Measurement Variability of Same-Day CT Quantification of Interstitial Lung Disease: A Multicenter Prospective Study

Purpose To investigate quantitative CT (QCT) measurement variability in interstitial lung disease (ILD) on the basis of two same-day CT scans. Materials and Methods Participants with ILD were enrolled in this multicenter prospective study between March and October 2022. Participants underwent two same-day CT scans at an interval of a few minutes. Deep learning-based texture analysis software was used to segment ILD features. Fibrosis extent was defined as the sum of reticular opacity and honeycombing cysts. Measurement variability between scans was assessed with Bland-Altman analyses for absolute and relative differences with 95% limits of agreement (LOA). The contribution of fibrosis extent to variability was analyzed using a multivariable linear mixed-effects model while adjusting for lung volume. Eight readers assessed ILD fibrosis stability with and without QCT information for 30 randomly selected samples. Results Sixty-five participants were enrolled in this study (mean age, 68.7 years ± 10 [SD]; 47 [72%] men, 18 [28%] women). Between two same-day CT scans, the 95% LOA for the mean absolute and relative differences of quantitative fibrosis extent were -0.9% to 1.0% and -14.8% to 16.1%, respectively. However, these variabilities increased to 95% LOA of -11.3% to 3.9% and -123.1% to 18.4% between CT scans with different reconstruction parameters. Multivariable analysis showed that absolute differences were not associated with the baseline extent of fibrosis (P = .09), but the relative differences were negatively associated (β = -0.252, P < .001). The QCT results increased readers' specificity in interpreting ILD fibrosis stability (91.7% vs 94.6%, P = .02). Conclusion The absolute QCT measurement variability of fibrosis extent in ILD was 1% in same-day CT scans. Keywords: CT, CT-Quantitative, Thorax, Lung, Lung Diseases, Interstitial, Pulmonary Fibrosis, Diagnosis, Computer Assisted, Diagnostic Imaging Supplemental material is available for this article. © RSNA, 2024.

Human-airway surface mesh smoothing based on graph convolutional neural networks

BACKGROUND AND OBJECTIVE

A detailed representation of the airway geometry in the respiratory system is critical for predicting precise airflow and pressure behaviors in computed tomography (CT)-image-based computational fluid dynamics (CFD). The CT-image-based geometry often contains artifacts, noise, and discontinuities due to the so-called stair step effect. Hence, an advanced surface smoothing is necessary. The existing smoothing methods based on the Laplacian operator drastically shrink airway geometries, resulting in the loss of information related to smaller branches. This study aims to introduce an unsupervised airway-mesh-smoothing learning (AMSL) method that preserves the original geometry of the three-dimensional (3D) airway for accurate CT-image-based CFD simulations.

METHOD

The AMSL method jointly trains two graph convolutional neural networks (GCNNs) defined on airway meshes to filter vertex positions and face normal vectors. In addition, it regularizes a combination of loss functions such as reproducibility, smoothness and consistency of vertex positions, and normal vectors. The AMSL adopts the concept of a deep mesh prior model, and it determines the self-similarity for mesh restoration without using a large dataset for training. Images of the airways of 20 subjects were smoothed by the AMSL method, and among them, the data of two subjects were used for the CFD simulations to assess the effect of airway smoothing on flow properties.

RESULTS

In 18 of 20 benchmark problems, the proposed smoothing method delivered better results compared with the conventional or state-of-the-art deep learning methods. Unlike the traditional smoothing, the AMSL successfully constructed 20 smoothed airways with airway diameters that were consistent with the original CT images. Besides, CFD simulations with the airways obtained by the AMSL method showed much smaller pressure drop and wall shear stress than the results obtained by the traditional method.

CONCLUSIONS

The airway model constructed by the AMSL method reproduces branch diameters accurately without any shrinkage, especially in the case of smaller airways. The accurate estimation of airway geometry using a smoothing method is critical for estimating flow properties in CFD simulations.

Interstitial Lung Abnormality in Asian Population

Interstitial lung abnormalities (ILAs) are radiologic abnormalities found incidentally on chest computed tomography (CT) that can be show a wide range of diseases, from subclinical lung fibrosis to early pulmonary fibrosis including definitive usual interstitial pneumonia. To clear up confusion about ILA, the Fleischner society published a position paper on the definition, clinical symptoms, increased mortality, radiologic progression, and management of ILAs based on several Western cohorts and articles. Recently, studies on long-term outcome, risk factors, and quantification of ILA to address the confusion have been published in Asia. The incidence of ILA was 7% to 10% for Westerners, while the prevalence of ILA was about 4% for Asians. ILA is closely related to various respiratory symptoms or increased rate of treatment-related complication in lung cancer. There is little difference between Westerners and Asians regarding the clinical importance of ILA. Although the role of quantitative CT as a screening tool for ILA requires further validation and standardized imaging protocols, using a threshold of 5% in at least one zone demonstrated 67.6% sensitivity, 93.3% specificity, and 90.5% accuracy, and a 1.8% area threshold showed 100% sensitivity and 99% specificity in South Korea. Based on the position paper released by the Fleischner society, I would like to report how much ILA occurs in the Asian population, what the prognosis is, and review what management strategies should be pursued in the future.

Coronary Artery Calcification on Low-Dose Lung Cancer Screening CT in South Korea: Visual and Artificial Intelligence-Based Assessment and Association With Cardiovascular Events

Background: Coronary artery calcification (CAC) on lung cancer screening low-dose chest CT (LDCT) is a cardiovascular risk marker. South Korea was the first Asian country to initiate a national LDCT lung cancer screening program, although CAC-related outcomes are poorly explored. Objective: To evaluate CAC prevalence and severity using visual analysis and artificial intelligence (AI) methods and to characterize CAC's association with major adverse cardiovascular events (MACEs) in patients undergoing LDCT in Korea's national lung cancer screening program. Methods: This retrospective study included 1002 patients (mean age, 62.4±5.4 years; 994 men, 8 women) who underwent LDCT at two Korean medical centers between April 2017 and May 2023 as part of Korea's national lung cancer screening program. Two radiologists independently assessed CAC presence and severity using visual analysis, consulting a third radiologist to resolve differences. Two AI software applications were also used to assess CAC presence and severity. MACE occurrences were identified by EMR review. Results: Interreader agreement for CAC presence and severity, expressed as kappa, were 0.793 and 0.671, respectively. CAC prevalence was 53.4% by consensus visual assessment, 56.6% by AI software I, and 60.1% by AI software II. CAC severity was mild, moderate, and severe by consensus visual analysis in 28.0%, 10.3% and 15.1%; by AI software I in 39.9%, 14.0%, and 6.2%; and by AI software II in 34.9%, 14.3%, and 7.3%. MACE occurred in 36 of 625 (5.6%) patients with follow-up after LDCT (median, 1108 days). MACE incidence in patients with no, mild, moderate, and severe CAC, for consensus visual analysis was 1.1%, 5.0%, 2.9%, and 8.6% (p<.001); for AI software I was 1.3%, 3.0%, 7.9%, and 11.3% (p<.001); and for AI software II was 1.2%, 5.0%, 7.7%, and 9.6% (p<.001) Conclusion: For Korea's national lung cancer screening program, MACE occurrence increased significantly with increasing CAC severity, whether assessed by visual analysis or AI software. The study is limited by the large sex imbalance for Korea's national lung cancer screening program. Clinical Impact: The findings provide reference data for healthcare practitioners engaged in developing and overseeing national lung cancer screening programs, highlighting the importance of routine CAC evaluation.

Structural and functional features of asthma participants with fixed airway obstruction using CT imaging and 1D computational fluid dynamics: A feasibility study

Asthma with fixed airway obstruction (FAO) is associated with significant morbidity and rapid decline in lung function, making its treatment challenging. Quantitative computed tomography (QCT) along with data postprocessing is a useful tool to obtain detailed information on airway structure, parenchymal function, and computational flow features. In this study, we aim to identify the structural and functional differences between asthma with and without FAO. The FAO group was defined by a ratio of forced expiratory volume in 1 s (FEV1 ) to forced vital capacity (FVC), FEV1 /FVC <0.7. Accordingly, we obtained two sets of QCT images at inspiration and expiration of asthma subjects without (N = 24) and with FAO (N = 12). Structural and functional QCT-derived airway variables were extracted, including normalized hydraulic diameter, normalized airway wall thickness, functional small airway disease, and emphysema percentage. A one-dimensional (1D) computational fluid dynamics (CFD) model considering airway deformation was used to compare the pressure distribution between the two groups. The computational pressures showed strong correlations with the pulmonary function test (PFT)-based metrics. In conclusion, asthma participants with FAO had worse lung functions and higher-pressure drops than those without FAO.

External validation of deep learning-based automated detection algorithm for chest radiograph: practical issues in outpatient clinic

BACKGROUND

There have been no reports on diagnostic performance of deep learning-based automated detection (DLAD) for thoracic diseases in real-world outpatient clinic.

PURPOSE

To validate DLAD for use at an outpatient clinic and analyze the interpretation time for chest radiographs.

MATERIAL AND METHODS

This is a retrospective single-center study. From 18 January 2021 to 18 February 2021, 205 chest radiographs with DLAD and paired chest CT from 205 individuals (107 men and 98 women; mean ± SD age: 63 ± 8 years) from an outpatient clinic were analyzed for external validation and observer performance. Two radiologists independently reviewed the chest radiographs by referring to the paired chest CT and made reference standards. Two pulmonologists and two thoracic radiologists participated in observer performance tests, and the total amount of time taken during the test was measured.

RESULTS

The performance of DLAD (area under the receiver operating characteristic curve [AUC] = 0.920) was significantly higher than that of pulmonologists (AUC = 0.756) and radiologists (AUC = 0.782) without assistance of DLAD. With help of DLAD, the AUCs were significantly higher for both groups (pulmonologists AUC = 0.853; radiologists AUC = 0.854). A greater than 50% decrease in mean interpretation time was observed in the pulmonologist group with assistance of DLAD compared to mean reading time without aid of DLAD (from 67 s per case to 30 s per case). No significant difference was observed in the radiologist group (from 61 s per case to 61 s per case).

CONCLUSION

DLAD demonstrated good performance in interpreting chest radiographs of patients at an outpatient clinic, and was especially helpful for pulmonologists in improving performance.

Quantitative CT Analysis Based on Smoking Habits and Chronic Obstructive Pulmonary Disease in Patients with Normal Chest CT

PURPOSE

To assess normal CT scans with quantitative CT (QCT) analysis based on smoking habits and chronic obstructive pulmonary disease (COPD).

MATERIALS AND METHODS

From January 2013 to December 2014, 90 male patients with normal chest CT and quantification analysis results were enrolled in our study [non-COPD never-smokers (n = 38) and smokers (n = 45), COPD smokers (n = 7)]. In addition, an age-matched cohort study was performed for seven smokers with COPD. The square root of the wall area of a hypothetical bronchus of internal perimeter 10 mm (Pi10), skewness, kurtosis, mean lung attenuation (MLA), and percentage of low attenuation area (%LAA) were evaluated.

RESULTS

Among patients without COPD, the Pi10 of smokers (4.176 ± 0.282) was about 0.1 mm thicker than that of never-smokers (4.070 ± 0.191, p = 0.047), and skewness and kurtosis of smokers (2.628 ± 0.484 and 6.448 ± 3.427) were lower than never-smokers (2.884 ± 0.624, p = 0.038 and 8.594 ± 4.944, p = 0.02). The Pi10 of COPD smokers (4.429 ± 0.435, n = 7) was about 0.4 mm thicker than never-smokers without COPD (3.996 ± 0.115, n = 14, p = 0.005). There were no significant differences in MLA and %LAA between groups (p > 0.05).

CONCLUSION

Even on normal CT scans, QCT showed that the airway walls of smokers are thicker than never-smokers regardless of COPD and it preceded lung parenchymal changes.

Interstitial Lung Abnormalities at CT in the Korean National Lung Cancer Screening Program: Prevalence and Deep Learning-based Texture Analysis

Background Interstitial lung abnormalities (ILAs) are associated with worse clinical outcomes, but ILA with lung cancer screening CT has not been quantitatively assessed. Purpose To determine the prevalence of ILA at CT examinations from the Korean National Lung Cancer Screening Program and define an optimal lung area threshold for ILA detection with CT with use of deep learning-based texture analysis. Materials and Methods This retrospective study included participants who underwent chest CT between April 2017 and December 2020 at two medical centers participating in the Korean National Lung Cancer Screening Program. CT findings were classified by three radiologists into three groups: no ILA, equivocal ILA, and ILA (fibrotic and nonfibrotic). Progression was evaluated between baseline and last follow-up CT scan. The extent of ILA was assessed visually and quantitatively with use of deep learning-based texture analysis. The Youden index was used to determine an optimal cutoff value for detecting ILA with use of texture analysis. Demographics and ILA subcategories were compared between participants with progressive and nonprogressive ILA. Results A total of 3118 participants were included in this study, and ILAs were observed with the CT scans of 120 individuals (4%). The median extent of ILA calculated by the quantitative system was 5.8% for the ILA group, 0.7% for the equivocal ILA group, and 0.1% for the no ILA group (P < .001). A 1.8% area threshold in a lung zone for quantitative detection of ILA showed 100% sensitivity and 99% specificity. Progression was observed in 48% of visually assessed fibrotic ILAs (15 of 31), and quantitative extent of ILA increased by 3.1% in subjects with progression. Conclusion ILAs were detected in 4% of the Korean lung cancer screening population. Deep learning-based texture analysis showed high sensitivity and specificity for detecting ILA with use of a 1.8% lung area cutoff value. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Egashira and Nishino in this issue.

An unsupervised image registration method employing chest computed tomography images and deep neural networks

BACKGROUND

Deformable image registration is crucial for multiple radiation therapy applications. Fast registration of computed tomography (CT) lung images is challenging because of the large and nonlinear deformation between inspiration and expiration. With advancements in deep learning techniques, learning-based registration methods are considered efficient alternatives to traditional methods in terms of accuracy and computational cost.

METHOD

In this study, an unsupervised lung registration network (LRN) with cycle-consistent training is proposed to align two acquired CT-derived lung datasets during breath-holds at inspiratory and expiratory levels without utilizing any ground-truth registration results. Generally, the LRN model uses three loss functions: image similarity, regularization, and Jacobian determinant. Here, LRN was trained on the CT datasets of 705 subjects and tested using 10 pairs of public CT DIR-Lab datasets. Furthermore, to evaluate the effectiveness of the registration technique, target registration errors (TREs) of the LRN model were compared with those of the conventional algorithm (sum of squared tissue volume difference; SSTVD) and a state-of-the-art unsupervised registration method (VoxelMorph).

RESULTS

The results showed that the LRN with an average TRE of 1.78 ± 1.56 mm outperformed VoxelMorph with an average TRE of 2.43 ± 2.43 mm, which is comparable to that of SSTVD with an average TRE of 1.66 ± 1.49 mm. In addition, estimating the displacement vector field without any folding voxel consumed less than 2 s, demonstrating the superiority of the learning-based method with respect to fiducial marker tracking and the overall soft tissue alignment with a nearly real-time speed.

CONCLUSIONS

Therefore, this proposed method shows significant potential for use in time-sensitive pulmonary studies, such as lung motion tracking and image-guided surgery.

Prevalence and Long-term Outcomes of CT Interstitial Lung Abnormalities in a Health Screening Cohort

Background The association between interstitial lung abnormalities (ILAs) and long-term outcomes has not been reported in Asian health screening populations. Purpose To investigate ILA prevalence in an Asian health screening cohort and determine rates and risks for ILA progression, lung cancer development, and mortality within the 10-year follow-up. Materials and Methods This observational, retrospective multicenter study included patients aged 50 years or older who underwent chest CT at three health screening centers over a 4-year period (2007-2010). ILA status was classified as none, equivocal ILA, and ILA (nonfibrotic or fibrotic). Progression was evaluated from baseline to the last follow-up CT examination, when available. The log-rank test was performed to compare mortality rates over time between ILA statuses. Multivariable Cox proportional hazards models were used to assess factors associated with hazards of ILA progression, lung cancer development, and mortality. Results Of the 2765 included patients (mean age, 59 years ± 7 [SD]; 2068 men), 94 (3%) had a finding of ILA (35 nonfibrotic and 59 fibrotic ILA) and 119 (4%) had equivocal ILA. The median time for CT follow-up and the entire observation was 8 and 12 years, respectively. ILA progression was observed in 80% (48 of 60) of patients with ILA over 8 years. Those with fibrotic and nonfibrotic ILA had a higher mortality rate than those without ILA (P < .001 and P = .01, respectively) over 12 years. Fibrotic ILA was independently associated with ILA progression (hazard ratio [HR], 10.3; 95% CI: 6.4, 16.4; P < .001), lung cancer development (HR, 4.4; 95% CI: 2.1, 9.1; P < .001), disease-specific mortality (HR, 6.7; 95% CI: 3.7, 12.2; P < .001), and all-cause mortality (HR, 2.5; 95% CI: 1.6, 3.8; P < .001) compared with no ILA. Conclusion The prevalence of interstitial lung abnormalities (ILAs) in an Asian health screening cohort was approximately 3%, and fibrotic ILA was an independent risk factor for ILA progression, lung cancer development, and mortality. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Hatabu and Hata in this issue.

[Lung Imaging Reporting and Data System (Lung-RADS) in Radiology: Strengths, Weaknesses and Improvement]

In 2019, the American College of Radiology announced Lung CT Screening Reporting & Data System (Lung-RADS) 1.1 to reduce lung cancer false positivity compared to that of Lung-RADS 1.0 for effective national lung cancer screening, and in December 2022, announced the new Lung-RADS 1.1, Lung-RADS^® 2022 improvement. The Lung-RADS^® 2022 measures the nodule size to the first decimal place compared to that of the Lung-RADS 1.0, to category 2 until the juxtapleural nodule size is < 10 mm, increases the size criterion of the ground glass nodule to 30 mm in category 2, and changes categories 4B and 4X to extremely suspicious. The category was divided according to the airway nodules location and shape or wall thickness of atypical pulmonary cysts. Herein, to help radiologists understand the Lung-RADS^® 2022, this review will describe its advantages, disadvantages, and future improvements.

CT-based lung motion differences in patients with usual interstitial pneumonia and nonspecific interstitial pneumonia

We applied quantitative CT image matching to assess the degree of motion in the idiopathic ILD such as usual interstitial pneumonia (UIP) and nonspecific interstitial pneumonia (NSIP). Twenty-one normal subjects and 42 idiopathic ILD (31 UIP and 11 NSIP) patients were retrospectively included. Inspiratory and expiratory CT images, reviewed by two experienced radiologists, were used to compute displacement vectors at local lung regions matched by image registration. Normalized three-dimensional and two-dimensional (dorsal-basal) displacements were computed at a sub-acinar scale. Displacements, volume changes, and tissue fractions in the whole lung and the lobes were compared between normal, UIP, and NSIP subjects. The dorsal-basal displacement in lower lobes was smaller in UIP patients than in NSIP or normal subjects (p = 0.03, p = 0.04). UIP and NSIP were not differentiated by volume changes in the whole lung or upper and lower lobes (p = 0.53, p = 0.12, p = 0.97), whereas the lower lobe air volume change was smaller in both UIP and NSIP than normal subjects (p = 0.02, p = 0.001). Regional expiratory tissue fractions and displacements showed positive correlations in normal and UIP subjects but not in NSIP subjects. In summary, lung motionography quantified by image registration-based lower lobe dorsal-basal displacement may be used to assess the degree of motion, reflecting limited motion due to fibrosis in the ILD such as UIP and NSIP.

Structural and functional alterations of subjects with cement dust exposure: A longitudinal quantitative computed tomography-based study

Cement dust exposure (CDE) can be a risk factor for pulmonary disease, causing changes in segmental airways and parenchymal lungs. This study investigates longitudinal alterations in quantitative computed tomography (CT)-based metrics due to CDE. We obtained CT-based airway structural and lung functional metrics from CDE subjects with baseline CT and follow-up CT scans performed three years later. From the CT, we extracted wall thickness (WT) and bifurcation angle (θ) at total lung capacity (TLC) and functional residual capacity (FRC), respectively. We also computed air volume (V_air), tissue volume (V_tissue), global lung shape, percentage of emphysema (Emph%), and more. Clinical measures were used to associate with CT-based metrics. Three years after their baseline, the pulmonary function tests of CDE subjects were similar or improved, but there were significant alterations in the CT-based structural and functional metrics. The follow-up CT scans showed changes in θ at most of the central airways; increased WT at the subgroup bronchi; smaller V_air at TLC at all except the right upper and lower lobes; smaller V_tissue at all lobes in TLC and FRC except for the upper lobes in FRC; smaller global lung shape; and greater Emph% at the right upper and lower lobes. CT-based structural and functional variables are more sensitive to the early identification of CDE subjects, while most clinical lung function changes were not noticeable. We speculate that the significant long-term changes in CT are uniquely observed in CDE subjects, different from smoking-induced structural changes.

Quantitative Assessment of Airway Changes in Fibrotic Interstitial Lung Abnormality Patients by Chest CT According to Cumulative Cigarette Smoking

Purpose: The aim of this study was to evaluate the role of Pi10 in patients with fibrotic interstitial lung abnormality (fibrotic ILA) in a chest CT, according to cumulative cigarette smoking. Methods: We retrospectively assessed 54 fibrotic ILA patients and 18 healthy non-smokers (control) who underwent non-enhanced CT and pulmonary function tests. We quantitatively analyzed airway changes (the inner luminal area, airway inner parameter, airway wall thickness, Pi10, skewness, and kurtosis) in the chest CT of fibrotic ILA patients, and the fibrotic ILA patients were categorized into groups based on pack-years: light, moderate, heavy. Airway change data and pulmonary function tests among the three groups of fibrotic ILA patients were compared with those of the control group by one-way ANOVA. Results: Mean skewness (2.58 ± 0.36) and kurtosis (7.64 ± 2.36) in the control group were significantly different from those of the fibrotic ILA patients (1.89 ± 0.37 and 3.62 ± 1.70, respectively, p < 0.001). In fibrotic ILA group, only heavy smokers had significantly increased Pi10 (mean increase 0.04, p = 0.013), increased airway wall thickness of the segmental bronchi (mean increase 0.06 mm, p = 0.005), and decreased lung diffusing capacity for carbon monoxide (p = 0.023). Conclusion: Pi10, as a biomaker of quantitative CT in fibrotic ILA patients, can reveal that smoking affects airway remodeling.

Radiologic-pathologic correlation of interstitial lung abnormalities and predictors for progression and survival

OBJECTIVES

To evaluate radiologic and histologic correlations for interstitial lung abnormalities (ILAs) and to investigate radiologic or pathologic features contributing to disease progression and mortality.

METHODS

From 268 patients who underwent surgical lung biopsy between January 2004 and April 2019, 45 patients with incidentally detected ILA and normal pulmonary function were retrospectively included. CT features were classified as subpleural fibrotic or non-fibrotic, and changes in ILA over at least 2 years of follow-up were evaluated. Histologic findings were categorized as definite, probable, indeterminate, or alternative diagnosis for usual interstitial pneumonia (UIP) patterns. Overall and progression-free survival were calculated using the Kaplan-Meier method, and the Cox proportional hazard method was used to examine predictors for ILA progression and survival.

RESULTS

Among 36 subpleural fibrotic ILA subjects, 25 (69%) showed definite or probable UIP patterns, and 89% (8/9) of subpleural non-fibrotic ILA subjects showed an indeterminate or alternative diagnosis for UIP pattern on histopathology. On the radiologic-pathologic correlation, reticular opacity of fibrotic ILA was correlated with patchy involvement of fibrosis, and ground-glass attenuation of non-fibrotic ILA corresponded to diffuse interstitial thickening. The median progression time of ILA was 54 months, and fibrotic ILA increased the likelihood of progression (hazard ratio, 2.42; p = 0.017). The median survival time of ILA subjects was 123 months, and fibrotic ILA was associated with an increased risk of death (hazard ratio, 9.22; p = 0.025).

CONCLUSIONS

Subpleural fibrotic ILAs are associated with pathologic UIP patterns, and it is important to recognize subpleural fibrotic ILA on CT to predict disease progression and mortality.

KEY POINTS

• In total, 69% of subpleural fibrotic ILA showed definite or probable UIP patterns, while 11% of subpleural non-fibrotic ILA showed definite or probable UIP patterns. • Subpleural fibrotic ILA was associated with an increased rate of progression (hazard ratio, 2.42; p = 0.017), and the median progression-free time was 40 months. • Subpleural fibrotic ILA had an increased risk of death (hazard ratio, 9.22; p = 0.025), and the median survival time was 86 months.

Use of Coronary CT Angiography as a Screening Tool for Coronary Artery Disease in Asymptomatic Healthy Individuals or Patients

심장 돌연사 환자들은 종종 흉통 또는 운동성 호흡곤란 등의 전조증상이 나타나지 않기 때문에 잠재적인 무증상 관상동맥 질환을 조기에 발견하는 것이 매우 중요하다. 관상동맥 전산화단층촬영 혈관조영술은 방사선 노출로 인한 위험성 때문에 무증상 환자에서 스크리닝 검사로 정당화되지 못했었지만 최근에 전산화단층촬영 기술의 비약적인 발전으로 방사선량을 1 mSv 미만으로 최소화함으로써 무증상 환자의 관상동맥 질환 선별 검사의 유용성에 대한 많은 연구가 진행되어 왔다. 그러나, 여전히 무증상 정상인 또는 환자의 관상동맥 질환 선별 검사에 대한 관상동맥 전산화단층촬영 혈관조영술의 유용성에 대해서는 다양한 의견들이 있다. 이 종설에서는 무증상 정상인 또는 환자들에게 관상동맥 질환 선별 검사로 관상동맥 칼슘 점수와 관상동맥 전산화단층촬영 혈관조영술 유용성에 대해서 다양한 문헌고찰을 통해서 알아보았다. 관상동맥 전산화단층촬영 혈관조영술상 무증상 정상인의 2.6%에서 70% 이상의 유의한 관상동맥 협착이 발견되었고, 선별 목적의 관상동맥 전산화단층촬영 혈관조영술이 무증상 건강한 사람의 미래의 심혈관 질환 발생을 예측할 수 있다. 그러나 현재 미국국립보건원에서 진행하고 있는 SCOT-HEART 2 연구가 끝나면 관상동맥 전산화단층촬영 혈관조영술이 무증상 성인의 심혈관 예방에 선별 검사로 적절한지 결정을 내릴 수 있을 것으로 생각된다.

Quantitative computed tomography imaging-based classification of cement dust-exposed subjects with an artificial neural network technique

BACKGROUND AND OBJECTIVE

Cement dust exposure is likely to affect the structural and functional alterations in segmental airways and parenchymal lungs. This study develops an artificial neural network (ANN) model for identifying cement dust-exposed (CDE) subjects using quantitative computed tomography-based airway structural and functional features.

METHODS

We obtained the airway features in five central and five sub-grouped segmental regions and the lung features in five lobar regions and one total lung region from 311 CDE and 298 non-CDE (NCDE) subjects. The five-fold cross-validation method was adopted to train the following classification models:ANN, support vector machine (SVM), logistic regression (LR), and decision tree (DT). For all the classification models, linear discriminant analysis (LDA) and genetic algorithm (GA) were applied for dimensional reduction and hyperparameterization, respectively. The ANN model without LDA was also optimized by the GA method to observe the effect of the dimensional reduction.

RESULTS

The genetically optimized ANN model without the LDA method was the best in terms of the classification accuracy. The accuracy, sensitivity, and specificity of the GA-ANN model with four layers were greater than those of the other classification models (i.e., ANN, SVM, LR, and DT using LDA and GA methods) in the five-fold cross-validation. The average values of accuracy, sensitivity, and specificity for the five-fold cross-validation were 97.0%, 98.7%, and 98.6%, respectively.

CONCLUSIONS

We demonstrated herein that a quantitative computed tomography-based ANN model could more effectively detect CDE subjects when compared to their counterpart models. By employing the model, the CDE subjects may be identified early for therapeutic intervention.

Quantitative CT Image-Based Structural and Functional Changes during Asthma Acute Exacerbations

Asthma acute exacerbations (AE) have been investigated using quantitative computed tomography (QCT)-based imaging metrics, but QCT has not yet been used to investigate a comprehensive set of imaging metrics during AE. This study aims to explore imaging features, captured both at segmental and parenchymal scales, during asthma AE, compared to stable asthma (SA). Two sets of the QCT images at total lung capacity (TLC) and functional residual capacity (FRC) were captured for 14 subjects during asthma AE and in SA phase, respectively. We calculated airway wall thickness (WT), hydraulic diameter (D_h), and airway circularity (Cr) of the 36 segmental airways, percentage of functional small airway disease (fSAD%), percentage of emphysema, tissue fraction (β_tiss), and coefficient of variation of β_tiss (CV of β_tiss). We performed Spearman correlation tests for changes in QCT metrics and pulmonary function tests, measured in AE and SA. During asthma AE, structural metrics, i.e., WT, D_h, and Cr, were not changed significantly. In functional metrics, CV of β_tiss at FRC indicating the heterogeneity of lung tissue distribution was significantly increased, while the mean of β_tiss at FRC did not change during AE. An increase of fSAD% during AE was most correlated with a decrease of forced expiratory volume in 1 second and forced vital capacity, especially in the lower lobes. This study demonstrates that the heterogeneous feature of β_tiss measured at lower lobes is more noticeable during asthma AE, compared with other traditional imaging metrics. This metric could be utilized to identify unique features during asthma AE.

Assessing the speed-accuracy trade-offs of popular convolutional neural networks for single-crop rib fracture classification

Rib fractures are injuries commonly assessed in trauma wards. Deep learning has demonstrated state-of-the-art accuracy for a variety of tasks, including image classification. This paper assesses the speed-accuracy trade-offs and general suitability of four popular convolutional neural networks to classify rib fractures from axial computed tomography imagery. We transfer learned InceptionV3, ResNet50, MobileNetV2, and VGG16 models, additionally training "decomposed" models comprised of taking only the first n blocks for each block for each architecture. Given that acute (new) fractures are generally most important to detect, we trained two types of models: a classful model with classes acute, old (healed), and normal (non-fractured); and a binary model with acute vs. the other classes. We found that the first 7 blocks of InceptionV3 achieved the best results and general speed-accuracy trade-off. The classful model achieved a 5-fold cross-validation average accuracy and macro recall of 96.00% and 94.0%, respectively. The binary model achieved a 5-fold cross-validation average accuracy, macro recall, and area under receiver operator characteristic curve of 97.76%, 94.6%, and 94.7%, respectively. On a Windows 10 PC with 32GB RAM and an Nvidia 1080ti GPU, the model's average CPU and GPU per-crop inference times were 13.6 and 12.2 ms, respectively. Compared to the InceptionV3 Block 7 classful model, a radiologist with 9 years of experience was less accurate but more sensitive to acute fractures; meanwhile, the deep learning model had fewer false positive diagnoses and better sensitivity to old fractures and normal ribs. The Cohen's Kappa between the two was 0.813.

Generation-based study of airway remodeling in smokers with normal-looking CT with normalization to control inter-subject variability

PURPOSE

With the help of quantitative computed tomography (QCT), it is possible to identify smoking-associated airway remodeling. However, there is currently little information on whether QCT-based airway metrics are sensitive to early airway wall remodeling in subclinical phases of smoking-associated airway disease. This study aimed to evaluate a predictive model that normalized airway parameters and investigate structural airway alterations in smokers with normal-looking CT using the normalization scheme.

METHODS

In this retrospective analysis, 222 non-smokers (male 97, female 125) and 69 smokers (male 66, female 3) from January 2014 to December 2016 were included, and airway parameters were quantitatively analyzed. To control inter-subject variability, multiple linear regressions of tracheal wall thickness (WT), diameter (D), and luminal area (LA) were performed, adjusted for age, sex, and height. Using this normalization scheme, airway parameters with matched generation were compared between smokers and non-smokers.

RESULTS

Using the normalization scheme, it was possible to assess generation-based structural alterations of the airways in subclinical smokers. Smokers showed diffuse luminal narrowing of airways for most generations (P < 0.05, except 3rd generation), no change in wall thickness of the proximal bronchi (1st-3rd generation), and a thinning of distal airways (P <0.05, ≥4th generation).

CONCLUSION

QCT assessment for subclinical smokers can help identify minimal structural changes in airways induced by smoking.

Identification of hub genes and pathways in the development of gastric cancer by gene co‑expression network analysis

There are many risk factors for gastric cancer (GC), including chronic atrophic gastritis, which involves multiple genes and signaling pathways. Weighted gene co-expression network analysis (WGCNA) was performed on GSE111762 to construct free-scale gene co-expression networks and identified four significant modules that consisted of blue, dark orange, dark red and dark violet. In each module, genes with the most connectivity were selected as hub genes, including G antigen 12J (GAGE12J) in blue, proline, histidine and glycine rich 1 (PHGR1) in dark orange, DNA polymerase gamma 2, accessory subunit (POLG2) in dark red and collagen type XXI alpha 1 chain (COL21A1) in dark violet. The transcription level of COL21A1 and GAGE12J was up-regulated in atrophic gastritis vs normal gastric mucosa, but down-regulated in GC vs atrophic gastritis. PHGR1 was consistently down-regulated from normal gastric mucosa to GC, while POLG2 was up-regulated. Gene set enrichment analysis (GSEA) was then conducted to study the biological functions of hub genes in the development of GC. It showed that multiple tumorigenesis-related pathways were enriched, including peroxisome, DNA repair and KRAS signaling pathway in COL21A1, IL6-JAK-STAT3, epithelial mesenchymal transition (EMT) and TNFα-NF-κB signaling pathway in PHGR1, MYC targets, E2F targets and angiogenesis in POLG2 and peroxisome, Notch signaling pathway and androgen response in GAGE12J. The identified four genes, especially for COL21A1, PHGR1 and POLG2, were important in GC tumorigenesis and affected many cancer-related pathways.

Bronchopleural Fistula after Surgery: Therapeutic Efficacy of Bronchial Occluders

목적

수술 후 발생하는 기관지늑막루에 대한 bronchial occluder device (이하 BOD)의 유용성 및 효과성을 평가하고자 한다.

대상과 방법

2009년도부터 2019년까지 폐암 및 폐결핵으로 수술 후 발생한 7명의 기관지늑막루 환자들 중에 BOD 치료를 실시한 6명의 환자를 대상으로 하였다. 폐암 환자가 5명, 폐결핵 환자는 1명이었다. 남자가 5명, 여자가 1명이었으며 59세에서 74세(평균: 69세)이었다. 진단은 전산화단층기관지 촬영과 기관지내시경으로 실시하였다. 시술은 진단 후 1주에서 2주 사이에 실시하였다. 치료에 사용된 BOD의 기술적 성공 및 임상적 성공을 평가하였다. 시술 후 임상적 치료 효과와 생존 기간 및 부가적인 치료에 대하여 알아보았다.

결과

총 6명 환자 모두에서 시술을 성공적으로 실시하였다. 임상성공 5명에서 있었고, 1명에서는 임상부분성공이 있었다. 임상실패한 경우는 없었다. 기구의 이동이나 천공의 합병증은 없었다. 2명은 전산화단층기관지 촬영으로, 4명은 기관지내시경으로 기관지늑막루를 진단하였다. 수술은 폐엽절제술(lobectomy) 2명, 폐이엽절제술(bilobectomy) 2명, 그리고 전폐절제술(pneumonectomy) 2명이었다. 수술 후 진단까지 걸린 시간은 1개월에서 34개월(평균: 10개월)이었다. 4명(59~103일: 평균 80.5일)은 사망하였고 2명(313일, 3331일)은 생존하였다. 사망 원인이 2명은 기저질환의 악화, 1명은 폐부종 및 흉수, 1명은 폐렴이었다. 한 명에서 부가적인 카테타 배액술, 2명에서 흉관 삽입을 유지하였다.

결론

폐절제 수술 후 발생할 수 있는 기관지늑막루 치료에 있어서 BOD는 유용하고 효과적인 치료 방법이다.

Relative Regional Air Volume Change Maps at the Acinar Scale Reflect Variable Ventilation in Low Lung Attenuation of COPD patients

OBJECTIVES

The purpose of this study was to investigate regional air volume changes at the acinar scale of the lung in chronic obstructive pulmonary disease (COPD) patients using an image registration technique.

MATERIALS AND METHODS

Thirty-four emphysema patients and 24 subjects with normal chest CT and pulmonary function test (PFT) results were included in this retrospective study for which informed consent was waived by the institutional review board. After lung segmentation, a mass-preserving image registration technique was used to compute relative regional air volume changes (RRAVCs) between inspiration and expiration CT scans. After determining the appropriate thresholds of RRAVCs for low ventilation areas (LVAs), they were displayed and analyzed using color maps on the background inspiration CT image, and compared with the low attenuation area (LAA) map. Correlations between quantitative CT parameters and PFTs were assessed using Pearson's correlation test, and parameters were compared between emphysema and normal-CT patients using the Student's t-test.

RESULTS

LVA percentage with an RRAVC threshold of 0.5 (%LVA_0.5) showed the strongest correlations with FEV₁/FVC (r = -0.566), FEV₁ (r = -0.534), %LAA_-950insp (r = 0.712), and %LAA_-856exp (r = 0.775). %LVA_0.5 was significantly higher (P < 0.001) in COPD patients than normal subjects. Despite the identical appearance of emphysematous lesions on the LAA_-950insp map, the RRAVC map depicted a wide range of ventilation differences between these LAA clusters.

CONCLUSION

RRAVC-based %LVA_0.5 correlated well with FEV₁/FVC, FEV₁, %LAA_-950insp and %LAA_-856exp. RRAVC holds the potential for providing additional acinar scale functional information for emphysematous LAAs in inspiratory CT images, providing the basis for a novel set for emphysematous phenotypes.

Interstitial Lung Abnormalities: What Radiologists Should Know

Interstitial lung abnormalities (ILAs) are radiologic abnormalities found incidentally on chest CT that are potentially related to interstitial lung diseases. Several articles have reported that ILAs are associated with increased mortality, and they can show radiologic progression. With the increased recognition of ILAs on CT, the role of radiologists in reporting them is critical. This review aims to discuss the clinical significance and radiologic characteristics of ILAs to facilitate and enhance their management.

Variability in interpretation of low-dose chest CT using computerized assessment in a nationwide lung cancer screening program: comparison of prospective reading at individual institutions and retrospective central reading

OBJECTIVES

To evaluate the degree of variability in computer-assisted interpretation of low-dose chest CTs (LDCTs) among radiologists in a nationwide lung cancer screening (LCS) program, through comparison with a retrospective interpretation from a central laboratory.

MATERIALS AND METHODS

Consecutive baseline LDCTs (n = 3353) from a nationwide LCS program were investigated. In the institutional reading, 20 radiologists in 14 institutions interpreted LDCTs using computer-aided detection and semi-automated segmentation systems for lung nodules. In the retrospective central review, a single radiologist re-interpreted all LDCTs using the same system, recording any non-calcified nodules ≥ 3 mm without arbitrary rejection of semi-automated segmentation to minimize the intervention of radiologist's discretion. Positive results (requiring additional follow-up LDCTs or diagnostic procedures) were initially classified by the lung CT screening reporting and data system (Lung-RADS) during the interpretation, while the classifications based on the volumetric criteria from the Dutch-Belgian lung cancer screening trial (NELSON) were retrospectively applied. Variabilities in positive rates were assessed with coefficients of variation (CVs).

RESULTS

In the institutional reading, positive rates by the Lung-RADS ranged from 7.5 to 43.3%, and those by the NELSON ranged from 11.4 to 45.0% across radiologists. The central review exhibited higher positive rates by Lung-RADS (20.0% vs. 27.3%; p < .001) and the NELSON (23.1% vs. 37.0%; p < .001), and lower inter-institution variability (CV, 0.30 vs. 0.12, p = .003 by Lung-RADS; CV, 0.25 vs. 0.12, p = .014 by the NELSON) compared to the institutional reading.

CONCLUSION

Considerable inter-institution variability in the interpretation of LCS results is caused by different usage of the computer-assisted system.

KEY POINTS

• Considerable variability existed in the interpretation of screening LDCT among radiologists partly from the different usage of the computerized system. • A retrospective reading of low-dose chest CTs in the central laboratory resulted in reduced variability but an increased positive rate.

Quantitative CT-based image registration metrics provide different ventilation and lung motion patterns in prone and supine positions in healthy subjects

Background

Previous studies suggested that the prone position (PP) improves oxygenation and reduces mortality among patients with acute respiratory distress syndrome (ARDS). However, the mechanism of this clinical benefit of PP is not completely understood. The aim of the present study was to quantitatively compare regional characteristics of lung functions in the PP with those in the supine position (SP) using inspiratory and expiratory computed tomography (CT) scans.

Methods

Ninety subjects with normal pulmonary function and inspiration and expiration CT images were included in the study. Thirty-four subjects were scanned in PP, and 56 subjects were scanned in SP. Non-rigid image registration-based inspiratory-expiratory image matching assessment was used for regional lung function analysis. Tissue fractions (TF) were computed based on the CT density and compared on a lobar basis. Three registration-derived functional variables, relative regional air volume change (RRAVC), volumetric expansion ratio (J), and three-dimensional relative regional displacement (s*) were used to evaluate regional ventilation and deformation characteristics.

Results

J was greater in PP than in SP in the right middle lobe (P = 0 .025), and RRAVC was increased in the upper and right middle lobes (P < 0.001). The ratio of the TF on inspiratory and expiratory scans, J, and RRAVC at the upper lobes to those at the middle and lower lobes and that ratio at the upper and middle lobes to those at the lower lobes of were all near unity in PP, and significantly higher than those in SP (0.98–1.06 vs 0.61–0.94, P < 0.001).

Conclusion

We visually and quantitatively observed that PP not only induced more uniform contributions of regional lung ventilation along the ventral-dorsal axis but also minimized the lobar differences of lung functions in comparison with SP. This may help in the clinician’s search for an understanding of the benefits of the application of PP to the patients with ARDS or other gravitationally dependent pathologic lung diseases.

Trial registration

Retrospectively registered.

Early detection of ankylosing spondylitis using texture features and statistical machine learning, and deep learning, with some patient age analysis

Ankylosing spondylitis (AS) is an arthritis with symptoms visible in medical imagery. This paper proposes, to the authors' best knowledge, the first use of statistical machine learning- and deep learning-based classifiers to detect erosion, an early AS symptom, via analysis of computed tomography (CT) imagery, giving some consideration to patient age in so doing. We used gray-level co-occurrence matrices and local binary patterns to generate input features to machine learning algorithms, specifically k-nearest neighbors (k-NN) and random forest. Deep learning solutions based on a modified InceptionV3 architecture were designed and tested, with one classifier produced by training with a cross-entropy loss function and another produced by additionally seeking to minimize validation loss. We found that the random forest classifiers outperform the k-NN classifiers and achieve an eightfold cross-validation average accuracy, recall, and area under receiver operator characteristic curve (ROC AUC) of 96.0%, 92.9%, and 0.97, respectively, for erosion vs. young control patients, and 82.4%, 80.6%, and 0.91, respectively, for erosion vs. old control patients. We found that the deep learning classifier trained without minimizing validation loss was best and achieves an eightfold cross-validation accuracy, recall, and ROC AUC of 99.0%, 97.5%, and 0.97, respectively, for erosion vs. all (combined young and old) control patients; this classifier outperforms a musculoskeletal radiologist with 9 years of experience in raw sensitivity and specificity by 8.4% and 9.5%, respectively. Despite the relatively small dataset on which we trained and cross-validated, our results indicate the potential of machine and deep learning to aid AS diagnosis, and further research using larger datasets should be conducted.

Quantitative CT-based structural alterations of segmental airways in cement dust-exposed subjects

Background

Dust exposure has been reported as a risk factor of pulmonary disease, leading to alterations of segmental airways and parenchymal lungs. This study aims to investigate alterations of quantitative computed tomography (QCT)-based airway structural and functional metrics due to cement-dust exposure.

Methods

To reduce confounding factors, subjects with normal spirometry without fibrosis, asthma and pneumonia histories were only selected, and a propensity score matching was applied to match age, sex, height, smoking status, and pack-years. Thus, from a larger data set (N = 609), only 41 cement dust-exposed subjects were compared with 164 non-cement dust-exposed subjects. QCT imaging metrics of airway hydraulic diameter (D_h), wall thickness (WT), and bifurcation angle (θ) were extracted at total lung capacity (TLC) and functional residual capacity (FRC), along with their deformation ratios between TLC and FRC.

Results

In TLC scan, dust-exposed subjects showed a decrease of D_h (airway narrowing) especially at lower-lobes (p < 0.05), an increase of WT (wall thickening) at all segmental airways (p < 0.05), and an alteration of θ at most of the central airways (p < 0.001) compared with non-dust-exposed subjects. Furthermore, dust-exposed subjects had smaller deformation ratios of WT at the segmental airways (p < 0.05) and θ at the right main bronchi and left main bronchi (p < 0.01), indicating airway stiffness.

Conclusions

Dust-exposed subjects with normal spirometry demonstrated airway narrowing at lower-lobes, wall thickening at all segmental airways, a different bifurcation angle at central airways, and a loss of airway wall elasticity at lower-lobes. The airway structural alterations may indicate different airway pathophysiology due to cement dusts.

Breast Cancer Classification in Automated Breast Ultrasound Using Multiview Convolutional Neural Network with Transfer Learning

To assist radiologists in breast cancer classification in automated breast ultrasound (ABUS) imaging, we propose a computer-aided diagnosis based on a convolutional neural network (CNN) that classifies breast lesions as benign and malignant. The proposed CNN adopts a modified Inception-v3 architecture to provide efficient feature extraction in ABUS imaging. Because the ABUS images can be visualized in transverse and coronal views, the proposed CNN provides an efficient way to extract multiview features from both views. The proposed CNN was trained and evaluated on 316 breast lesions (135 malignant and 181 benign). An observer performance test was conducted to compare five human reviewers' diagnostic performance before and after referring to the predicting outcomes of the proposed CNN. Our method achieved an area under the curve (AUC) value of 0.9468 with five-folder cross-validation, for which the sensitivity and specificity were 0.886 and 0.876, respectively. Compared with conventional machine learning-based feature extraction schemes, particularly principal component analysis (PCA) and histogram of oriented gradients (HOG), our method achieved a significant improvement in classification performance. The proposed CNN achieved a >10% increased AUC value compared with PCA and HOG. During the observer performance test, the diagnostic results of all human reviewers had increased AUC values and sensitivities after referring to the classification results of the proposed CNN, and four of the five human reviewers' AUCs were significantly improved. The proposed CNN employing a multiview strategy showed promise for the diagnosis of breast cancer, and could be used as a second reviewer for increasing diagnostic reliability.

[Clinical study on the effects of global end-diastolic volume index-directed fluid resuscitation on the prognosis of chronic heart failure patients with septic shock]

Objective: To investigate the effect of global end-diastolic volume index (GEDI)-guided fluid resuscitation on the prognosis of patients with chronic heart failure and septic shock. Methods: This study was a prospective randomized controlled study. Consecutive eligible patients were divided into 2 groups according to the random number table method: control group (n=21) and experimental group (n=20). On the basis of routine treatment, patients in the control group received early goal-directed therapy until the central venous pressure (CVP) reaching 8-12 mmHg (1 mmHg=0.133 kPa), mean arterial pressure reaching over 65 mmHg, urine volume reaching over 0.5 ml·kg(-1)·h(-1), and central venous oxygen saturation reaching more than 70%. On the basis of routine treatment, patients in the experimental group were monitored continuously on cardiac output with pulse indication and fluid resuscitation guided by volume index GEDI. The GEDI should be maintained on the range of 680-800 ml/m(2). The remaining resuscitation goals were the same as control group. General clinical data of the two groups were collected at admission. Negative fluid balance onset time, duration of mechanical ventilation, ICU mortality and 28-day mortality were compared between the two groups. The outcomes were recorded as listed: start time of negative fluid balance, duration of mechanical ventilation, mortality in ICU and 28-day mortality. Results: There was no significant difference in age, sex, weight, APACHE Ⅱ score, SOFA score and NYHA functional class score between the two groups (all P>0.05). The negative liquid balance onset time in the control group was 3.5 (2.5, 4.0) days, which was significantly longer than that in the experimental group (2.6 (2.0, 3.0) days,U=115.0, P=0.012). The duration of mechanical ventilation was 355 (118, 552) hours in the control group, which was significantly longer than that in the experimental group (132 (36.75, 233.3) hours, U=130, P=0.038). The ICU mortality was 38.1% (8/21) in the control group, tended to be higher than that in the experimental group (20.0%(4/20), χ(2)=1.620, P=0.203). The 28-day mortality was 42.9% (9/21) in the control group, similar as in the experimental group (25.0%(5/20), χ(2)=1.482,P=0.477). Conclusion: Fluid resuscitation guided by volume index (GEDI) may improve the prognosis of patients with chronic heart failure complicated with septic shock.

Non-diagnostic Results of Percutaneous Transthoracic Needle Biopsy: A Meta-analysis

Non-diagnostic results can affect the diagnostic performance of percutaneous transthoracic needle biopsy (PTNB) but have not been critically meta-analyzed yet. To meta-analyze the incidence and malignancy rate of non-diagnostic results, 3-by-2 table approaches rather than the conventional 2-by-2 approaches are needed to know its impact on the diagnostic performance of PTNB. A systematic literature search identified studies evaluating the diagnostic performance of PTNB with extractable outcomes. A total of 143 studies with 35,059 biopsies were included. The pooled incidence of non-diagnostic results was 6.8% (95% CI, 6.0-7.6%; I² = 0.91). The pooled malignancy rate of non-diagnostic results was 59.3% (95% CI, 51.7-66.8%; I² = 0.80), and was correlated with the prevalence of malignancy (correlation coefficient, 0.66; 95% CI, 0.42-0.91). Pooled percentage decrease of sensitivity and specificity due to non-diagnostic results were 4.5% (95% CI, 3.2-5.7%; I² = 0.64) and 10.7% (95% CI, 7.7-13.7%; I² = 0.70), respectively, and the pooled incidence of non-diagnostic results was 4.4% (95% CI, 3.2-5.8%; I² = 0.83) in lesions ultimately diagnosed as malignancies and 10.4% (95% CI, 7.5-13.8%; I² = 0.74) in benign disease. In conclusion, non-diagnostic results averagely occurred in 6.8% of PTNB and more than half of the results were malignancies. The non-diagnostic results decreased specificity and sensitivity by 10.7% and 4.5%, respectively, demanding efforts to minimize the non-diagnostic results in PTNB.

Structural and Functional Features on Quantitative Chest Computed Tomography in the Korean Asian versus the White American Healthy Non-Smokers

OBJECTIVE

Considering the different prevalence rates of diseases such as asthma and chronic obstructive pulmonary disease in Asians relative to other races, Koreans may have unique airway structure and lung function. This study aimed to investigate unique features of airway structure and lung function based on quantitative computed tomography (QCT)-imaging metrics in the Korean Asian population (Koreans) as compared with the White American population (Whites).

MATERIALS AND METHODS

QCT data of healthy non-smokers (223 Koreans vs. 70 Whites) were collected, including QCT structural variables of wall thickness (WT) and hydraulic diameter (Dh) and functional variables of air volume, total air volume change in the lung (ΔVair), percent emphysema-like lung (Emph%), and percent functional small airway disease-like lung (fSAD%). Mann-Whitney U tests were performed to compare the two groups.

RESULTS

As compared with Whites, Koreans had smaller volume at inspiration, ΔVair between inspiration and expiration (p < 0.001), and Emph% at inspiration (p < 0.001). Especially, Korean females had a decrease of ΔVair in the lower lobes (p < 0.001), associated with fSAD% at the lower lobes (p < 0.05). In addition, Koreans had smaller Dh and WT of the trachea (both, p < 0.05), correlated with the forced expiratory volume in 1 second (R = 0.49, 0.39; all p < 0.001) and forced vital capacity (R = 0.55, 0.45; all p < 0.001).

CONCLUSION

Koreans had unique features of airway structure and lung function as compared with Whites, and the difference was clearer in female individuals. Discriminating structural and functional features between Koreans and Whites enables exploration of inter-racial differences of pulmonary disease in terms of severity, distribution, and phenotype.

LOGIS (LOcalization of Ground-glass-opacity and pulmonary lesions for mInimal Surgery) registry: Design and Rationale

Background and purpose

An optimal pulmonary localization technique for video-assisted thoracic surgery (VATS) of small lung nodules has not yet been established. The LOcalization of Ground-glass-opacity and pulmonary lesions for mInimal Surgery (LOGIS) registry aims to establish a multicenter database and investigate the usefulness and safety of localization techniques for small pulmonary lesions in individuals undergoing VATS.

Methods/Design

The LOGIS registry is a large-scale, multicenter cohort study, aiming to enroll 825 patients at 10 institutions. Based on the inclusion and exclusion criteria, all study participants with pulmonary lesions indicated for VATS will be screened and enrolled at each site. All study participants will undergo preoperative lesion localization by the hook-wire or lipiodol localization methods according to site-specific methods. Within a few hours of marking, thoracoscopic surgery will be done under general anesthesia by experienced thoracoscopic surgeons. The primary endpoints are the success and complication rates of the two localization techniques. Secondary endpoints include procedure duration, recurrence rate, and all-cause mortality. Study participant enrollment will be completed within 2 years. Procedure success rates and incidence of complications will be analyzed based on computed tomography findings. Procedure duration, recurrence rate, and all-cause mortality will be compared between the two techniques. The study will require 5 years for completion, including 6 months of preparation, 3.5 years for recruitment, and 1 year of follow-up endpoint assessment.

Discussion

The LOGIS registry, once complete, will provide objective comparative results regarding the usefulness and safety of the lipiodol and hook-wire localization techniques.

A Review of the Use of Cardiac Computed Tomography for Evaluating the Mitral Valve before and after Mitral Valve Repair

The role of cardiac computed tomography (CT) for evaluating the mitral valve (MV) has been limited since echocardiography is the main method of evaluation. However, recent advances in cardiac CT have enable detailed evaluation of the anatomy and geometry of the MV. We describe assessments of the anatomy and coaptation geometric parameters of normal MVs, and also review repair of diseased/damaged MV. We also discuss pre- and post-surgical imaging of MV pathology using cardiac CT and various CT images. We found that cardiac CT could be used as an alternative imaging modality to echocardiography for pre-operative MV evaluation and to predict clinical outcomes following repair.

CT Quantification of Lungs and Airways in Normal Korean Subjects

Objective

To measure and compare the quantitative parameters of the lungs and airways in Korean never-smokers and current or former smokers (“ever-smokers”).

Materials and Methods

Never-smokers (n = 119) and ever-smokers (n = 45) who had normal spirometry and visually normal chest computed tomography (CT) results were retrospectively enrolled in this study. For quantitative CT analyses, the low attenuation area (LAA) of LAA_I-950, LAA_E-856, CT attenuation value at the 15th percentile, mean lung attenuation (MLA), bronchial wall thickness of inner perimeter of a 10 mm diameter airway (Pi10), total lung capacity (TLC_CT), and functional residual capacity (FRC_CT) were calculated based on inspiratory and expiratory CT images. To compare the results between groups according to age, sex, and smoking history, independent t test, one way ANOVA, correlation test, and simple and multiple regression analyses were performed.

Results

The values of attenuation parameters and volume on inspiratory and expiratory quantitative computed tomography (QCT) were significantly different between males and females (p < 0.001). The MLA and the 15th percentile value on inspiratory QCT were significantly lower in the ever-smoker group than in the never-smoker group (p < 0.05). On expiratory QCT, all lung attenuation parameters were significantly different according to the age range (p < 0.05). Pi10 in ever-smokers was significantly correlated with forced expiratory volume in 1 second/forced vital capacity (r = −0.455, p = 0.003). In simple and multivariate regression analyses, TLC_CT, FRC_CT, and age showed significant associations with lung attenuation (p < 0.05), and only TLC_CT was significantly associated with inspiratory Pi10.

Conclusion

In Korean subjects with normal spirometry and visually normal chest CT, there may be significant differences in QCT parameters according to sex, age, and smoking history.

Differentiating Smoking-Related Interstitial Fibrosis (SRIF) from Usual Interstitial Pneumonia (UIP) with Emphysema Using CT Features Based on Pathologically Proven Cases

OBJECTIVE

To differentiate smoking-related interstitial fibrosis (SRIF) from usual interstitial pneumonia (UIP) with emphysema on CT in combined pulmonary fibrosis and emphysema (CPFE) patients.

MATERIALS AND METHODS

This study was approved by the institutional review board and informed consent was waived. We included 65 patients who underwent lung biopsy under the suspicion of UIP pattern on HRCT, and after radiologic-pathologic correlation, they were divided into three groups: UIP without emphysema (n = 30), UIP with emphysema (n = 26), and SRIF (n = 9). The quantitative extent of emphysema in the entire lung was visually assessed and fibrotic patterns were qualitatively analyzed based on six characteristics (asymmetry, juxta-subpleural sparing, emphysema beside the honeycombing area, absence of ground grass attenuation/reticulation in honeycombing area, inhomogeneous honeycombing, and absence of honeycombing in the upper lobes). Kaplan-Meier analysis was used for survival analysis, and logistic regression with a receiver operating characteristic curve was used to predict the possibility of SRIF.

RESULTS

In qualitative analysis of fibrotic patterns, SRIF tended to exhibit more than three of six fibrotic features, whereas UIP with emphysema demonstrated about two of these characteristics (p = 0.035). In addition, SRIF had a higher extent of emphysema than UIP with emphysema when they have same amount of fibrosis (p = 0.014). In patients with SRIF, 5-year survival rate was 85.7%, while it was 40.7% in UIP with emphysema patients (p = 0.035).

CONCLUSION

Fibrotic CT patterns and survival rate differed between SRIF and UIP with emphysema among CPFE patients, which explains the variable prognosis of CPFE.

Factors associated with abnormal pulmonary function test among subjects with normal chest radiography: The Korean National Health and Nutrition Examination Survey

Determinants of abnormal lung function among subjects with normal chest radiography have not been widely evaluated. We investigated 12 109 participants with normal chest radiographs from the Korean National Health and Nutrition Examination Survey. Factors associated with abnormal pulmonary function were male gender, age ≥50, smoking history and a clinical history of cough or sputum production. Pulmonary function tests should be considered in population-based screening, especially in men over 50 years old with a smoking history.

Intra-Individual, Inter-Vendor Comparison of Diffusion-Weighted MR Imaging of Upper Abdominal Organs at 3.0 Tesla with an Emphasis on the Value of Normalization with the Spleen

OBJECTIVE

To compare the apparent diffusion coefficient (ADC) values of upper abdominal organs with 2 different 3.0 tesla MR systems and to investigate the usefulness of normalization using the spleen.

MATERIALS AND METHODS

Forty-one patients were enrolled in this prospective study, of which, 35 patients (M:F, 27:8; mean age ± standard deviation, 62.3 ± 12.3 years) were finally analyzed. In addition to the routine liver MR protocol, single-shot spin-echo echo-planar diffusion-weighted imaging using b values of 0, 50, 400, and 800 s/mm(2) in 2 different MR systems was performed. ADC values of the liver, spleen, pancreas, kidney and liver lesion (if present) were measured and analyzed. ADC values of the spleen were used for normalization. The Pearson correlation, Spearman correlation, paired sample t test, Wilcoxon signed rank test and Bland-Altman method were used for statistical analysis.

RESULTS

For all anatomical regions and liver lesions, both non-normalized and normalized ADC values from 2 different MR systems showed significant correlations (r = 0.5196-0.8488). Non-normalized ADC values of both MR systems differed significantly in all anatomical regions and liver lesions (p < 0.001). However, the normalized ADC of all anatomical regions and liver lesions did not differ significantly (p = 0.065-0.661), with significantly lower coefficient of variance than that of non-normalized ADC (p < 0.009).

CONCLUSION

Normalization of the abdominal ADC values using the spleen as a reference organ reduces differences between different MR systems, and could facilitate consistent use of ADC as an imaging biomarker for multi-center or longitudinal studies.

Pulmonary malignant melanoma with distant metastasis assessed by positron emission tomography-computed tomography

Melanoma is a cutaneous malignant neoplasm of melanocytes. Primary malignant melanoma (MM) of the lung is very rare. Although previous reports have described the radiologic features of pulmonary MM, its rarity means that many factors are unknown. Thus, radiologic diagnosis is very difficult. Furthermore, there is little information regarding diagnostic application and/or the usefulness of [¹⁸F]‐fluorine‐2‐fluoro‐2‐deoxy‐D‐glucose positron emission tomography‐computed tomography (FDG‐PET‐CT) for primary pulmonary MM. A 69‐year‐old patient with a productive cough lasting three weeks was admitted to our hospital. Chest CT showed a large single mass with a multi‐lobulated margin and homogeneous enhancement in the right upper lobe, which was subsequently diagnosed as a primary pulmonary MM with multiple metastases. On PET‐CT images, the pulmonary mass and multiple bone lesions showed very increased uptakes of FDG. Considering that pulmonary metastasis from a mucocutaneous melanoma is the main differential diagnosis of primary pulmonary MM, systemic assessment of the whole body is more important than for other types of lung malignancies. This report introduces PET‐CT as a useful diagnostic modality for pulmonary MM, especially in cases of distant multiple metastases.

Mediastinal Desmoid Tumor With Remarkably Rapid Growth: A Case Report

Desmoid tumors (DTs) are a group of rare and benign soft tissue tumors that result from monoclonal proliferation of well-differentiated fibroblasts. Since DTs tend to infiltrate and compress adjacent structures, the location of DTs is one of the most crucial factors for determining the severity of the disease. Furthermore, DTs can further complicate the clinical course of patients when the growth is remarkably rapid, especially for DTs occurring in anatomically critical compartments, including the thoracic cavity.The authors report a case of a 71-year-old man with a known mediastinal mass incidentally detected 4 months ago, presenting dyspnea with right-sided atelectasis and massive pleural effusion. Imaging studies revealed a 16.4 × 9.4-cm fibrous mass with high glucose metabolism in the anterior mediastinum. The mass infiltrated into the chest wall and also displaced the mediastinum contralaterally. Interestingly, the tumor had an extremely rapid doubling time of 31.3 days.En bloc resection of the tumor was performed as a curative as well as a diagnostic measure. Histopathologic examination showed spindle cells with low cellularity and high collagen deposition in the stroma. Immunohistochemical staining was positive for nuclear β-catenin. Based on these pathologic findings, the mass was diagnosed as DT. After surgery, there has been no evidence of recurrence of disease in the patient.This patient presents a mediastinal DT with extremely rapid growth. Notably, the doubling time of DT in our case was the shortest among reported cases of DT. Our experience also highlights the benefits of early interventional strategy, especially for rapidly growing DTs in the thoracic cavity.

Visually stratified CT honeycombing as a survival predictor in combined pulmonary fibrosis and emphysema

OBJECTIVE

To determine whether visually stratified CT findings and pulmonary function variables are helpful in predicting mortality in patients with combined pulmonary fibrosis and emphysema (CPFE).

METHODS

We retrospectively identified 113 patients with CPFE who underwent high-resolution CT between January 2004 and December 2009. The extent of emphysema and fibrosis on CT was visually assessed using a 6- or 5-point scale, respectively. Univariate and multivariate Cox proportional regression analyses were performed to determine the prognostic value of visually stratified CT findings and pulmonary function variables in patients with CPFE. Differences in 5-year survival rates in patients with CPFE according to the extent of honeycombing were calculated using Kaplan-Meier analysis.

RESULTS

An increase in the extent of visually stratified honeycombing on CT [hazard ratio (HR), 1.95; p = 0.018; 95% confidence interval (CI), 1.12-3.39] and reduced diffusing capacity of lung for carbon monoxide (DLCO) (HR, 0.97; p = 0.017; 95% CI, 0.94-0.99) were independently associated with increased mortality. In patients with CPFE, the 5-year survival rate was 78.5% for <5% honeycombing, 55.7% for 5-25% honeycombing, 32% for 26-50% honeycombing and 33.3% for >50% honeycombing on CT.

CONCLUSION

The >50% honeycombing on CT and reduced DLCO are important prognostic factors in CPFE.

ADVANCES IN KNOWLEDGE

Visual estimation of honeycombing extent on CT can help in the prediction of prognosis in CPFE.

A Case Report of Mass-Forming Aspergillus Tracheobronchitis Successfully Treated with Voriconazole

Aspergillus tracheobronchitis (ATB) represents a rare disease entity accounting for 3.5% to 5.0% of cases of invasive aspergillosis. Particularly, mass-forming ATB is extremely rare, and clinical reports are limited. Given that the patency of the trachea and bronchi are essential for maintaining sufficient airflow, a central airway mass originating from an Aspergillus infection is likely to be associated with severe clinical manifestations and fatal outcomes. Although several therapeutic options for ATB have been suggested, including medication and airway interventions, the optimal choice of treatment in diverse clinical conditions remains under discussion.We report a case of ATB that initially manifested as severe dyspnea and total atelectasis of the left lung in a patient with newly detected diabetes.Radiographic study, bronchoscopy, and pathologic findings of the lesion revealed mass-forming type of ATB.Interestingly, our patient's symptoms dramatically resolved with voriconazole without further invasive intervention.This clinical experience highlights the beneficial role of voriconazole in the treatment of rare and potentially fatal cases of ATB.