Automatic Mechanical Ventilation vs Manual Bag Ventilation During CPR: A Pilot Randomized Controlled Trial.

BACKGROUND: There is insufficient evidence supporting the theory that mechanical ventilation can replace the manual ventilation method during CPR. RESEARCH QUESTION: Is using automatic mechanical ventilation feasible and comparable to the manual ventilation method during CPR? STUDY DESIGN AND METHODS: This pilot randomized controlled trial compared automatic mechanical ventilation (MV) and manual bag ventilation (BV) during CPR of out-of-hospital cardiac arrest (OHCA). Patients with medical OHCA arriving at the ED were randomly assigned to two groups: an MV group using a mechanical ventilator and a BV group using Ambu-bag. Primary outcome was any return-of-spontaneous circulation (ROSC). Secondary outcomes were changes of arterial blood gas analysis results during CPR. Tidal volume, minute volume, and peak airway pressure were also analyzed. RESULTS: A total of 60 patients were enrolled, and 30 patients were randomly assigned to each group. There were no statistically significant differences in basic characteristics of OHCA patients between the two groups. The rate of any ROSC was 56.7% in the MV group and 43.3% in the BV group, indicating no significant (P = .439) difference between the two groups. There were also no statistically significant differences in changes of PH, Pco2, Po2, HCO3, or lactate levels during CPR between the two groups (P values = .798, 0.249, .515, .876, and .878, respectively). Significantly lower VT (P < .001) and minute volume (P = .009) were observed in the MV group. INTERPRETATION: In this pilot trial, the use of MV instead of BV during CPR was feasible and could serve as a viable alternative. A multicenter randomized controlled trial is needed to create sufficient evidence for ventilation guideline during CPR. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov; No.: NCT05550454; URL: www. CLINICALTRIALS: gov.

Acute Exacerbation and Longitudinal Lung Function Change of Preserved Ratio Impaired Spirometry.

Background: Preserved ratio impaired spirometry (PRISm) is a heterogeneous disease entity. Limited data are available regarding its prevalence, clinical course, or prognosis. We aimed to evaluate the longitudinal clinical course of patients with PRISm compared with chronic obstructive pulmonary disease (COPD). Methods: A retrospective study enrolled PRISm and COPD patients who underwent chest computed tomography and longitudinal pulmonary function tests between January 2013 and December 2020. We compared the incidence of acute exacerbations and lung function changes between PRISm and COPD patients. Results: Of the 623 patients, 40 and 583 had PRISm and COPD, respectively. Compared to COPD patients, PRISm patients were younger, more likely to be female and have a history of tuberculosis, and less likely to be smokers. They also had less severe comorbidities, lower forced vital capacity (FVC) and diffusing capacity of the lungs for carbon monoxide (DLCO). The clinical course was not significantly different between the PRISm and COPD patients in terms of the risk of moderate-to-severe acute exacerbations or proportion of frequent exacerbators. During follow-up, PRISm patients had a significantly slower annual decline of forced expiratory volume in 1 second, FVC, and DLCO than COPD patients. Conclusion: PRISm patients had no significant difference in the risk of acute exacerbations, but a significantly slower decline of lung function during longitudinal follow-up, compared with COPD patients.

Diffusing capacity as an independent predictor of acute exacerbations in chronic obstructive pulmonary disease.

A weak correlation between diffusing capacity of the lung for carbon monoxide (DLCO) and emphysema has been reported. This study investigated whether impaired DLCO in chronic obstructive pulmonary disease (COPD) is associated with increased risk of acute exacerbation independent of the presence or extent of emphysema. This retrospective cohort study included patients with COPD between January 2004 and December 2019. The participants were divided into four groups based on visually detected emphysema and impaired DLCO. Among 597 patients with COPD, 8.5% had no emphysema and impaired DLCO whereas 36.3% had emphysema without impaired DLCO. Among the four groups, patients with impaired DLCO and emphysema showed a higher risk of moderate-to-severe or severe exacerbation than those with normal DLCO. Impaired DLCO was an independent risk factor for severe exacerbation (hazard ratio, 1.524 [95% confidence interval 1.121-2.072]), whereas the presence of emphysema was not. The risk of moderate-to-severe or severe exacerbation increases with the severity of impaired DLCO. After propensity-score matching for the extent of emphysema, impaired DLCO was significantly associated with a higher risk of moderate-to-severe (p = 0.041) or severe exacerbation (p = 0.020). In patients with COPD and heterogeneous parenchymal abnormalities, DLCO can be considered an independent biomarker of acute exacerbation.

Effectiveness of COVID-19 Vaccines Over Time Against Clinical and Radiologic Outcomes Related to Severe SARS-CoV-2 Infection.

Background The impact of waning vaccine effectiveness on the severity of COVID-19-related findings discovered with radiologic examinations remains underexplored. Purpose To evaluate the effectiveness of vaccines over time against severe clinical and radiologic outcomes related to SARS-CoV-2 infections. Materials and Methods This multicenter retrospective study included patients in the Korean Imaging Cohort of COVID-19 database who were hospitalized for COVID-19 between June 2021 and December 2022. Patients who had received at least one dose of a SARS-CoV-2 vaccine were categorized based on the time elapsed between diagnosis and their last vaccination. Adjusted multivariable logistic regression analysis was used to estimate vaccine effectiveness against a composite of severe clinical outcomes (invasive ventilation, extracorporeal membrane oxygenation, or in-hospital death) and severe radiologic pneumonia (≥25% of lung involvement), and odds ratios (ORs) were compared between patients vaccinated within 90 days of diagnosis and those vaccinated more than 90 days before diagnosis. Results Of 4196 patients with COVID-19 (mean age, 66 years ± 17 [SD]; 2132 [51%] women, 2064 [49%] men), the ratio of severe pneumonia since their most recent vaccination was as follows: 90 days or less, 18% (277 of 1527); between 91 and 120 days, 22% (172 of 783); between 121 and 180 days, 27% (274 of 1032); between 181 and 240 days, 32% (159 of 496); and more than 240 days, 31% (110 of 358). Patients vaccinated more than 240 days before diagnosis showed increased odds of severe clinical outcomes compared with patients vaccinated within 90 days (OR = 1.94 [95% CI: 1.16, 3.24]; P = .01). Similarly, patients vaccinated more than 240 days before diagnosis showed increased odds of severe pneumonia on chest radiographs compared with patients vaccinated within 90 days (OR = 1.65 [95% CI: 1.13, 2.40]; P = .009). No difference in odds of severe clinical outcomes (P = .13 to P = .68) or severe pneumonia (P = .15 to P = .86) were observed between patients vaccinated 91-240 days before diagnosis and those vaccinated within 90 days of diagnosis. Conclusion Vaccine effectiveness against severe clinical outcomes and severe pneumonia related to SARS-CoV-2 infection gradually declined, with increased odds of both observed in patients vaccinated more than 240 days before diagnosis. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Wells in this issue.

Establishment and characterization of BMC-PDC-019: a novel patient-derived cell line of EGFR-mutant pulmonary adenocarcinoma transformed into small-cell lung cancer.

Transformed small-cell lung cancer (tSCLC) from EGFR-mutant adenocarcinoma is a rare and aggressive form of lung cancer that can occur when the tumor develops resistance to EGFR targeted therapy and the cancer cells acquire additional genomic alterations that cause them to transform into SCLC. Treatment for tSCLC has not been established yet, and chemotherapy regimens for de novo SCLC are mostly recommended. However, these treatments showed disappointing outcome, and novel anti-cancer agents and immunological approaches are currently being developed. The patient-derived cell line is a critical tool for pre-clinical and translational research, but cell line models for tSCLC are not publicly available from cell banks. The aim of this study was to establish and characterize a novel cell line for tSCLC. Using a lymph-node biopsy tissue from a 58-year-old female patient, whose tumor was EGFR-mutant lung adenocarcinoma progressed on afatinib, we successfully established a cell line, named BMC-PDC-019. The tumor sample and cell line showed a typical expression of SCLC markers, such as CD56 and synaptophysin. The population doubling-time of BMC-PDC-019 cells was 48 h. We examined a range of proliferation-inhibiting effects of anti-cancer drugs currently used for de novo SCLC, using BMC-PDC-019 cells. We concluded that BMC-PDC-019 would be a useful tool for pre-clinical and translational research.

[Expert Opinion Questionnaire About Chest CT Scan Using A Negative Pressure Isolation Strecher in COVID-19 Patients: Image Quality and Infection Risk]. / COVID-19 환자에서 음압격리들것을 이용한 흉부 CT 검사에 대한 ì „ë¬¸ê°€ 의견 설문: 영상품질과 감염위험.

Purpose: To survey perceptions of certified physicians on the protocol of chest CT in patients with coronavirus (COVID-19) using a negative pressure isolation stretcher (NPIS). Materials and Methods: This study collected questionnaire responses from a total of 27 certified physicians who had previously performed chest CT with NPIS in COVID-19 isolation hospitals. Results: The nine surveyed hospitals performed an average of 116 chest CT examinations with NPIS each year. Of these, an average of 24 cases (21%) were contrast chest CT. Of the 9 pulmonologists we surveyed, 5 (56%) agreed that patients who showed abnormalities in serum D-dimer required contrast chest CT. All 9 surveyed radiologists agreed that the image quality of the chest CT with NPIS was sufficient for CT image interpretation regarding pneumonia or pulmonary embolism. Furthermore, in our 9 surveyed infectionologists, 5 (56%) agreed that a risk of secondary infection in the CT room after temporary opening of NPIS could be prevented through a process of disinfection. Conclusion: Experienced physicians considered that the effects of NIPS on chest CT image quality was minimal in patients with COVID-19, and the risk of CT room contamination was easily controlled.

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.].

Association between successful weaning from nasogastric tube feeding and thoracic muscle mass in patients with aspiration pneumonia.

Nasogastric tube feeding is often used to provide optimal nutrition and hydration in patients with aspiration pneumonia. However, evidence regarding radiologic indicators for successful nasogastric tube weaning is lacking. We investigated whether thoracic skeletal muscle assessment can be useful for predicting successful weaning from nasogastric tube feeding in patients with aspiration pneumonia. This longitudinal, observational study included subjects with aspiration pneumonia who underwent a videofluoroscopic swallowing study (VFSS) and chest computed tomography (CT) in Boramae Medical Center, from January 2012 to December 2019. We estimated the area under the receiver operating characteristics curve (AUC) to evaluate the predictive performance of skeletal muscle and visceral fat parameters and VFSS results for successful weaning from nasogastric tube feeding. A board-certified radiologist measured muscle and fat areas. Muscle and fat volumes were segmented and measured using an externally validated convolutional neural network model. Among the 146 included patients, nasogastric tube feeding was successfully transitioned to oral feeding in 46.6%. After adjusting for covariables related to successful weaning, skeletal muscle areas, indices, and volume indices were positively associated with successful nasogastric tube weaning. Although VFSS results and skeletal muscle parameters alone showed suboptimal performance for predicting successful weaning, a prediction model combining skeletal muscle index at the T4 level and VFSS results improved the prediction performance to an acceptable level (AUC ≥ 0.7). Skeletal muscle index measured at the T4 level may be a useful supplementary indicator for predicting successful weaning from nasogastric tube feeding in patients with aspiration pneumonia.

Machine learning-based prediction of in-ICU mortality in pneumonia patients.

Conventional severity-of-illness scoring systems have shown suboptimal performance for predicting in-intensive care unit (ICU) mortality in patients with severe pneumonia. This study aimed to develop and validate machine learning (ML) models for mortality prediction in patients with severe pneumonia. This retrospective study evaluated patients admitted to the ICU for severe pneumonia between January 2016 and December 2021. The predictive performance was analyzed by comparing the area under the receiver operating characteristic curve (AU-ROC) of ML models to that of conventional severity-of-illness scoring systems. Three ML models were evaluated: (1) logistic regression with L2 regularization, (2) gradient-boosted decision tree (LightGBM), and (3) multilayer perceptron (MLP). Among the 816 pneumonia patients included, 223 (27.3%) patients died. All ML models significantly outperformed the Simplified Acute Physiology Score II (AU-ROC: 0.650 [0.584-0.716] vs 0.820 [0.771-0.869] for logistic regression vs 0.827 [0.777-0.876] for LightGBM 0.838 [0.791-0.884] for MLP; P < 0.001). In the analysis for NRI, the LightGBM and MLP models showed superior reclassification compared with the logistic regression model in predicting in-ICU mortality in all length of stay in the ICU subgroups; all age subgroups; all subgroups with any APACHE II score, PaO2/FiO2 ratio < 200; all subgroups with or without history of respiratory disease; with or without history of CVA or dementia; treatment with mechanical ventilation, and use of inotropic agents. In conclusion, the ML models have excellent performance in predicting in-ICU mortality in patients with severe pneumonia. Moreover, this study highlights the potential advantages of selecting individual ML models for predicting in-ICU mortality in different subgroups.

Chest Radiography of Tuberculosis: Determination of Activity Using Deep Learning Algorithm.

BACKGROUND: Inactive or old, healed tuberculosis (TB) on chest radiograph (CR) is often found in high TB incidence countries, and to avoid unnecessary evaluation and medication, differentiation from active TB is important. This study develops a deep learning (DL) model to estimate activity in a single chest radiographic analysis. METHODS: A total of 3,824 active TB CRs from 511 individuals and 2,277 inactive TB CRs from 558 individuals were retrospectively collected. A pretrained convolutional neural network was fine-tuned to classify active and inactive TB. The model was pretrained with 8,964 pneumonia and 8,525 normal cases from the National Institute of Health (NIH) dataset. During the pretraining phase, the DL model learns the following tasks: pneumonia vs. normal, pneumonia vs. active TB, and active TB vs. normal. The performance of the DL model was validated using three external datasets. Receiver operating characteristic analyses were performed to evaluate the diagnostic performance to determine active TB by DL model and radiologists. Sensitivities and specificities for determining active TB were evaluated for both the DL model and radiologists. RESULTS: The performance of the DL model showed area under the curve (AUC) values of 0.980 in internal validation, and 0.815 and 0.887 in external validation. The AUC values for the DL model, thoracic radiologist, and general radiologist, evaluated using one of the external validation datasets, were 0.815, 0.871, and 0.811, respectively. CONCLUSION: This DL-based algorithm showed potential as an effective diagnostic tool to identify TB activity, and could be useful for the follow-up of patients with inactive TB in high TB burden countries.

Risk of thyroid cancer in a lung cancer screening population of the National Lung Screening Trial according to the presence of incidental thyroid nodules detected on low-dose chest CT.

PURPOSE: This study evaluated thyroid cancer risk in a lung cancer screening population according to the presence of an incidental thyroid nodule (ITN) detected on low-dose chest computed tomography (LDCT). METHODS: Of 47,837 subjects who underwent LDCT, a lung cancer screening population according to the National Lung Screening Trial results was retrospectively enrolled. The prevalence of ITN on LDCT was calculated, and the ultrasonography (US)/fine-needle aspiration (FNA)-based risk of thyroid cancer according to the presence of ITN on LDCT was compared using the Fisher exact or Student t-test as appropriate. RESULTS: Of the 2,329 subjects (female:male=44:2,285; mean age, 60.9±4.9 years), the prevalence of ITN on LDCT was 4.8% (111/2,329). The incidence of thyroid cancer was 0.8% (18/2,329, papillary thyroid microcarcinomas [PTMCs]) and was higher in the ITN-positive group than in the ITN-negative group (3.6% [4/111] vs. 0.6% [14/2,218], P=0.009). Among the 2,011 subjects who underwent both LDCT and thyroid US, all risks were higher (P<0.001) in the ITNpositive group than in the ITN-negative group: presence of thyroid nodule on US, 94.1% (95/101) vs. 48.6% (928/1,910); recommendation of FNA according to the American Thyroid Association guideline and Korean Thyroid Imaging Reporting and Data System guideline, 41.2% (42/101) vs. 2.4% (46/1,910) and 39.6% (40/101) vs. 1.9% (37/1,910), respectively. CONCLUSION: Despite a higher risk of thyroid cancer in the LDCT ITN-positive group than in the ITN-negative group in a lung cancer screening population, all cancers were PTMCs. A heavy smoking history may not necessitate thorough screening US for thyroid incidentalomas.

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.

Artificial Intelligence Solution for Chest Radiographs in Respiratory Outpatient Clinics: Multicenter Prospective Randomized Clinical Trial.

Rationale: Artificial intelligence (AI)-assisted diagnosis imparts high accuracy to chest radiography (CXR) interpretation; however, its benefit for nonradiologist physicians in detecting lung lesions on CXR remains unclear. Objectives: To investigate whether AI assistance improves the diagnostic performance of physicians for CXR interpretation and affects their clinical decisions in clinical practice. Methods: We randomly allocated eligible patients who visited an outpatient clinic to the intervention (with AI-assisted interpretation) and control (without AI-assisted interpretation) groups. Lung lesions on CXR were recorded by seven nonradiologists with or without AI assistance. The reference standard for lung lesions was established by three radiologists. The primary and secondary endpoints were the physicians' diagnostic accuracy and clinical decision, respectively. Results: Between October 2020 and May 2021, 162 and 161 patients were assigned to the intervention and control groups, respectively. The area under the receiver operating characteristic curve was significantly larger in the intervention group than in the control group for the CXR level (0.840 [95% confidence interval (CI), 0.778-0.903] vs. 0.718 [95% CI, 0.640-0.796]; P = 0.017) and lung lesion level (0.800 [95% CI, 0.740-0.861] vs. 0.677 [95% CI, 0.605-0.750]; P = 0.011). The intervention group had higher sensitivity in terms of both CXR and lung lesion level and a lower false referral rate for the lung lesion level. AI-assisted CXR interpretation did not affect the physicians' clinical decisions. Conclusions: AI-assisted CXR interpretation improves the diagnostic performance of nonradiologist physicians in detecting abnormal lung findings. Clinical trial registered with Clinical Research Information Service of the Republic of Korea (KCT 0005466).

Deep Learning for Estimating Lung Capacity on Chest Radiographs Predicts Survival in Idiopathic Pulmonary Fibrosis.

Background Total lung capacity (TLC) has been estimated with use of chest radiographs based on time-consuming methods, such as planimetric techniques and manual measurements. Purpose To develop a deep learning-based, multidimensional model capable of estimating TLC from chest radiographs and demographic variables and validate its technical performance and clinical utility with use of multicenter retrospective data sets. Materials and Methods A deep learning model was pretrained with use of 50 000 consecutive chest CT scans performed between January 2015 and June 2017. The model was fine-tuned on 3523 pairs of posteroanterior chest radiographs and plethysmographic TLC measurements from consecutive patients who underwent pulmonary function testing on the same day. The model was tested with multicenter retrospective data sets from two tertiary care centers and one community hospital, including (a) an external test set 1 (n = 207) and external test set 2 (n = 216) for technical performance and (b) patients with idiopathic pulmonary fibrosis (n = 217) for clinical utility. Technical performance was evaluated with use of various agreement measures, and clinical utility was assessed in terms of the prognostic value for overall survival with use of multivariable Cox regression. Results The mean absolute difference and within-subject SD between observed and estimated TLC were 0.69 L and 0.73 L, respectively, in the external test set 1 (161 men; median age, 70 years [IQR: 61-76 years]) and 0.52 L and 0.53 L in the external test set 2 (113 men; median age, 63 years [IQR: 51-70 years]). In patients with idiopathic pulmonary fibrosis (145 men; median age, 67 years [IQR: 61-73 years]), greater estimated TLC percentage was associated with lower mortality risk (adjusted hazard ratio, 0.97 per percent; 95% CI: 0.95, 0.98; P < .001). Conclusion A fully automatic, deep learning-based model estimated total lung capacity from chest radiographs, and the model predicted survival in idiopathic pulmonary fibrosis. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Sorkness in this issue.

Different treatment response to systemic corticosteroids according to white blood cell counts in severe COVID-19 patients.

BACKGROUND: Limited data are available in COVID-19 patients on the prediction of treatment response to systemic corticosteroid therapy based on systemic inflammatory markers. There is a concern whether the response to systemic corticosteroid is different according to white blood cell (WBC) counts in COVID-19 patients. We aimed to assess whether WBC count is related with the clinical outcomes after treatment with systemic corticosteroids in severe COVID-19. METHODS: We conducted a retrospective cohort study and analysed the patients hospitalised for severe COVID-19 and received systemic corticosteroids between July 2020 and June 2021. The primary endpoint was to compare the composite poor outcome of mechanical ventilation, extracorporeal membrane oxygenation, and mortality among the patients with different WBC counts. RESULTS: Of the 585 COVID-19 patients who required oxygen supplementation and systemic corticosteroids, 145 (24.8%) belonged to the leukopoenia group, 375 (64.1%) belonged to the normal WBC group, and 65 (11.1%) belonged to the leukocytosis group. In Kaplan-Meier curve, the composite poor outcome was significantly reduced in leukopoenia group compared to leukocytosis group (log-rank p-value < 0.001). In the multivariable Cox regression analysis, leukopoenia group was significantly associated with a lower risk of the composite poor outcome compared to normal WBC group (adjusted hazard ratio [aHR] = 0.32, 95% CI 0.14-0.76, p-value = 0.009) and leukocytosis group (aHR = 0.30, 95% CI = 0.12-0.78, p-value = 0.013). There was no significant difference in aHR for composite poor outcome between leukocytosis and normal WBC group. CONCLUSION: Leukopoenia may be related with a better response to systemic corticosteroid therapy in COVID-19 patients requiring oxygen supplementation.KEY MESSAGESIn severe COVID-19 treated with systemic corticosteroids, patients with leukopoenia showed a lower hazard for composite poor outcome compared to patients with normal white blood cell counts or leukocytosis.Leukopoenia may be a potential biomarker for better response to systemic corticosteroid therapy in COVID-19 pneumonia.

[Korean Clinical Imaging Guidelines for Justification of Diagnostic Imaging Study for COVID-19]. / 한국형 COVID-19 흉부영상 진단 시행 가이드라인.

To develop Korean coronavirus disease (COVID-19) chest imaging justification guidelines, eight key questions were selected and the following recommendations were made with the evidence-based clinical imaging guideline adaptation methodology. It is appropriate not to use chest imaging tests (chest radiograph or CT) for the diagnosis of COVID-19 in asymptomatic patients. If reverse transcription-polymerase chain reaction testing is not available or if results are delayed or are initially negative in the presence of symptoms suggestive of COVID-19, chest imaging tests may be considered. In addition to clinical evaluations and laboratory tests, chest imaging may be contemplated to determine hospital admission for asymptomatic or mildly symptomatic unhospitalized patients with confirmed COVID-19. In hospitalized patients with confirmed COVID-19, chest imaging may be advised to determine or modify treatment alternatives. CT angiography may be considered if hemoptysis or pulmonary embolism is clinically suspected in a patient with confirmed COVID-19. For COVID-19 patients with improved symptoms, chest imaging is not recommended to make decisions regarding hospital discharge. For patients with functional impairment after recovery from COVID-19, chest imaging may be considered to distinguish a potentially treatable disease.

Radiographic Phenotypes Affect the Risk of Inhaled Corticosteroid-Associated Pneumonia in Patients with COPD.

Purpose: Few studies have reported the association between the radiographic characteristics and the development of pneumonia in patients with chronic obstructive pulmonary disease (COPD) treated with inhaled corticosteroids (ICSs). Our study aimed to assess the effect of radiographic phenotypes on the risk of pneumonia in patients treated with ICSs. Patients and Methods: This study retrospectively analysed all patients with COPD treated with ICSs in a subset of the Korea Chronic Obstructive Pulmonary Disorders Subgroup Study registry between January 2017 and December 2019. The association between radiographic phenotypes including the presence and severity of emphysema, airway wall thickening, or bronchiectasis on chest computed tomography were determined visually/qualitatively and the risk of pneumonia was analyzed using the Cox regression model. Results: Among the 90 patients with COPD treated with ICSs, 41 experienced pneumonia more than once during the median follow-up of 29 (interquartile range, 8-35) months. In univariate Cox regression analysis, older age, longer use of ICSs, use of fluticasone propionate or metered dose inhaler, and severe exacerbation events increased the risk of pneumonia. In multivariate analysis, the presence of emphysema (adjusted hazard ratio [aHR]=3.73, P=0.033), severity measured using the visual sum score (mild-to-moderate, aHR=8.58, P=0.016; severe, aHR=3.58, P=0.042), Goddard sum score (mild-to-moderate, aHR=3.31, P=0.058; severe, aHR=5.38, P=0.014), and the upper lobe distribution of emphysema (aHR=3.76, P=0.032) were associated with a higher risk of pneumonia. Subtypes of centrilobular and panlobular emphysema had a higher risk of pneumonia compared with paraseptal emphysema (aHR=3.98, P=0.033; HR=3.91, P=0.041 vs HR=2.74, P=0.304). The presence of bronchiectasis (aHR=2.41, P=0.02) and emphysema/bronchiectasis overlap phenotype (aHR=2.19, P=0.053) on chest CT was a risk factor for pneumonia in this population. However, severity of bronchiectasis and the presence or severity of bronchial wall thickening according to the visual sum score were not associated with the risk of pneumonia. Conclusion: Among patients with COPD treated with ICSs, radiographic phenotypes including the presence of emphysema, bronchiectasis or emphysema/bronchiectasis overlap phenotype, severity with emphysema, subtypes of centrilobular or panlobular emphysema, and upper lobe distribution of emphysema may help predict the risk of pneumonia.

Phenotypic clusters on computed tomography reflects asthma heterogeneity and severity.

Background: Asthma is a heterogeneous inflammatory airway disorder with various phenotypes. Quantitative computed tomography (QCT) methods can differentiate among lung diseases through accurate assessment of the location, extent, and severity of the disease. The purpose of this study was to identify asthma clusters using QCT metrics of airway and parenchymal structure, and to identify associations with visual analyses conducted by radiologists. Methods: This prospective study used input from QCT-based metrics including hydraulic diameter (D h), luminal wall thickness (WT), functional small airway disease (fSAD), and emphysematous lung (Emph) to perform a cluster analysis and made comparisons with the visual grouping analysis conducted by radiologists based on site of airway involvement and remodeling evaluated. Results: A total of 61 asthmatics of varying severities were grouped into 4 clusters. From C1 to C4, more severe lung function deterioration, higher fixed obstruction rate, and more frequent asthma exacerbations were observed in the 5-year follow-up period. C1 presented non-severe asthma with increased WT, D h of proximal airways, and fSAD. C2 was mixed with non-severe and severe asthmatics, and showed bronchodilator responses limited to the proximal airways. C3 and C4 included severe asthmatics that showed a reduced D h of the proximal airway and diminished bronchodilator responses. While C3 was characterized by severe allergic asthma without fSAD, C4 included ex-smokers with high fSAD% and Emph%. These clusters correlated well with the grouping done by radiologists and clinical outcomes. Conclusions: Four QCT imaging-based clusters with distinct structural and functional changes in proximal and small airways can stratify heterogeneous asthmatics and can be a complementary tool to predict clinical outcomes.

Artificial Intelligence-Based Identification of Normal Chest Radiographs: A Simulation Study in a Multicenter Health Screening Cohort.

OBJECTIVE: This study aimed to investigate the feasibility of using artificial intelligence (AI) to identify normal chest radiography (CXR) from the worklist of radiologists in a health-screening environment. MATERIALS AND METHODS: This retrospective simulation study was conducted using the CXRs of 5887 adults (mean age ± standard deviation, 55.4 ± 11.8 years; male, 4329) from three health screening centers in South Korea using a commercial AI (Lunit INSIGHT CXR3, version 3.5.8.8). Three board-certified thoracic radiologists reviewed CXR images for referable thoracic abnormalities and grouped the images into those with visible referable abnormalities (identified as abnormal by at least one reader) and those with clearly visible referable abnormalities (identified as abnormal by at least two readers). With AI-based simulated exclusion of normal CXR images, the percentages of normal images sorted and abnormal images erroneously removed were analyzed. Additionally, in a random subsample of 480 patients, the ability to identify visible referable abnormalities was compared among AI-unassisted reading (i.e., all images read by human readers without AI), AI-assisted reading (i.e., all images read by human readers with AI assistance as concurrent readers), and reading with AI triage (i.e., human reading of only those rendered abnormal by AI). RESULTS: Of 5887 CXR images, 405 (6.9%) and 227 (3.9%) contained visible and clearly visible abnormalities, respectively. With AI-based triage, 42.9% (2354/5482) of normal CXR images were removed at the cost of erroneous removal of 3.5% (14/405) and 1.8% (4/227) of CXR images with visible and clearly visible abnormalities, respectively. In the diagnostic performance study, AI triage removed 41.6% (188/452) of normal images from the worklist without missing visible abnormalities and increased the specificity for some readers without decreasing sensitivity. CONCLUSION: This study suggests the feasibility of sorting and removing normal CXRs using AI with a tailored cut-off to increase efficiency and reduce the workload of radiologists.

A Challenge for Emphysema Quantification Using a Deep Learning Algorithm With Low-dose Chest Computed Tomography.

PURPOSE: We aimed to identify clinically relevant deep learning algorithms for emphysema quantification using low-dose chest computed tomography (LDCT) through an invitation-based competition. MATERIALS AND METHODS: The Korean Society of Imaging Informatics in Medicine (KSIIM) organized a challenge for emphysema quantification between November 24, 2020 and January 26, 2021. Seven invited research teams participated in this challenge. In total, 558 pairs of computed tomography (CT) scans (468 pairs for the training set, and 90 pairs for the test set) from 9 hospitals were collected retrospectively or prospectively. CT acquisition followed the hospitals' protocols to reflect the real-world clinical setting. Using the training set, each team developed an algorithm that generated converted LDCT by changing the pixel values of LDCT to simulate those of standard-dose CT (SDCT). The agreement between SDCT and LDCT was evaluated using the intraclass correlation coefficient (ICC; 2-way random effects, absolute agreement, and single rater) for the percentage of low-attenuated area below -950 HU (LAA-950 HU), κ value for emphysema categorization (LAA-950 HU, <5%, 5% to 10%, and ≥10%) and cosine similarity of LAA-950 HU. RESULTS: The mean LAA-950 HU of the test set was 14.2%±10.5% for SDCT, 25.4%±10.2% for unconverted LDCT, and 12.9%±10.4%, 11.7%±10.8%, and 12.4%±10.5% for converted LDCT (top 3 teams). The agreement between the SDCT and converted LDCT of the first-place team was 0.94 (95% confidence interval: 0.90, 0.97) for ICC, 0.71 (95% confidence interval: 0.58, 0.84) for categorical agreement, and 0.97 (interquartile range: 0.94 to 0.99) for cosine similarity. CONCLUSIONS: Emphysema quantification with LDCT was feasible through deep learning-based CT conversion strategies.