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Objective: To validate a simplified ordinal scoring method, referred to as modified length-based grading, for assessing coronary artery calcium (CAC) severity on non-electrocardiogram (ECG)-gated chest computed tomography (CT)., Materials and Methods: This retrospective study enrolled 120 patients (mean age ± standard deviation [SD], 63.1 ± 14.5 years; male, 64) who underwent both non-ECG-gated chest CT and ECG-gated cardiac CT between January 2011 and December 2021. Six radiologists independently assessed CAC severity on chest CT using two scoring methods (visual assessment and modified length-based grading) and categorized the results as none, mild, moderate, or severe. The CAC category on cardiac CT assessed using the Agatston score was used as the reference standard. Agreement among the six observers for CAC category classification was assessed using Fleiss kappa statistics. Agreement between CAC categories on chest CT obtained using either method and the Agatston score categories on cardiac CT was assessed using Cohen's kappa. The time taken to evaluate CAC grading was compared between the observers and two grading methods., Results: For differentiation of the four CAC categories, interobserver agreement was moderate for visual assessment (Fleiss kappa, 0.553 [95% confidence interval {CI}: 0.496-0.610]) and good for modified length-based grading (Fleiss kappa, 0.695 [95% CI: 0.636-0.754]). The modified length-based grading demonstrated better agreement with the reference standard categorization with cardiac CT than visual assessment (Cohen's kappa, 0.565 [95% CI: 0.511-0.619 for visual assessment vs. 0.695 [95% CI: 0.638-0.752] for modified length-based grading). The overall time for evaluating CAC grading was slightly shorter in visual assessment (mean ± SD, 41.8 ± 38.9 s) than in modified length-based grading (43.5 ± 33.2 s) ( P < 0.001)., Conclusion: The modified length-based grading worked well for evaluating CAC on non-ECG-gated chest CT with better interobserver agreement and agreement with cardiac CT than visual assessment., Competing Interests: Kyunghwa Han, a contributing editor of the Korean Journal of Radiology, was not involved in the editorial evaluation or decision to publish this article. All remaining authors have declared no conflicts of interest., (Copyright © 2023 The Korean Society of Radiology.)

Objective: T1 mapping provides valuable information regarding cardiomyopathies. Manual drawing is time consuming and prone to subjective errors. Therefore, this study aimed to test a DL algorithm for the automated measurement of native T1 and extracellular volume (ECV) fractions in cardiac magnetic resonance (CMR) imaging with a temporally separated dataset., Materials and Methods: CMR images obtained for 95 participants (mean age ± standard deviation, 54.5 ± 15.2 years), including 36 left ventricular hypertrophy (12 hypertrophic cardiomyopathy, 12 Fabry disease, and 12 amyloidosis), 32 dilated cardiomyopathy, and 27 healthy volunteers, were included. A commercial deep learning (DL) algorithm based on 2D U-net (Myomics-T1 software, version 1.0.0) was used for the automated analysis of T1 maps. Four radiologists, as study readers, performed manual analysis. The reference standard was the consensus result of the manual analysis by two additional expert readers. The segmentation performance of the DL algorithm and the correlation and agreement between the automated measurement and the reference standard were assessed. Interobserver agreement among the four radiologists was analyzed., Results: DL successfully segmented the myocardium in 99.3% of slices in the native T1 map and 89.8% of slices in the post-T1 map with Dice similarity coefficients of 0.86 ± 0.05 and 0.74 ± 0.17, respectively. Native T1 and ECV showed strong correlation and agreement between DL and the reference: for T1, r = 0.967 (95% confidence interval [CI], 0.951-0.978) and bias of 9.5 msec (95% limits of agreement [LOA], -23.6-42.6 msec); for ECV, r = 0.987 (95% CI, 0.980-0.991) and bias of 0.7% (95% LOA, -2.8%-4.2%) on per-subject basis. Agreements between DL and each of the four radiologists were excellent (intraclass correlation coefficient [ICC] of 0.98-0.99 for both native T1 and ECV), comparable to the pairwise agreement between the radiologists (ICC of 0.97-1.00 and 0.99-1.00 for native T1 and ECV, respectively)., Conclusion: The DL algorithm allowed automated T1 and ECV measurements comparable to those of radiologists., Competing Interests: Kyunghwa Han who is on the editorial board of the Korean Journal of Radiology was not involved in the editorial evaluation or decision to publish this article. Pan Ki Kim and Byoung Wook Choi are founders of Phantomics, Inc. (Seoul, Korea) and Young Joong Yang is an employee of the same company. The company supported the software for this study. Other authors have no conflicts of interest to declare., (Copyright © 2022 The Korean Society of Radiology.)

Objective: The purpose of this meta-analysis was to investigate the pooled agreements of the coronary artery calcium (CAC) severities assessed by electrocardiogram (ECG)-gated and non-ECG-gated CT and evaluate the impact of the scan parameters., Materials and Methods: PubMed, EMBASE, and the Cochrane library were systematically searched. A modified Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to evaluate the quality of the studies. Meta-analytic methods were utilized to determine the pooled weighted bias, limits of agreement (LOA), and the correlation coefficient of the CAC scores or the weighted kappa for the categorization of the CAC severities detected by the two modalities. The heterogeneity among the studies was also assessed. Subgroup analyses were performed based on factors that could affect the measurement of the CAC score and severity: slice thickness, reconstruction kernel, and radiation dose for non-ECG-gated CT., Results: A total of 4000 patients from 16 studies were included. The pooled bias was 62.60, 95% LOA were -36.19 to 161.40, and the pooled correlation coefficient was 0.94 (95% confidence interval [CI] = 0.89-0.97) for the CAC score. The pooled weighted kappa of the CAC severity was 0.85 (95% CI = 0.79-0.91). Heterogeneity was observed in the studies (I² > 50%, p < 0.1). In the subgroup analysis, the agreement between the CAC categorizations was better when the two CT examinations had reconstructions based on the same slice thickness and kernel., Conclusion: The pooled agreement of the CAC severities assessed by the ECG-gated and non-ECG-gated CT was excellent; however, it was significantly affected by scan parameters, such as slice thickness and the reconstruction kernel., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2021 The Korean Society of Radiology.)

Objective: We aimed to develop a prediction model for diagnosing severe aortic stenosis (AS) using computed tomography (CT) radiomics features of aortic valve calcium (AVC) and machine learning (ML) algorithms., Materials and Methods: We retrospectively enrolled 408 patients who underwent cardiac CT between March 2010 and August 2017 and had echocardiographic examinations (240 patients with severe AS on echocardiography [the severe AS group] and 168 patients without severe AS [the non-severe AS group]). Data were divided into a training set (312 patients) and a validation set (96 patients). Using non-contrast-enhanced cardiac CT scans, AVC was segmented, and 128 radiomics features for AVC were extracted. After feature selection was performed with three ML algorithms (least absolute shrinkage and selection operator [LASSO], random forests [RFs], and eXtreme Gradient Boosting [XGBoost]), model classifiers for diagnosing severe AS on echocardiography were developed in combination with three different model classifier methods (logistic regression, RF, and XGBoost). The performance (c-index) of each radiomics prediction model was compared with predictions based on AVC volume and score., Results: The radiomics scores derived from LASSO were significantly different between the severe AS and non-severe AS groups in the validation set (median, 1.563 vs. 0.197, respectively, p < 0.001). A radiomics prediction model based on feature selection by LASSO + model classifier by XGBoost showed the highest c-index of 0.921 (95% confidence interval [CI], 0.869-0.973) in the validation set. Compared to prediction models based on AVC volume and score (c-indexes of 0.894 [95% CI, 0.815-0.948] and 0.899 [95% CI, 0.820-0.951], respectively), eight and three of the nine radiomics prediction models showed higher discrimination abilities for severe AS. However, the differences were not statistically significant ( p > 0.05 for all)., Conclusion: Models based on the radiomics features of AVC and ML algorithms may perform well for diagnosing severe AS, but the added value compared to AVC volume and score should be investigated further., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2021 The Korean Society of Radiology.)

Percutaneous transthoracic needle biopsy (PTNB) is one of the essential diagnostic procedures for pulmonary lesions. Its role is increasing in the era of CT screening for lung cancer and precision medicine. The Korean Society of Thoracic Radiology developed the first evidence-based clinical guideline for PTNB in Korea by adapting pre-existing guidelines. The guideline provides 39 recommendations for the following four main domains of 12 key questions: the indications for PTNB, pre-procedural evaluation, procedural technique of PTNB and its accuracy, and management of post-biopsy complications. We hope that these recommendations can improve the diagnostic accuracy and safety of PTNB in clinical practice and promote standardization of the procedure nationwide., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2021 The Korean Society of Radiology.)

Objective: We investigated the prevalence of pneumonia in novel coronavirus disease 2019 (COVID-19) patients using chest radiographs to identify the characteristics of those with initially negative chest radiographs, who were positive for pneumonia on follow-up., Materials and Methods: Retrospective cohort data of 236 COVID-19 patients were reviewed. Chest radiography was performed on admission, with serial radiographs obtained until discharge. The 'positive conversion group' was defined as patients whose initial chest radiographs were negative but were positive for pneumonia during follow-up. Patients with initially positive chest radiographs were defined as the 'initial pneumonia group.' Patients with negative initial and follow-up chest radiographs were defined as the 'non-pneumonia group.' Clinical and laboratory findings were compared between groups, and predictors of positive conversion were investigated., Results: Among 236 patients, 108 (45.8%) were in the non-pneumonia group, 69 (29.2%) were in the initial pneumonia group, and 59 (25%) were in the positive conversion group. The patients in the 'initial pneumonia group' and 'positive conversion group' were older, had higher C-reactive protein (CRP) and lactate dehydrogenase levels, and lower absolute lymphocyte counts than those in the 'non-pneumonia group' (all p < 0.001). Among patients with negative initial chest radiographs, age ≥ 45 years (odds ratio [OR]: 3.93, 95% confidence interval [CI]: 1.76-8.75, p = 0.001), absolute lymphocyte count < 1500 cells/μL (OR: 2.25, 95% CI: 1.03-4.89, p = 0.041), and CRP > 0.5 mg/dL (OR: 3.91, 95% CI: 1.54-9.91, p = 0.004) were independent predictors for future development of pneumonia., Conclusion: More than a half of COVID-19 patients initially had normal chest radiographs; however, elderly patients (≥ 45 years of age) with abnormal laboratory findings (elevated CRP and low absolute lymphocyte counts) developed pneumonia on follow-up radiographs., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2020 The Korean Society of Radiology.)

Objective: The present study aimed to investigate whether quantitative dual-energy computed tomography (DECT) parameters offer an incremental risk stratification benefit over the CT ventricular diameter ratio in patients with acute pulmonary embolism (PE) by using propensity score analysis., Materials and Methods: This study was conducted on 480 patients with acute PE who underwent CT pulmonary angiography (CTPA) or DECT pulmonary angiography (DE CT-PA). This propensity-matched study population included 240 patients with acute PE each in the CTPA and DECT groups. Altogether, 260 (54.1%) patients were men, and the mean age was 64.9 years (64.9 ± 13.5 years). The primary endpoint was all-cause death within 30 days. The Cox proportional hazards regression model was used to identify associations between CT parameters and outcomes and to identify potential predictors. Concordance (C) statistics were used to compare the prognoses between the two groups., Results: In both CTPA and DECT groups, right to left ventricle diameter ratio ≥ 1 was associated with an increased risk of all-cause death within 30 days (hazard ratio: 3.707, p < 0.001 and 5.573, p < 0.001, respectively). However, C-statistics showed no statistically significant difference between the CTPA and DECT groups for predicting death within 30 days (C-statistics: 0.759 vs. 0.819, p = 0.117)., Conclusion: Quantitative measurement of lung perfusion defect volume by DECT had no added benefit over CT ventricular diameter ratio for predicting all-cause death within 30 days., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2020 The Korean Society of Radiology.)

Objective: We performed a meta-analysis to evaluate the agreement of cardiac computed tomography (CT) with cardiac magnetic resonance imaging (CMRI) in the assessment of right ventricle (RV) volume and functional parameters., Materials and Methods: PubMed, EMBASE, and Cochrane library were systematically searched for studies that compared CT with CMRI as the reference standard for measurement of the following RV parameters: end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), or ejection fraction (EF). Meta-analytic methods were utilized to determine the pooled weighted bias, limits of agreement (LOA), and correlation coefficient ( r ) between CT and CMRI. Heterogeneity was also assessed. Subgroup analyses were performed based on the probable factors affecting measurement of RV volume: CT contrast protocol, number of CT slices, CT reconstruction interval, CT volumetry, and segmentation methods., Results: A total of 766 patients from 20 studies were included. Pooled bias and LOA were 3.1 mL (-5.7 to 11.8 mL), 3.6 mL (-4.0 to 11.2 mL), -0.4 mL (5.7 to 5.0 mL), and -1.8% (-5.7 to 2.2%) for EDV, ESV, SV, and EF, respectively. Pooled correlation coefficients were very strong for the RV parameters ( r = 0.87-0.93). Heterogeneity was observed in the studies (I² > 50%, p < 0.1). In the subgroup analysis, an RV-dedicated contrast protocol, ≥ 64 CT slices, CT volumetry with the Simpson's method, and inclusion of the papillary muscle and trabeculation had a lower pooled bias and narrower LOA., Conclusion: Cardiac CT accurately measures RV volume and function, with an acceptable range of bias and LOA and strong correlation with CMRI findings. The RV-dedicated CT contrast protocol, ≥ 64 CT slices, and use of the same CT volumetry method as CMRI can improve agreement with CMRI., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2020 The Korean Society of Radiology.)

Objective: To measure the diagnostic accuracy of percutaneous transthoracic needle lung biopsies (PTNBs) on the basis of the intention-to-diagnose principle and identify risk factors for diagnostic failure of PTNBs in a multi-institutional setting., Materials and Methods: A total of 9384 initial PTNBs performed in 9239 patients (mean patient age, 65 years [range, 20-99 years]) from January 2010 to December 2014 were included. The accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of PTNBs for diagnosis of malignancy were measured. The proportion of diagnostic failures was measured, and their risk factors were identified., Results: The overall accuracy, sensitivity, specificity, PPV, and NPV were 91.1% (95% confidence interval [CI], 90.6-91.7%), 92.5% (95% CI, 91.9-93.1%), 86.5% (95% CI, 85.0-87.9%), 99.2% (95% CI, 99.0-99.4%), and 84.3% (95% CI, 82.7-85.8%), respectively. The proportion of diagnostic failures was 8.9% (831 of 9384; 95% CI, 8.3-9.4%). The independent risk factors for diagnostic failures were lesions ≤ 1 cm in size (adjusted odds ratio [AOR], 1.86; 95% CI, 1.23-2.81), lesion size 1.1-2 cm (1.75; 1.45-2.11), subsolid lesions (1.81; 1.32-2.49), use of fine needle aspiration only (2.43; 1.80-3.28), final diagnosis of benign lesions (2.18; 1.84-2.58), and final diagnosis of lymphomas (10.66; 6.21-18.30). Use of cone-beam CT (AOR, 0.31; 95% CI, 0.13-0.75) and conventional CT-guidance (0.55; 0.32-0.94) reduced diagnostic failures., Conclusion: The accuracy of PTNB for diagnosis of malignancy was fairly high in our large-scale multi-institutional cohort. The identified risk factors for diagnostic failure may help reduce diagnostic failure and interpret the biopsy results., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2019 The Korean Society of Radiology.)

Objective: We aimed to investigate the diagnostic accuracy of cardiac computed tomography (CT) for the detection of mitral valve (MV) prolapse in mitral regurgitation (MR) with surgical findings as the standard reference, and to assess the predictability of MV replacement based on morphologic CT findings., Materials and Methods: A total of 156 patients who had undergone preoperative cardiac CT and subsequently received MV surgery due to severe MR were retrospectively enrolled. Non-repairable MV was defined when at least one of the following conditions was met: 1) anterior leaflet prolapse, 2) bi-leaflet prolapse, or 3) valve morphology (leaflet thickening, calcification, or mitral annular calcification [MAC]). Diagnostic performance of CT for the detection of the prolapsed segment was assessed with surgical findings as the standard reference. Logistic regression analysis was performed to evaluate the value of CT findings to predict actual valve replacement., Results: During surgery, MV prolapse was identified in 72.1%. The sensitivity, specificity, and diagnostic accuracy for the detection of MV prolapse were 99.1%, 81.4%, and 94.2%, respectively, per patient. One-hundred eighteen patients (75.6%) underwent MV repair and the remaining 38 patients received MV replacement. Bi-leaflet prolapse and valve morphology were independent predictors of valve replacement after adjusting for clinical variables (adjusted odds ratio, [OR] 8.63 for bi-leaflet prolapse; OR, 4.14 for leaflet thickening; and OR, 5.37 for leaflet calcium score > 5.6; p < 0.05)., Conclusion: Cardiac CT can have high diagnostic performance for detecting the prolapsed segment of the MV and predictability of valve replacement before surgery. Bi-leaflet prolapse and valve morphology, such as leaflet thickening, or calcification or MAC, are the most important predictors of valve replacement., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2019 The Korean Society of Radiology.)

Objective: To analyze the complications of percutaneous transthoracic needle biopsy using CT-based imaging modalities for needle guidance in comparison with fluoroscopy in a large retrospective cohort., Materials and Methods: This study was approved by multiple Institutional Review Boards and the requirement for informed consent was waived. We retrospectively included 10568 biopsies from eight referral hospitals from 2010 through 2014. In univariate and multivariate logistic analyses, 3 CT-based guidance modalities (CT, CT fluoroscopy, and cone-beam CT) were compared with fluoroscopy in terms of the risk of pneumothorax, pneumothorax requiring chest tube insertion, and hemoptysis, with adjustment for other risk factors., Results: Pneumothorax occurred in 2298 of the 10568 biopsies (21.7%). Tube insertion was required after 316 biopsies (3.0%), and hemoptysis occurred in 550 cases (5.2%). In the multivariate analysis, pneumothorax was more frequently detected with CT {odds ratio (OR), 2.752 (95% confidence interval [CI], 2.325-3.258), p < 0.001}, CT fluoroscopy (OR, 1.440 [95% CI, 1.176-1.762], p < 0.001), and cone-beam CT (OR, 2.906 [95% CI, 2.235-3.779], p < 0.001), but no significant relationship was found for pneumothorax requiring chest tube insertion ( p = 0.497, p = 0.222, and p = 0.216, respectively). The incidence of hemoptysis was significantly lower under CT (OR, 0.348 [95% CI, 0.247-0.491], p < 0.001), CT fluoroscopy (OR, 0.594 [95% CI, 0.419-0.843], p = 0.004), and cone-beam CT (OR, 0.479 [95% CI, 0.317-0.724], p < 0.001) guidance., Conclusion: Hemoptysis occurred less frequently with CT-based guidance modalities in comparison with fluoroscopy. Although pneumothorax requiring chest tube insertion showed a similar incidence, pneumothorax was more frequently detected using CT-based guidance modalities., Competing Interests: The authors have no financial conflicts of interest., (Copyright © 2019 The Korean Society of Radiology.)

Cardiac magnetic resonance (CMR) imaging is widely used in various medical fields related to cardiovascular diseases. Rapid technological innovations in magnetic resonance imaging in recent times have resulted in the development of new techniques for CMR imaging. T1 and T2 image mapping sequences enable the direct quantification of T1, T2, and extracellular volume fraction (ECV) values of the myocardium, leading to the progressive integration of these sequences into routine CMR settings. Currently, T1, T2, and ECV values are being recognized as not only robust biomarkers for diagnosis of cardiomyopathies, but also predictive factors for treatment monitoring and prognosis. In this study, we have reviewed various T1 and T2 mapping sequence techniques and their clinical applications.

Objective: The aims of this study were to compare opening and closing angles of normally functioning mechanical aortic valves measured on dual-source computed tomography (CT) with the manufacturers' values and to compare CT-measured opening angles according to valve function., Materials and Methods: A total of 140 patients with 10 different types of mechanical aortic valves, who underwent dual-source cardiac CT, were included. Opening and closing angles were measured on CT images. Agreement between angles in normally functioning valves and the manufacturer values was assessed using the interclass coefficient and the Bland-Altman method. CT-measured opening angles were compared between normal functioning valves and suspected dysfunctioning valves., Results: The CT-measured opening angles of normally functioning valves and manufacturers' values showed excellent agreement for seven valve types (intraclass coefficient [ICC], 0.977; 95% confidence interval [CI], 0.962-0.987). The mean differences in opening angles between the CT measurements and the manufacturers' values were 1.2° in seven types of valves, 11.0° in On-X valves, and 15.5° in ATS valves. The manufacturers' closing angles and those measured by CT showed excellent agreement for all valve types (ICC, 0.953; 95% CI, 0.920-0.972). Among valves with suspected dysfunction, those with limitation of motion (LOM) and an increased pressure gradient (PG) had smaller opening angles than those with LOM only (p < 0.05)., Conclusion: Dual-source cardiac CT accurately measures opening and closing angles in most types of mechanical aortic valves, compared with the manufacturers' values. Opening angles on CT differ according to the type of valve dysfunction and a decreased opening angle may suggest an elevated PG.