and Q in COPD patients with emphysema.

Quantitative Assessment of Global and Regional Air Trappings Using Non-Rigid Registration and Regional Specific Volume Change of Inspiratory/Expiratory CT Scans: Studies on Healthy Volunteers and Asthmatics.

OBJECTIVE: The purpose of this study was to compare air trapping in healthy volunteers with asthmatics using pulmonary function test and quantitative data, such as specific volume change from paired inspiratory CT and registered expiratory CT. MATERIALS AND METHODS: Sixteen healthy volunteers and 9 asthmatics underwent paired inspiratory/expiratory CT. ΔSV, which represents the ratio of air fraction released after exhalation, was measured with paired inspiratory and anatomically registered expiratory CT scans. Air trapping indexes, ΔSV0.4 and ΔSV0.5, were defined as volume fraction of lung below 0.4 and 0.5 ΔSV, respectively. To assess the gravity effect of air-trapping, ΔSV values of anterior and posterior lung at three different levels were measured and ΔSV ratio of anterior lung to posterior lung was calculated. Color-coded ΔSV map of the whole lung was generated and visually assessed. Mean ΔSV, ΔSV0.4, and ΔSV0.5 were compared between healthy volunteers and asthmatics. In asthmatics, correlation between air trapping indexes and clinical parameters were assessed. RESULTS: Mean ΔSV, ΔSV0.4, and ΔSV0.5 in asthmatics were significantly higher than those in healthy volunteer group (all p < 0.05). ΔSV values in posterior lung in asthmatics were significantly higher than those in healthy volunteer group (p = 0.049). In asthmatics, air trapping indexes, such as ΔSV0.5 and ΔSV0.4, showed negative strong correlation with FEF25-75, FEV1, and FEV1/FVC. ΔSV map of asthmatics showed abnormal geographic pattern in 5 patients (55.6%) and disappearance of anterior-posterior gradient in 3 patients (33.3%). CONCLUSION: Quantitative assessment of ΔSV (the ratio of air fraction released after exhalation) shows the difference in extent of air trapping between health volunteers and asthmatics.

Assessment of perfusion pattern and extent of perfusion defect on dual-energy CT angiography: correlations between the causes of pulmonary hypertension and vascular parameters.

OBJECTIVE: To assess perfusion patterns on a dual-energy pulmonary CT angiography (DECTA) of pulmonary hypertension (PHT) with variable causes and to assess whether the extent of perfusion defect can be used in the severity assessment of PHT. MATERIALS AND METHODS: Between March 2007 and February 2011, DECTA scans of 62 consecutive patients (24 men, 38 women; mean age, 58.5 ± 17.3 [standard deviation] years; range, 19-87 years) with PHT were retrospectively included with following inclusion criteria; 1) absence of acute pulmonary thromboembolism, 2) maximal velocity of tricuspid regurgitation jet (TR Vmax) above 3 m/s on echocardiography performed within one week of the DECTA study. Perfusion patterns of iodine map were divided into normal (NL), diffuse heterogeneously decreased (DH), multifocal geographic and multiple peripheral wedging patterns. The extent of perfusion defects (PD), the diameter of main pulmonary artery (MPA) and the ratio of ascending aorta diameter/MPA (aortopulmonary ratio, APR) were measured. Pearson correlation analysis was performed between TR Vmax on echocardiography and CT imaging parameters. RESULTS: Common perfusion patterns of primary PHT were DH (n = 15) and NL (n = 12). The perfusion patterns of secondary PHT were variable. On the correlation analysis, in primary PHT, TR Vmax significantly correlated with PD, MPA and APR (r = 0.52, r = 0.40, r = -0.50, respectively, all p < 0.05). In secondary PHT, TR Vmax significantly correlated with PD and MPA (r = 0.38, r = 0.53, respectively, all p < 0.05). CONCLUSION: Different perfusion patterns are observed on DECTA of PHT according to the causes. PD and MPA are significantly correlated with the TR Vmax.