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. The purpose of this article is 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 [SD] years; 994 men, eight 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, was 0.793 and 0.671, respectively. CAC prevalence was 53.4% by consensus visual assessment, 60.1% by AI software I, and 56.6% 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%. MACEs 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%, respectively (p < .001); for AI software I, it was 1.3%, 3.0%, 7.9%, and 11.3% (p < .001); and for AI software II, it was 1.2%, 3.4%, 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 health care practitioners engaged in developing and overseeing national lung cancer screening programs, highlighting the importance of routine CAC evaluation.

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.

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.

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.

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.

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.

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 (Dh), 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 Dh (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.

Diffusion-weighted MR imaging of upper abdominal organs at different time points: Apparent diffusion coefficient normalization using a reference organ.

PURPOSE: To compare the apparent diffusion coefficient (ADC) of upper abdominal organs acquired at different time points, and to investigate the usefulness of normalization. MATERIALS AND METHODS: We retrospectively evaluated 58 patients who underwent three rounds of magnetic resonance (MR) imaging including diffusion-weighted imaging of the upper abdomen. MR examinations were performed using three different 3.0 Tesla (T) and one 1.5T systems, with variable b value combinations and respiratory motion compensation techniques. The ADC values of the upper abdominal organs from three different time points were analyzed, using the ADC values of the paraspinal muscle (ADCpsm ) and spleen (ADCspleen ) for normalization. Intraclass correlation coefficients (ICC) and comparison of dependent ICCs were used for statistical analysis. RESULTS: The ICCs of the original ADC and ADCpsm showed fair to substantial agreement, while ADCspleen showed substantial to almost perfect agreement. The ICC of ADCspleen of all anatomical regions showed less variability compared with that of the original ADC (P < 0.005). CONCLUSION: Normalized ADC using the spleen as a reference organ significantly decreased variability in measurement of the upper abdominal organs in different MR systems at different time points and could be regarded as an imaging biomarker for future multicenter, longitudinal studies. LEVEL OF EVIDENCE: 5 J. MAGN. RESON. IMAGING 2017;45:1494-1501.

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.

Prevalence and progression of combined pulmonary fibrosis and emphysema in asymptomatic smokers: A case-control study.

OBJECTIVES: We aimed to estimate the prevalence of combined pulmonary fibrosis and emphysema (CPFE) and describe the follow-up CT results of CPFE in asymptomatic smokers. METHODS: This study was retrospective, and approved by an institutional review board. CT images of 2,016 current or previous male smokers who underwent low-dose chest CT at our healthcare centre were reviewed. Quantitative CT analysis was used to assess the extent of emphysema, and two radiologists visually analyzed the extent of fibrosis. Changes in fibrosis (no change, improvement, or progression) were evaluated on follow-up CT imaging (n = 42). Kaplan-Meier survival analysis, multivariate logistic regression and its ROC curve were used for survival and progression analysis. RESULTS: The prevalence of CPFE among asymptomatic male smokers was 3.1 % (63/2,016). The median follow-up period was 50.4 months, and 72.7 % (16/22) of continued smoker had progressing fibrosis on follow-up CT. CPFE progressed more rapidly in continuous smokers than in former smokers (p = 0.002). The 3.5-year follow-up period after initial CPFE diagnosis maximized the sum of sensitivity and specificity of CPFE progression prediction in continuous smokers. CONCLUSIONS: The prevalence of CPFE turned out not to be inconsiderable in asymptomatic male smokers, but serial CT follow-up would be helpful in recognizing disease progression. KEY POINTS: • The prevalence of CPFE in asymptomatic smokers is 3.1 % (63/2,016). • Progression of CPFE is associated with smoking status. • 3.5 years of follow-up period would be needed to identify CPFE progression.

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.

[Using Artificial Intelligence Software for Diagnosing Emphysema and Interstitial Lung Disease]. / íê¸°ì¢ ë° 간질성 폐질환: 인공지능 소프트웨어 사용 경험.

Researchers have developed various algorithms utilizing artificial intelligence (AI) to automatically and objectively diagnose patterns and extent of pulmonary emphysema or interstitial lung diseases on chest CT scans. Studies show that AI-based quantification of emphysema on chest CT scans reveals a connection between an increase in the relative percentage of emphysema and a decline in lung function. Notably, quantifying centrilobular emphysema has proven helpful in predicting clinical symptoms or mortality rates of chronic obstructive pulmonary disease. In the context of interstitial lung diseases, AI can classify the usual interstitial pneumonia pattern on CT scans into categories like normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation. This classification accuracy is comparable to chest radiologists (70%-80%). However, the results generated by AI are influenced by factors such as scan parameters, reconstruction algorithms, radiation doses, and the training data used to develop the AI. These limitations currently restrict the widespread adoption of AI for quantifying pulmonary emphysema and interstitial lung diseases in daily clinical practice. This paper will showcase the authors' experience using AI for diagnosing and quantifying emphysema and interstitial lung diseases through case studies. We will primarily focus on the advantages and limitations of AI for these two diseases.

[Diagnosis of Rib Fracture Using Artificial Intelligence on Chest CT Images of Patients with Chest Trauma]. / 외상 환자의 흉부 CT에서 인공지능을 이용한 갈비뼈 ê³¨ì ˆ 진단.

Purpose: To determine the pros and cons of an artificial intelligence (AI) model developed to diagnose acute rib fractures in chest CT images of patients with chest trauma. Materials and Methods: A total of 1209 chest CT images (acute rib fracture [n = 1159], normal [n = 50]) were selected among patients with chest trauma. Among 1159 acute rib fracture CT images, 9 were randomly selected for AI model training. 150 acute rib fracture CT images and 50 normal ones were tested, and the remaining 1000 acute rib fracture CT images was internally verified. We investigated the diagnostic accuracy and errors of AI model for the presence and location of acute rib fractures. Results: Sensitivity, specificity, positive and negative predictive values, and accuracy for diagnosing acute rib fractures in chest CT images were 93.3%, 94%, 97.9%, 82.5%, and 95.6% respectively. However, the accuracy of the location of acute rib fractures was low at 76% (760/1000). The cause of error in the diagnosis of acute rib fracture seemed to be a result of considering the scapula or clavicle that were in the same position (66%) or some ribs that were not recognized (34%). Conclusion: The AI model for diagnosing acute rib fractures showed high accuracy in detecting the presence of acute rib fractures, but diagnosis of the exact location of rib fractures was limited.

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.

BranchLabelNet: Anatomical Human Airway Labeling Approach using a Dividing-and-Grouping Multi-Label Classification.

Anatomical airway labeling is crucial for precisely identifying airways displaying symptoms such as constriction, increased wall thickness, and modified branching patterns, facilitating the diagnosis and treatment of pulmonary ailments. This study introduces an innovative airway labeling methodology, BranchLabelNet, which accounts for the fractal nature of airways and inherent hierarchical branch nomenclature. In developing this methodology, branch-related parameters, including position vectors, generation levels, branch lengths, areas, perimeters, and more, are extracted from a dataset of 1000 chest computed tomography (CT) images. To effectively manage this intricate branch data, we employ an n-ary tree structure that captures the complicated relationships within the airway tree. Subsequently, we employ a divide-and-group deep learning approach for multi-label classification, streamlining the anatomical airway branch labeling process. Additionally, we address the challenge of class imbalance in the dataset by incorporating the Tomek Links algorithm to maintain model reliability and accuracy. Our proposed airway labeling method provides robust branch designations and achieves an impressive average classification accuracy of 95.94% across fivefold cross-validation. This approach is adaptable for addressing similar complexities in general multi-label classification problems within biomedical systems.

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 (ß = -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 abnormalities in a CT lung cancer screening population: prevalence and progression rate.

PURPOSE: To determine the prevalence of interstitial lung abnormalities (ILAs) at initial computed tomography (CT) examination and the rate of progression of ILAs on 2-year follow-up CT images in a National Lung Screening Trial population studied at a single site. MATERIALS AND METHODS: The study was approved by the institutional review board and informed consent was obtained from all participants. Image review for this study was HIPAA compliant. We reviewed the CT images of 884 cigarette smokers who underwent low-dose CT at a single site in the National Lung Screening Trial. CT findings were categorized as having no evidence of ILA, equivocal for ILA, or ILA. We categorized the type of ILA as nonfibrotic (ground-glass opacity, consolidation, mosaic attenuation), or fibrotic (ground glass with reticular pattern, reticular pattern, honeycombing). We evaluated the temporal change of the CT findings (no change, improvement, or progression) of ILA at 2-year follow-up. A χ(2) with Fisher exact test or unpaired t test was used to determine whether smoking parameters were associated with progression of ILA at 2-year follow-up CT. RESULTS: The prevalence of ILA was 9.7% (86 of 884 participants; 95% confidence interval: 7.9%, 11.9%), with a further 11.5% (102 of 884 participants) who had findings equivocal for ILA. The pattern was fibrotic in 19 (2.1%), nonfibrotic in 52 (5.9%), and mixed fibrotic and nonfibrotic in 15 (1.7%) of the 86 participants with ILA. The percentage of current smokers (P = .001) and mean number of cigarette pack-years (P = .001) were significantly higher in those with ILA than those without. At 2-year follow-up of those with ILA (n = 79), findings of nonfibrotic ILA improved in 49% of cases and progressed in 11%. Fibrotic ILA improved in 0% and progressed in 37% of cases. CONCLUSION: ILA is common in cigarette smokers. Nonfibrotic ILA improved in about 50% of cases, and fibrotic ILA progressed in about 37%.

[Lung Imaging Reporting and Data System (Lung-RADS) in Radiology: Strengths, Weaknesses and Improvement]. / 영상의학에서 폐영상 íŒë ê³¼ 자료체계: ê°•ì , ë‹¨ì , ê·¸ë¦¬ê³ ê°œì„ .

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.