Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study.

BACKGROUND: An artificial intelligence (AI) model using chest radiography (CXR) may provide good performance in making prognoses for COVID-19. OBJECTIVE: We aimed to develop and validate a prediction model using CXR based on an AI model and clinical variables to predict clinical outcomes in patients with COVID-19. METHODS: This retrospective longitudinal study included patients hospitalized for COVID-19 at multiple COVID-19 medical centers between February 2020 and October 2020. Patients at Boramae Medical Center were randomly classified into training, validation, and internal testing sets (at a ratio of 8:1:1, respectively). An AI model using initial CXR images as input, a logistic regression model using clinical information, and a combined model using the output of the AI model (as CXR score) and clinical information were developed and trained to predict hospital length of stay (LOS) ≤2 weeks, need for oxygen supplementation, and acute respiratory distress syndrome (ARDS). The models were externally validated in the Korean Imaging Cohort of COVID-19 data set for discrimination and calibration. RESULTS: The AI model using CXR and the logistic regression model using clinical variables were suboptimal to predict hospital LOS ≤2 weeks or the need for oxygen supplementation but performed acceptably in the prediction of ARDS (AI model area under the curve [AUC] 0.782, 95% CI 0.720-0.845; logistic regression model AUC 0.878, 95% CI 0.838-0.919). The combined model performed better in predicting the need for oxygen supplementation (AUC 0.704, 95% CI 0.646-0.762) and ARDS (AUC 0.890, 95% CI 0.853-0.928) compared to the CXR score alone. Both the AI and combined models showed good calibration for predicting ARDS (P=.079 and P=.859). CONCLUSIONS: The combined prediction model, comprising the CXR score and clinical information, was externally validated as having acceptable performance in predicting severe illness and excellent performance in predicting ARDS in patients with COVID-19.

Correction: Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study.

[This corrects the article DOI: 10.2196/42717.].

Erratum: Correction of Affiliations in the Article "Establishment of a Nationwide Korean Imaging Cohort of Coronavirus Disease 2019".

This corrects the article on p. e413 in vol. 35, PMID: 33258333.

Quantitative assessment the longitudinal changes of pulmonary vascular counts in chronic obstructive pulmonary disease.

BACKGROUND: Chest computed tomography (CT) is a widely used method to assess morphological and dynamic abnormalities in chronic obstructive pulmonary disease (COPD). The small pulmonary vascular cross-section (CSA), quantitatively extracted from volumetric CT, is a reliable indicator for predicting pulmonary vascular changes. CSA is associated with the severity of symptoms, pulmonary function tests (PFT) and emphysema and in COPD patients the severity increases over time. We analyzed the correlation longitudinal changes in pulmonary vascular parameters with clinical parameters in COPD patients. MATERIALS AND METHODS: A total of 288 subjects with COPD were investigated during follow up period up to 6 years. CT images were classified into five subtypes from normal to severe emphysema according to percentage of low-attenuation areas less than -950 and -856 Hounsfield units (HU) on inspiratory and expiratory CT (LAA-950, LAA-856exp). Total number of vessels (Ntotal) and total number of vessels with area less than 5 mm2 (N<5 mm) per 1 cm2 of lung surface area (LSA) were measured at 6 mm from the pleural surface. RESULTS: Ntotal/LSA and N<5 mm/LSA changed from 1.16 ± 0.27 to 0.87 ± 0.2 and from 1.02 ± 0.22 to 0.78 ± 0.22, respectively, during Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage progression. Both parameters changed from normal to severe emphysema according to CT subtype from 1.39 ± 0.21 to 0.74 ± 0.17 and from 1.18 ± 0.19 to 0.67 ± 0.15, respectively. LAA-950 and LAA-856exp were negatively correlated with Ntotal/LSA (r = - 0.738, - 0.529) and N<5 mm /LSA (r = - 0.729, -- .497). On the other hand, pulmonary function test (PFT) results showed a weak correlation with Ntotal/LSA and N<5 mm/LSA (r = 0.205, 0.210). The depth in CT subtypes for longitudinal change both Ntotal/LSA and N<5 mm/LSA was (- 0.032, - 0.023) and (- 0.027) in normal and SAD, respectively. CONCLUSIONS: Quantitative computed tomography features faithfully reflected pulmonary vessel alterations, showing in particular that pulmonary vascular alteration started.

Association between plasma sRAGE and emphysema according to the genotypes of AGER gene.

BACKGROUND: Higher soluble receptor for advanced glycation end product (sRAGE) levels are considered to be associated with severe emphysema. However, the relationship remains uncertain when the advanced glycation end-product specific receptor (AGER) gene is involved. We aimed to analyse the association between sRAGE levels and emphysema according to the genotypes of rs2070600 in the AGER gene. METHODS: We genotyped rs2070600 and measured the plasma concentration of sRAGE in each participant. Emphysema was quantified based on the chest computed tomography findings. We compared sRAGE levels based on the presence or absence and severity of emphysema in each genotype. Multiple logistic and linear regression models were used for the analyses. RESULTS: A total of 436 participants were included in the study. Among them, 64.2% had chronic obstructive pulmonary disease and 34.2% had emphysema. Among the CC-genotyped participants, the sRAGE level was significantly higher in participants without emphysema than in those with emphysema (P < 0.001). In addition, sRAGE levels were negatively correlated with emphysema severity in CC-genotyped patients (r = - 0.268 P < 0.001). Multiple regression analysis revealed that sRAGE was an independent protective factor for the presence of emphysema (adjusted odds ratio, 0.24; 95% confidence interval (CI) 0.11-0.51) and severity of emphysema (ß = - 3.28, 95% CI - 4.86 to - 1.70) in CC-genotyped participants. CONCLUSION: Plasma sRAGE might be a biomarker with a protective effect on emphysema among CC-genotyped patients of rs2070600 on the AGER gene. This is important in determining the target group for the future prediction and treatment of emphysema.

A comparative study of chest CT findings regarding the effects of regional dust exposure on patients with COPD living in urban areas and rural areas near cement plants.

BACKGROUND: The clinical and radiological presentation of chronic obstructive pulmonary disease (COPD) is heterogenous depending on the characterized sources of inflammation. This study aimed to evaluate COPD phenotypes associated with specific dust exposure. METHODS: This study was designed to compare the characteristics, clinical outcomes and radiological findings between two prospective COPD cohorts representing two distinguishing regions in the Republic of Korea; COPD in Dusty Area (CODA) and the Korean Obstructive Lung Disease (KOLD) cohort. A total of 733 participants (n = 186 for CODA, and n = 547 for KOLD) were included finally. A multivariate analysis to compare lung function and computed tomography (CT) measurements of both cohort studies after adjusting for age, sex, education, body mass index, smoking status, and pack-year, Charlson comorbidity index, and frequency of exacerbation were performed by entering the level of FEV1(%), biomass exposure and COPD medication into the model in stepwise. RESULTS: The mean wall area (MWA, %) became significantly lower in COPD patients in KOLD from urban and metropolitan area than those in CODA cohort from cement dust area (mean ± standard deviation [SD]; 70.2 ± 1.21% in CODA vs. 66.8 ± 0.88% in KOLD, p = 0.028) after including FEV1 in the model. COPD subjects in KOLD cohort had higher CT-emphysema index (EI, 6.07 ± 3.06 in CODA vs. 20.0 ± 2.21 in KOLD, p < 0.001, respectively). The difference in the EI (%) was consistently significant even after further adjustment of FEV1 (6.12 ± 2.88% in CODA vs. 17.3 ± 2.10% in KOLD, p = 0.002, respectively). However, there was no difference in the ratio of mean lung density (MLD) between the two cohorts (p = 0.077). Additional adjustment for biomass parameters and medication for COPD did not alter the statistical significance after entering into the analysis with COPD medication. CONCLUSIONS: Higher MWA and lower EI were observed in COPD patients from the region with dust exposure. These results suggest that the imaging phenotype of COPD is influenced by specific environmental exposure.

Word Embedding Reveals Cyfra 21-1 as a Biomarker for Chronic Obstructive Pulmonary Disease.

BACKGROUND: Although patients with chronic obstructive pulmonary disease (COPD) experience high morbidity and mortality worldwide, few biomarkers are available for COPD. Here, we analyzed potential biomarkers for the diagnosis of COPD by using word embedding. METHODS: To determine which biomarkers are likely to be associated with COPD, we selected respiratory disease-related biomarkers. Degrees of similarity between the 26 selected biomarkers and COPD were measured by word embedding. And we infer the similarity with COPD through the word embedding model trained in the large-capacity medical corpus, and search for biomarkers with high similarity among them. We used Word2Vec, Canonical Correlation Analysis, and Global Vector for word embedding. We evaluated the associations of selected biomarkers with COPD parameters in a cohort of patients with COPD. RESULTS: Cytokeratin 19 fragment (Cyfra 21-1) was selected because of its high similarity and its significant correlation with the COPD phenotype. Serum Cyfra 21-1 levels were determined in patients with COPD and controls (4.3 ± 5.9 vs. 3.9 ± 3.6 ng/mL, P = 0.611). The emphysema index was significantly correlated with the serum Cyfra 21-1 level (correlation coefficient = 0.219, P = 0.015). CONCLUSION: Word embedding may be used for the discovery of biomarkers for COPD and Cyfra 21-1 may be used as a biomarker for emphysema. Additional studies are needed to validate Cyfra 21-1 as a biomarker for COPD.

Long-term exposure to PM10 and NO2 in relation to lung function and imaging phenotypes in a COPD cohort.

BACKGROUND: Ambient air pollution can contribute to the development and exacerbation of COPD. However, the influence of air pollution on objective COPD phenotypes, especially from imaging, is not well studied. We investigated the influence of long-term exposure to air pollution on lung function and quantitative imaging measurements in a Korean cohort of participants with and without COPD diagnosis. METHODS: Study participants (N = 457 including 296 COPD cases) were obtained from the COPD in Dusty Areas (CODA) cohort. Annual average concentrations of particulate matter less than or equal to 10 µm in diameter (PM10) and nitrogen dioxide (NO2) were estimated at the participants' residential addresses using a spatial air pollution prediction model. All the participants underwent volumetric computerized tomography (CT) and spirometry measurements and completed survey questionnaires. We examined the associations of PM10 and NO2 with FVC, FEV1, emphysema index, and wall area percent, using linear regression models adjusting for age, gender, education, smoking, height, weight, and COPD medication. RESULTS: The age of study participants averaged 71.7 years. An interquartile range difference in annual PM10 exposure of 4.4 µg/m3 was associated with 0.13 L lower FVC (95% confidence interval (CI), - 0.22- -0.05, p = 0.003). Emphysema index (mean = 6.36) was higher by 1.13 (95% CI, 0.25-2.02, p = 0.012) and wall area percent (mean = 68.8) was higher by 1.04 (95% CI, 0.27-1.80, p = 0.008). Associations with imaging phenotypes  were not observed with NO2. CONCLUSIONS: Long-term exposure to PM10 correlated with both lung function and COPD-relevant imaging phenotypes in a Korean cohort.

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.

Emphysema quantification using low-dose computed tomography with deep learning-based kernel conversion comparison.

OBJECTIVE: This study determined the effect of dose reduction and kernel selection on quantifying emphysema using low-dose computed tomography (LDCT) and evaluated the efficiency of a deep learning-based kernel conversion technique in normalizing kernels for emphysema quantification. METHODS: A sample of 131 participants underwent LDCT and standard-dose computed tomography (SDCT) at 1- to 2-year intervals. LDCT images were reconstructed with B31f and B50f kernels, and SDCT images were reconstructed with B30f kernels. A deep learning model was used to convert the LDCT image from a B50f kernel to a B31f kernel. Emphysema indices (EIs), lung attenuation at 15th percentile (perc15), and mean lung density (MLD) were calculated. Comparisons among the different kernel types for both LDCT and SDCT were performed using Friedman's test and Bland-Altman plots. RESULTS: All values of LDCT B50f were significantly different compared with the values of LDCT B31f and SDCT B30f (p < 0.05). Although there was a statistical difference, the variation of the values of LDCT B50f significantly decreased after kernel normalization. The 95% limits of agreement between the SDCT and LDCT kernels (B31f and converted B50f) ranged from - 2.9 to 4.3% and from - 3.2 to 4.4%, respectively. However, there were no significant differences in EIs and perc15 between SDCT and LDCT converted B50f in the non-chronic obstructive pulmonary disease (COPD) participants (p > 0.05). CONCLUSION: The deep learning-based CT kernel conversion of sharp kernel in LDCT significantly reduced variation in emphysema quantification, and could be used for emphysema quantification. KEY POINTS: • Low-dose computed tomography with smooth kernel showed adequate performance in quantifying emphysema compared with standard-dose CT. • Emphysema quantification is affected by kernel selection and the application of a sharp kernel resulted in a significant overestimation of emphysema. • Deep learning-based kernel normalization of sharp kernel significantly reduced variation in emphysema quantification.

Imaging findings of Stenotrophomonas maltophilia pneumonia: emphasis on CT findings between immunocompromised and immunocompetent patients.

BACKGROUND: Stenotrophomonas maltophilia (S. maltophilia) is a globally emerging, rare, waterborne, aerobic, gram-negative, multiple-drug-resistant organism, most commonly associated with respiratory tract infection in humans. Computed tomography (CT) findings in patients with S. maltophilia pneumonia are rarely reported. PURPOSE: To compare CT findings between immunocompromised and immunocompetent patients, and to determine characteristic imaging findings of S. maltophilia pneumonia. MATERIAL AND METHODS: CT findings of eight immunocompromised and 29 immunocompetent patients with proven S. maltophilia pneumonia were reviewed retrospectively. Different patterns of CT abnormalities between immunocompromised and immunocompetent patients were compared by Fisher's exact test. RESULTS: Patchy ground-glass opacities (GGOs) were the most common CT findings, present in 36 (97.3%) of the 37 patients. Among the patients with patchy GGOs, consolidation was seen in 29 (78.4%) patients, and centrilobular nodules were noted in 15 (40.5%) patients. The transaxial distribution of the parenchymal abnormalities was predominantly randomly distributed in 30 (81.1%) cases. Regarding longitudinal plane involvement, the predominant zonal distributions were the diffuse distribution (n=23, 62.2%) and the lower lung zone (n=14, 37.8%). None of the patients showed upper lung zone predominance. The proportion of patients with parenchymal CT findings or associated findings in the immunocompromised patients was not significantly different from that of the immunocompetent patients. However, lower lung zone predominance on the longitudinal plane was significantly more common in immunocompetent patients than in immunocompromised patients (14/29 vs. 0/8, P=0.015). And diffuse distribution of parenchymal abnormalities on a longitudinal plane was significantly more frequent in immunocompromised patients than in immunocompetent patients (8/8 vs. 15/29, P=0.015). CONCLUSION: The most common CT patterns of S. maltophilia pneumonia in immunocompromised and immunocompetent patients were patchy GGOs and consolidation. However, in immunocompetent patients, parenchymal abnormalities were more predominately distributed in lower lung zone than in immunocompromised patients; and in immunocompromised patients, parenchymal abnormalities were more diffusely distributed than in immunocompetent patients.

Establishment of a Nationwide Korean Imaging Cohort of Coronavirus Disease 2019.

BACKGROUND: The Korean Society of Thoracic Radiology (KSTR) recently constructed a nation-wide coronavirus disease 2019 (COVID-19) database and imaging repository, referred to the Korean imaging cohort of COVID-19 (KICC-19) based on the collaborative efforts of its members. The purpose of this study was to provide a summary of the clinico-epidemiological data and imaging data of the KICC-19. METHODS: The KSTR members at 17 COVID-19 referral centers retrospectively collected imaging data and clinical information of consecutive patients with reverse transcription polymerase chain reaction-proven COVID-19 in respiratory specimens from February 2020 through May 2020 who underwent diagnostic chest computed tomography (CT) or radiograph in each participating hospital. RESULTS: The cohort consisted of 239 men and 283 women (mean age, 52.3 years; age range, 11-97 years). Of the 522 subjects, 201 (38.5%) had an underlying disease. The most common symptoms were fever (n = 292) and cough (n = 245). The 151 patients (28.9%) had lymphocytopenia, 86 had (16.5%) thrombocytopenia, and 227 patients (43.5%) had an elevated CRP at admission. The 121 (23.4%) needed nasal oxygen therapy or mechanical ventilation (n = 38; 7.3%), and 49 patients (9.4%) were admitted to an intensive care unit. Although most patients had cured, 21 patients (4.0%) died. The 465 (89.1%) subjects underwent a low to standard-dose chest CT scan at least once during hospitalization, resulting in a total of 658 CT scans. The 497 subjects (95.2%) underwent chest radiography at least once during hospitalization, which resulted in a total of 1,475 chest radiographs. CONCLUSION: The KICC-19 was successfully established and comprised of 658 CT scans and 1,475 chest radiographs of 522 hospitalized Korean COVID-19 patients. The KICC-19 will provide a more comprehensive understanding of the clinical, epidemiological, and radiologic characteristics of patients with COVID-19.

Computed tomography-derived area and density of pectoralis muscle associated disease severity and longitudinal changes in chronic obstructive pulmonary disease: a case control study.

BACKGROUND: Muscle wasting is associated with prognosis in patients with chronic obstructive pulmonary disease (COPD). The cross-sectional area of skeletal muscles on computed tomography (CT) could serve as a method to evaluate body composition. The present study aimed to determine the ability of CT-derived pectoralis muscle area (PMA) and pectoralis muscle density (PMD) to determine the severity of COPD and change in longitudinal pulmonary function in patients with COPD. METHODS: A total of 293 participants were enrolled in this study, a whom 222 had undergone at least two spirometry measurements within 3 years after baseline data acquisition. PMA and PMD were measured from a single axial slice of chest CT above the aortic arch at baseline. The emphysema index and bronchial wall thickness were quantitatively assessed in all scans. The generalized linear model was used to determine the correlation between PMA and PMD measurements and pulmonary function. RESULTS: PMA and PMD were significantly associated with baseline lung function and the severity of emphysema (P < 0.05). Patients with the lowest PMA and PMD exhibited significantly more severe airflow obstruction (ß = - 0.06; 95% confidence interval: - 0.09 to - 0.03]. PMA was statistically associated with COPD assessment test (CAT) score (P = 0.033). However, PMD did not exhibit statistically significant correlation with either CAT scores or modified Medical Research Council scores (P > 0.05). Furthermore, neither PMA nor PMD were associated with changes in forced expiratory volume in 1 s over a 3-year periods. CONCLUSIONS: CT-derived features of the pectoralis muscle may be helpful in predicting disease severity in patients with COPD, but are not necessarily associated with longitudinal changes in lung function.

Inflammatory biomarkers and radiologic measurements in never-smokers with COPD: A cross-sectional study from the CODA cohort.

Various biomarkers have emerged as potential surrogates to represent various subgroups of chronic obstructive pulmonary disease (COPD), which manifest with different phenotypes. However, the biomarkers representing never-smokers with COPD have not yet been well elucidated. The aim of this study was to evaluate the associations of certain serum and radiological biomarkers with the presence of COPD in never-smokers. To explore the associations of serum and radiological biomarkers with the presence of COPD in never-smokers, we conducted a cross-sectional patient cohort study composed of never-smokers from the COPD in Dusty Areas (CODA) cohort, consisting of subjects living in dusty areas near cement plants in South Korea. Of the 131 never-smokers in the cohort, 77 (58.8%) had COPD. There were no significant differences in the number of subjects with high levels of inflammatory biomarkers (>90th percentile of never-smokers without COPD), including white blood cell count, total bilirubin, interleukin (IL)-6, IL-8, and C-reactive protein, or radiologic measurements (including emphysema index and mean wall area percentage) between never-smokers with COPD and those without COPD. However, the number of subjects with high uric acid was significantly higher in never-smokers with COPD than never-smokers without COPD (31.2% (24/77) vs. 11.1% (6/54); p = 0.013). In addition, multivariate analysis revealed that high uric acid was significantly associated with the presence of COPD in never-smokers (adjusted relative risk: 1.63; 95% confidence interval: 1.21, 2.18; p = 0.001). Our study suggests that high serum levels of uric acid might be a potential biomarker for assessing the presence of COPD in never-smokers.

Fused aortic valve without an elliptical-shaped systolic orifice in patients with severe aortic stenosis: cardiac computed tomography is useful for differentiation between bicuspid aortic valve with raphe and tricuspid aortic valve with commissural fusion.

OBJECTIVE: The objective is to determine cardiac computed tomography (CCT) features capable of differentiating between bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV) in severe aortic stenosis (AS) patients with fused cusp and without elliptical-shaped systolic orifices. METHODS: We retrospectively enrolled 53 patients who had severe AS with fused cusps and without an elliptical-shaped systolic orifice on CCT and who had undergone surgery. CCT features were analyzed using: 1) aortic valve findings including cusp size, cusp area, opening shape, midline calcification, fusion length, calcium volume score, and calcium grade; 2) diameters of ascending and descending aorta, and main pulmonary artery; and 3) rheumatic mitral valve findings. The variables were evaluated using univariate and multivariate logistic regression analyses. RESULTS: At surgery, 19 patients had BAV and 34 had TAV. CCT features including uneven cusp size, uneven cusp area, round-shaped systolic orifice, longer cusp fusion, and dilatation of ascending aorta were significantly associated with BAV (P < 0.05). In particular, fusion length (OR, 1.76; P = 0.001), uneven cusp area (OR, 10.46; P = 0.012), and midline calcification (OR, 0.08; P = 0.013) were strongly associated with BAV. CONCLUSION: CCT provides diagnostic clues that helps differentiate between BAV with raphe and TAV with commissural fusion in patients with severe AS. KEY POINTS: • Accurate morphologic assessment of the aortic valve is important for treatment planning. • It is difficult to differentiate BAV from TAV with a fused cusp. • CCT provides diagnostic clues for the differentiation of BAV and TAV.

Diagnosis of esophageal varices on liver CT: is thin-section reconstruction necessary?

BACKGROUND/AIMS: The aim of this study was to evaluate the effect of 1 mm, 3 mm, and 5 mm section thicknesses of liver CT on the detection and grading of esophageal varices in cirrhotic patients. METHODOLOGY: A total of 219 consecutive cirrhotic patients who had undergone both upper endoscopy and 64-channel liver CT were included. Portal phase images of each CT were reconstructed with a section thickness of 1 mm, 3 mm, and 5 mm. Four radiologists independently reviewed the 3 image sets. The observers evaluated the presence of esophageal varices on a 5-point confidence scale and measured the maximal short diameter of the largest varix identified. Sensitivity, specificity and predictive value calculation and receiver operating characteristic analysis were performed. Correlation between CT measurements and endoscopic grading as the reference standard was assessed. RESULTS: The averaged Az values at 1 mm, 3 mm, and 5 mm image sets were 0.936, 0.946, and 0.935, respectively, and they were not significantly different among the 4 observers. When a 3 mm cut-off criterion for large varices was applied, sensitivity, specificity, and predictive values were comparable among the 3 image datasets. CONCLUSIONS: Routine liver CT protocol is sufficient for evaluation of esophageal varices in cirrhotic patients without adding thin section reconstruction images.

Clinical usability of 3D gradient-echo-based ultrashort echo time imaging: Is it enough to facilitate diagnostic decision in real-world practice?

BACKGROUND: With recent advances in magnetic resonance imaging (MRI) technology, the practical role of lung MRI is expanding despite the inherent challenges of the thorax. The purpose of our study was to evaluate the current status of the concurrent dephasing and excitation (CODE) ultrashort echo-time sequence and the T1-weighted volumetric interpolated breath-hold examination (VIBE) sequence in the evaluation of thoracic disease by comparing it with the gold standard computed tomography (CT). METHODS: Twenty-four patients with lung cancer and mediastinal masses underwent both CT and MRI including T1-weighted VIBE and CODE. For CODE images, data were acquired in free breathing and end-expiratory images were reconstructed using retrospective respiratory gating. All images were evaluated through qualitative and quantitative approaches regarding various anatomical structures and lesions (nodule, mediastinal mass, emphysema, reticulation, honeycombing, bronchiectasis, pleural plaque and lymphadenopathy) inside the thorax in terms of diagnostic performance in making specific decisions. RESULTS: Depiction of the lung parenchyma, mediastinal and pleural lesion was not significant different among the three modalities (p > 0.05). Intra-tumoral and peritumoral features of lung nodules were not significant different in the CT, VIBE or CODE images (p > 0.05). However, VIBE and CODE had significantly lower image quality and poorer depiction of airway, great vessels, and emphysema compared to CT (p < 0.05). Image quality of central airways and depiction of bronchi were significantly better in CODE than in VIBE (p < 0.001 and p = 0.005). In contrast, the depiction of the vasculature was better for VIBE than CODE images (p = 0.003). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were significant greater in VIBE than CODE except for SNRlung and SNRnodule (p < 0.05). CONCLUSIONS: Our study showed the potential of CODE and VIBE sequences in the evaluation of localized thoracic abnormalities including solid pulmonary nodules.

Multi-center Korean cohort study based on RNA-sequencing data targeting COPD patients.

In 2023, WHO ranked chronic obstructive pulmonary disease (COPD) as the third leading cause of death, with 3.23 million fatalities in 2019. The intricate nature of the disease, which is influenced by genetics, environment, and lifestyle, is evident. The effect of air pollution and changes in atmospheric substances because of global warming highlight the need for this research. These environmental shifts are associated with the emergence of various respiratory infections such as COVID-19. RNA sequencing is pivotal in airway diseases, including COPD, as it enables comprehensive transcriptome analysis, biomarker discovery, and uncovers novel pathways. It facilitates personalized medicine by tracking dynamic changes in gene expression in response to various triggers. However, the limited research on East Asian populations may overlook the unique nuances of COPD development and progression. Bridging this gap and using peripheral blood samples for systemic analysis are crucial for comprehensive and globally applicable COPD diagnosis and treatment.

Predictions of PD-L1 Expression Based on CT Imaging Features in Lung Squamous Cell Carcinoma.

Purpose: To develop models to predict programmed death ligand 1 (PD-L1) expression in pulmonary squamous cell carcinoma (SCC) using CT. Materials and Methods: A total of 97 patients diagnosed with SCC who underwent PD-L1 expression assay were included in this study. We performed a CT analysis of the tumors using pretreatment CT images. Multiple logistic regression models were constructed to predict PD-L1 positivity in the total patient group and in the 40 advanced-stage (≥ stage IIIB) patients. The area under the receiver operating characteristic curve (AUC) was calculated for each model. Results: For the total patient group, the AUC of the 'total significant features model' (tumor stage, tumor size, pleural nodularity, and lung metastasis) was 0.652, and that of the 'selected feature model' (pleural nodularity) was 0.556. For advanced-stage patients, the AUC of the 'selected feature model' (tumor size, pleural nodularity, pulmonary oligometastases, and absence of interstitial lung disease) was 0.897. Among these factors, pleural nodularity and pulmonary oligometastases had the highest odds ratios (8.78 and 16.35, respectively). Conclusion: Our model could predict PD-L1 expression in patients with lung SCC, and pleural nodularity and pulmonary oligometastases were notable predictive CT features of PD-L1.

CT-Derived Deep Learning-Based Quantification of Body Composition Associated with Disease Severity in Chronic Obstructive Pulmonary Disease.

Purpose: Our study aimed to evaluate the association between automated quantified body composition on CT and pulmonary function or quantitative lung features in patients with chronic obstructive pulmonary disease (COPD). Materials and Methods: A total of 290 patients with COPD were enrolled in this study. The volume of muscle and subcutaneous fat, area of muscle and subcutaneous fat at T12, and bone attenuation at T12 were obtained from chest CT using a deep learning-based body segmentation algorithm. Parametric response mapping-derived emphysema (PRMemph), PRM-derived functional small airway disease (PRMfSAD), and airway wall thickness (AWT)-Pi10 were quantitatively assessed. The association between body composition and outcomes was evaluated using Pearson's correlation analysis. Results: The volume and area of muscle and subcutaneous fat were negatively associated with PRMemph and PRMfSAD (p < 0.05). Bone density at T12 was negatively associated with PRMemph (r = -0.1828, p = 0.002). The volume and area of subcutaneous fat and bone density at T12 were positively correlated with AWT-Pi10 (r = 0.1287, p = 0.030; r = 0.1668, p = 0.005; r = 0.1279, p = 0.031). However, muscle volume was negatively correlated with the AWT-Pi10 (r = -0.1966, p = 0.001). Muscle volume was significantly associated with pulmonary function (p < 0.001). Conclusion: Body composition, automatically assessed using chest CT, is associated with the phenotype and severity of COPD.