Deep Learning Classification of Usual Interstitial Pneumonia Predicts Outcomes.

Rationale: Computed tomography (CT) enables noninvasive diagnosis of usual interstitial pneumonia (UIP), but enhanced image analyses are needed to overcome the limitations of visual assessment. Objectives: Apply multiple instance learning (MIL) to develop an explainable deep learning algorithm for prediction of UIP from CT and validate its performance in independent cohorts. Methods: We trained an MIL algorithm using a pooled dataset (n = 2,143) and tested it in three independent populations: data from a prior publication (n = 127), a single-institution clinical cohort (n = 239), and a national registry of patients with pulmonary fibrosis (n = 979). We tested UIP classification performance using receiver operating characteristic analysis, with histologic UIP as ground truth. Cox proportional hazards and linear mixed-effects models were used to examine associations between MIL predictions and survival or longitudinal FVC. Measurements and Main Results: In two cohorts with biopsy data, MIL improved accuracy for histologic UIP (area under the curve, 0.77 [n = 127] and 0.79 [n = 239]) compared with visual assessment (area under the curve, 0.65 and 0.71). In cohorts with survival data, MIL-UIP classifications were significant for mortality (n = 239, mortality to April 2021: unadjusted hazard ratio, 3.1; 95% confidence interval [CI], 1.96-4.91; P < 0.001; and n = 979, mortality to July 2022: unadjusted hazard ratio, 3.64; 95% CI, 2.66-4.97; P < 0.001). Individuals classified as UIP positive by the algorithm had a significantly greater annual decline in FVC than those classified as UIP negative (-88 ml/yr vs. -45 ml/yr; n = 979; P < 0.01), adjusting for extent of lung fibrosis. Conclusions: Computerized assessment using MIL identifies clinically significant features of UIP on CT. Such a method could improve confidence in radiologic assessment of patients with interstitial lung disease, potentially enabling earlier and more precise diagnosis.

Chronic Lung Injury after COVID-19 Pneumonia: Clinical, Radiologic, and Histopathologic Perspectives.

With the COVID-19 pandemic having lasted more than 3 years, concerns are growing about prolonged symptoms and respiratory complications in COVID-19 survivors, collectively termed post-COVID-19 condition (PCC). Up to 50% of patients have residual symptoms and physiologic impairment, particularly dyspnea and reduced diffusion capacity. Studies have also shown that 24%-54% of patients hospitalized during the 1st year of the pandemic exhibit radiologic abnormalities, such as ground-glass opacity, reticular opacity, bronchial dilatation, and air trapping, when imaged more than 1 year after infection. In patients with persistent respiratory symptoms but normal results at chest CT, dual-energy contrast-enhanced CT, xenon 129 MRI, and low-field-strength MRI were reported to show abnormal ventilation and/or perfusion, suggesting that some lung injury may not be detectable with standard CT. Histologic patterns in post-COVID-19 lung disease include fibrosis, organizing pneumonia, and vascular abnormality, indicating that different pathologic mechanisms may contribute to PCC. Therefore, a comprehensive imaging approach is necessary to evaluate and diagnose patients with persistent post-COVID-19 symptoms. This review will focus on the long-term findings of clinical and radiologic abnormalities and describe histopathologic perspectives. It also addresses advanced imaging techniques and deep learning approaches that can be applied to COVID-19 survivors. This field remains an active area of research, and further follow-up studies are warranted for a better understanding of the chronic stage of the disease and developing a multidisciplinary approach for patient management.

Diagnostic performance of radiologists in distinguishing post-COVID-19 residual abnormalities from interstitial lung abnormalities.

OBJECTIVE: Distinguishing post-COVID-19 residual abnormalities from interstitial lung abnormalities (ILA) on CT can be challenging if clinical information is limited. This study aimed to evaluate the diagnostic performance of radiologists in distinguishing post-COVID-19 residual abnormalities from ILA. METHODS: This multi-reader, multi-case study included 60 age- and sex-matched subjects with chest CT scans. There were 40 cases of ILA (20 fibrotic and 20 non-fibrotic) and 20 cases of post-COVID-19 residual abnormalities. Fifteen radiologists from multiple nations with varying levels of experience independently rated suspicion scores on a 5-point scale to distinguish post-COVID-19 residual abnormalities from fibrotic ILA or non-fibrotic ILA. Interobserver agreement was assessed using the weighted κ value, and the scores of individual readers were compared with the consensus of all readers. Receiver operating characteristic curve analysis was conducted to evaluate the diagnostic performance of suspicion scores for distinguishing post-COVID-19 residual abnormalities from ILA and for differentiating post-COVID-19 residual abnormalities from both fibrotic and non-fibrotic ILA. RESULTS: Radiologists' diagnostic performance for distinguishing post-COVID-19 residual abnormalities from ILA was good (area under the receiver operating characteristic curve (AUC) range, 0.67-0.92; median AUC, 0.85) with moderate agreement (κ = 0.56). The diagnostic performance for distinguishing post-COVID-19 residual abnormalities from non-fibrotic ILA was lower than that from fibrotic ILA (median AUC = 0.89 vs. AUC = 0.80, p = 0.003). CONCLUSION: Radiologists demonstrated good diagnostic performance and moderate agreement in distinguishing post-COVID-19 residual abnormalities from ILA, but careful attention is needed to avoid misdiagnosing them as non-fibrotic ILA. KEY POINTS: Question How good are radiologists at differentiating interstitial lung abnormalities (ILA) from changes related to COVID-19 infection? Findings Radiologists had a median AUC of 0.85 in distinguishing post-COVID-19 abnormalities from ILA with moderate agreement (κ = 0.56). Clinical relevance Radiologists showed good diagnostic performance and moderate agreement in distinguishing post-COVID-19 residual abnormalities from ILA; nonetheless, caution is needed in distinguishing residual abnormalities from non-fibrotic ILA.

Central Role of CT in Management of Pulmonary Fibrosis.

With the approval of antifibrotic medications to treat patients with idiopathic pulmonary fibrosis and progressive pulmonary fibrosis, radiologists have an integral role in diagnosing these entities and guiding treatment decisions. CT features of early pulmonary fibrosis include irregular thickening of interlobular septa, pleura, and intralobular linear structures, with subsequent progression to reticular abnormality, traction bronchiectasis or bronchiolectasis, and honeycombing. CT patterns of fibrotic lung disease can often be reliably classified on the basis of the CT features and distribution of the condition. Accurate identification of usual interstitial pneumonia (UIP) or probable UIP patterns by radiologists can obviate the need for a tissue sample-based diagnosis. Other entities that can appear as a UIP pattern must be excluded in multidisciplinary discussion before a diagnosis of idiopathic pulmonary fibrosis is made. Although the imaging findings of nonspecific interstitial pneumonia and fibrotic hypersensitivity pneumonitis can overlap with those of a radiologic UIP pattern, these entities can often be distinguished by paying careful attention to the radiologic signs. Diagnostic challenges may include misdiagnosis of fibrotic lung disease due to pitfalls such as airspace enlargement with fibrosis, paraseptal emphysema, recurrent aspiration, and postinfectious fibrosis. The radiologist also plays an important role in identifying complications of pulmonary fibrosis-pulmonary hypertension, acute exacerbation, infection, and lung cancer in particular. In cases in which there is uncertainty regarding the clinical and radiologic diagnoses, surgical biopsy is recommended, and a multidisciplinary discussion among clinicians, radiologists, and pathologists can be used to address diagnosis and management strategies. This review is intended to help radiologists diagnose and manage pulmonary fibrosis more accurately, ultimately aiding in the clinical management of affected patients. ©RSNA, 2024 Supplemental material is available for this article.

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.

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.

Imaging and Clinical Features of COVID-19 Breakthrough Infections: A Multicenter Study.

Background Since vaccines against COVID-19 became available, rare breakthrough infections have been reported despite their high efficacies. Purpose To evaluate the clinical and imaging characteristics of patients with COVID-19 breakthrough infections and compare them with those of unvaccinated patients with COVID-19. Materials and Methods In this retrospective multicenter cohort study, the authors analyzed patient (aged ≥18 years) data from three centers that were registered in an open data repository for COVID-19 between June and August 2021. Hospitalized patients with baseline chest radiographs were divided into three groups according to their vaccination status. Differences between clinical and imaging features were analyzed using the Pearson χ2 test, Fisher exact test, and analysis of variance. Univariable and multivariable logistic regression analyses were used to evaluate associations between clinical factors, including vaccination status and clinical outcomes. Results Of the 761 hospitalized patients with COVID-19, the mean age was 47 years and 385 (51%) were women; 47 patients (6%) were fully vaccinated (breakthrough infection), 127 (17%) were partially vaccinated, and 587 (77%) were unvaccinated. Of the 761 patients, 412 (54%) underwent chest CT during hospitalization. Among the patients who underwent CT, the proportions without pneumonia were 22% of unvaccinated patients (71 of 326), 30% of partially vaccinated patients (19 of 64), and 59% of fully vaccinated patients (13 of 22) (P < .001). Fully vaccinated status was associated with a lower risk of requiring supplemental oxygen (odds ratio [OR], 0.24 [95% CI: 0.09, 0.64; P = .005]) and lower risk of intensive care unit admission (OR, 0.08 [95% CI: 0.09, 0.78; P = .02]) compared with unvaccinated status. Conclusion Patients with COVID-19 breakthrough infections had a significantly higher proportion of CT scans without pneumonia compared with unvaccinated patients. Vaccinated patients with breakthrough infections had a lower likelihood of requiring supplemental oxygen and intensive care unit admission. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Schiebler and Bluemke in this issue.

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.

Invasive Hypervirulent Klebsiella pneumoniae Syndrome Originating from an Anorectal Abscess as Opposed to a Pyogenic Liver Abscess.

An immunocompetent 49-year-old man presented with swelling and pain in the lower region of his left leg that had lasted for 4 weeks. The diagnosis was severe pyomyositis and osteomyelitis in the lower left leg caused by hypervirulent Klebsiella pneumoniae (hvKP) along with multiple metastatic infections in the kidneys, lungs, and brain originating from an anorectal abscess. A virulence-gene analysis revealed that the isolated K. pneumoniae harbored rmpA, entB, ybtS, kfu, iutA, mrkD, and allS-virulence genes and belonged to the K1 capsular serotype. After repeated abscess drainage procedures, intravenous ceftriaxone was administered for more than 10 weeks, and the patient's infection was controlled. We focused on the clinical features of hvKP originating from an anorectal abscess without a pyogenic liver abscess. We suggest that hvKP be considered a causative pathogen of pyomyositis and osteomyelitis resulting in multiple metastatic infections in an immunocompetent patient, and more information on the unexpected multiple metastatic infections should be obtained from a virulence analysis of K. pneumoniae.

A Rare Case of Familial Schwannomatosis Showing Intrafamilial Variability with Identification of a Shared Novel Germline SMARCB1 Mutation.

Schwannomatosis is characterized by the presence of multiple schwannomas without landmarks of NF2. It is considered the rarest form of neurofibromatosis (NF). Here, we report the first case of familial schwannomatosis with regard to the segmental/generalized phenotype, in which the proband and the daughter present a distinct phenotype in this classification. The proband presents a generalized, painless, extradural type of schwannomatosis, while the daughter shows a segmental, painful, intradural type of schwannomatosis. Whole-exome sequencing of the affected individuals revealed a shared novel SMARCB1 gene mutation (c.92A > G, p.Glu31Gly) despite the clinical variability. We thus suggest two points in the diagnosis of familial schwannomatosis: The identified novel germline SMARCB1 variant can be reflective of a phenotypical progression from a segmental to a generalized type of schwannomatosis, or an intrafamilial variability in inherited schwannomatosis, which was not reported in previous literature. The specific combination of somatic NF2 mutations may be a major factor in regulating the severity and scope of the resulting phenotype in schwannomatosis.

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.

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.

Repeat biopsy of patients with acquired resistance to EGFR TKIs: implications of biopsy-related factors on T790M mutation detection.

OBJECTIVES: To find predictors of non-diagnostic repeat biopsy specimen acquisition for mutational analysis and detection of epidermal growth factor receptor (EGFR) T790M mutation. METHODS: We retrospectively reviewed 90 non-small cell lung cancer patients harbouring EGFR mutations who underwent repeat cone-beam CT-guided transthoracic needle biopsy. Clinical characteristics as well as biopsy-related factors were compared between patients with and without diagnostic specimen acquisition and between patients with and without T790M mutation. After univariate analysis, multivariate logistic regression analysis was performed to reveal independent predictors. RESULTS: Diagnostic biopsy specimens for mutational test were obtained in 90% (81/90) of patients, of which 62% (50/81) possessed T790M mutation. None of the analysed variables were significantly associated with non-diagnostic specimen acquisition. For T790M detection, duration of EGFR tyrosine kinase inhibitor treatment (p = 0.066), duration of total chemotherapy (p = 0.026), tumour size (p = 0.066), and metastatic lung lesion as a biopsy target (p = 0.029) showed p values less than 0.10. Multivariate analysis revealed that target tumour size (odds ratio, 0.765; p = 0.031) was an independent predictor of T790M mutation. Metastatic lesions as biopsy targets (odds ratio, 4.194; p = 0.050) showed marginal statistical significance. CONCLUSIONS: Non-diagnostic repeat biopsy specimen acquisition was not related to the clinical or technical factors. However, detection of T790M at repeat biopsy might be associated with smaller target tumour size and selection of metastatic lesions as biopsy targets. KEY POINTS: • Cone-beam CT-guided repeat biopsy yielded high diagnostic specimen acquisition rate. • Biopsy-related features were associated with the detection of T790M mutation. • Target tumour size was an independent predictor of the T790M detection. • Biopsy targeting metastatic lung nodules might help detect the T790M mutation.

Effect of CT Acquisition Parameters on Iodine Density Measurement at Dual-Layer Spectral CT.

OBJECTIVE: We aimed to evaluate the effect of tube voltage, tube current-time product, and iterative reconstruction on iodine quantification using a dual-layer spectral CT scanner. MATERIALS AND METHODS: Two mediastinal iodine phantoms, each containing six tubes of different iodine concentrations (0, 1, 2.5, 5, 10, and 20 mg I/mL; the two phantoms had tubes with contrast media diluted in water and in 10% amino acid solution, respectively), were inserted into an anthropomorphic chest phantom and scanned with varying acquisition parameters (120 and 140 kVp; 20, 40, 60, 80, 100, 150, and 200 mAs; and spectral reconstruction levels 0 and 6). Thereafter, iodine density was measured (in milligrams of iodine per milliliter) using a dedicated software program, and the effect of acquisition parameters on iodine density and on its relative measurement error (RME) was analyzed using a linear mixed-effects model. RESULTS: Tube voltages (all, p < 0.001) and tube current-time products (p < 0.05, depending on the interaction terms for iodine density; p = 0.023 for RME) had statistically significant effects on iodine density and RME. However, the magnitude of their effects was minimal. That is, estimated differences between tube voltage settings ranged from 0 to 0.8 mg I/mL for iodine density and from 1.0% to 4.2% for RME. For tube current-time product, alteration of 100 mAs caused changes in iodine density and RME of approximately 0.1 mg I/mL and 0.6%, respectively. Spectral level was not an affecting factor for iodine quantification (p = 0.647 for iodine density and 0.813 for RME). CONCLUSION: Iodine quantification using dual-layer spectral CT was feasible irrespective of CT acquisition parameters because their effects on iodine density and RME were minimal.