Objective: This study investigated the feasibility and prognostic relevance of threshold-based quantification of myocardial delayed enhancement (MDE) on CT in patients with nonischemic dilated cardiomyopathy (NIDCM). Materials and Methods: Forty-three patients with NIDCM (59.3 ± 17.1 years; 21 male) were included in the study and underwent cardiac CT and MRI. MDE was quantified manually and with a threshold-based quantification method using cutoffs of 2, 3, and 4 standard deviations (SDs) on three sets of CT images (100 kVp, 120 kVp, and 70 keV). Interobserver agreement in MDE quantification was assessed using the intraclass correlation coefficient (ICC). Agreement between CT and MRI was evaluated using the Bland-Altman method and the concordance correlation coefficient (CCC). Patients were followed up for the subsequent occurrence of the primary composite outcome, including cardiac death, heart transplantation, heart failure hospitalization, or appropriate use of an implantable cardioverter-defibrillator. The Kaplan–Meier method was used to estimate event-free survival according to MDE levels. Results: Late gadolinium enhancement (LGE) was observed in 29 patients (67%, 29/43), and the mean LGE found with the 5-SD threshold was 4.1% ± 3.6%. The 4-SD threshold on 70-keV CT showed excellent interobserver agreement (ICC = 0.810) and the highest concordance with MRI (CCC = 0.803). This method also yielded the smallest bias with the narrowest range of 95% limits of agreement compared to MRI (bias, -0.119%; 95% limits of agreement, -4.216% to 3.978%). During a median follow-up of 1625 days (interquartile range, 712–1430 days), 10 patients (23%, 10/43) experienced the primary composite outcome. Event-free survival significantly differed between risk subgroups divided by the optimal MDE cutoff of 4.3% (log-rank P = 0.005). Conclusion The 4-SD threshold on 70-keV monochromatic CT yielded results comparable to those of MRI for quantifying MDE as a marker of myocardial fibrosis, which showed prognostic value in patients with NIDCM.

Objective: To quantitatively analyze the cardiac magnetic resonance imaging (CMR) characteristics of chemotherapy-related cardiac dysfunction (CTRCD) and explore their prognostic value for major adverse cardiovascular events (MACE). Materials and Methods: A total of 145 patients (male:female = 76:69, mean age = 63.0 years) with cancer and heart failure who underwent CMR between January 2015 and January 2021 were included. CMR was performed using a 3T scanner (Siemens). Biventricular functions, native T1 T2, extracellular volume fraction (ECV) values, and late gadolinium enhancement (LGE) of the left ventricle (LV) were compared between those with and without CTRCD. These were compared between patients with mild-to-moderate CTRCD and those with severe CTRCD. Cox proportional hazard regression analysis was used to evaluate the association between the CMR parameters and MACE occurrence during follow-up in the CTRCD patients. Results: Among 145 patients, 61 had CTRCD and 84 did not have CTRCD. Native T1, ECV, and T2 were significantly higher in the CTRCD group (1336.9 ms, 32.5%, and 44.7 ms, respectively) than those in the non-CTRCD group (1303.4 ms, 30.5%, and 42.0 ms, respectively; P = 0.013, 0.010, and < 0.001, respectively). They were not significantly different between patients with mild-to-moderate and severe CTRCD. Indexed LV mass was significantly smaller in the CTRCD group (65.0 g/m2 vs. 78.9 g/m2 ; P < 0.001). According to the multivariable Cox regression analysis, T2 (hazard ratio [HR]: 1.14, 95% confidence interval [CI]: 1.01–1.27; P = 0.028) and quantified LGE (HR: 1.07, 95% CI: 1.01–1.13; P = 0.021) were independently associated with MACE in the CTRCD patients. Conclusion Quantitative parameters from CMR have the potential to evaluate myocardial changes in CTRCD. Increased T2 with reduced LV mass was demonstrated in CTRCD patients even before the development of severe cardiac dysfunction. T2 and quantified LGE may be independent prognostic factors for MACE in patients with CTRCD.

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.

Objective: We aimed to evaluate the ostium of right coronary artery of anomalous origin from the left coronary sinus (AORL) with an interarterial course throughout the cardiac cycle on CT and analyze the clinical significance of the ostial findings. Materials and Methods: From January 2011 to December 2015, 68 patients (41 male, 57.3 ± 12.1 years) with AORL with an interarterial course and retrospective cardiac CT data were included. AORL was classified as high or low ostial location based on the pulmonary annulus in the diastolic and systolic phases on cardiac CT. In addition, the height, width, height/width ratio, area, and angle of the ostium were measured in both cardiac phases. After cardiac CT, patients were followed until December 31, 2020 for major adverse cardiac events (MACE). Clinical and CT characteristics associated with MACE were explored using Cox regression analysis. Results: During a median follow-up period of 2071 days (interquartile range, 1180.5–2747.3 days), 13 patients experienced MACE (19.1%, 13/68). Seven (10.3%, 7/68) had the ostial location change from high in the diastolic phase to low in the systolic phase. In the univariable analysis, younger age (hazard ratio [HR] = 0.918, p < 0.001), high ostial location (HR = 4.008, p = 0.036), larger height/width ratio (HR = 5.621, p = 0.049), and smaller ostial angle (HR = 0.846, p = 0.048) in the systolic phase were significant predictors of MACE. In multivariable cox regression analysis, younger age (adjusted HR = 0.917, p = 0.002) and high ostial location in the systolic phase (adjusted HR = 4.345, p = 0.026) were independent predictors of MACE. Conclusion The ostial location of AORL with an interarterial course can change during the cardiac cycle, and high ostial location in the systolic phase was an independent predictor of MACE.

Purpose: This study aimed to assess the factors influencing aortic unfolding (AU) defined by aortic width on coronary artery calcium (CAC) scan and determine the normal limits for AU. Materials and Methods: In this retrospective study, we measured AU in 924 asymptomatic subjects who underwent CAC scanning during routine health screening from June 2015 to June 2018. Multivariate regression analysis was used to evaluate the factors influencing AU. After the exclusion of subjects with risk factors associated with AU, 283 subjects were included in the analysis of normal values of AU. Mean AU, standard deviation, and upper normal limit were calculated. Results: Sex, age, CAC score, body mass index, body surface area, hypertension, left ventricular hypertrophy, plasma creatinine, and smoking were significantly associated with AU. The mean AU was 102.2 ± 12.8 mm for men and 93.1 ± 10.7 mm for women. AU increased with advancing age (9.6 mm per decade). Conclusion AU determined from a single measurement on CAC scans was associated with cardiovascular risk factors. The normal limits of AU were defined by age, sex, and body surface area in low-risk subjects in this study.

Background/Aims: Hip fracture and acute exacerbation of chronic obstructive pulmonary disease (AE-COPD) could increase mortality in patients with COPD. There are no data on the relationship between AE-COPD and hip fracture, which may significantly affect the prognosis of patients with COPD. Therefore, we conducted this study to determine the effects of AE-COPD on hip fractures in patients with COPD. Methods: This retrospective, nested, case-control study included 253,471 patients with COPD (≥ 40 years of age) identified from the Korea National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) from 2002 to 2015. Among 176,598 patients with COPD, 1,415 patients with hip fractures were identified. Each case was matched to one control for age (within 10 years), sex, and year of COPD diagnosis. We estimated the adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for hip fractures associated with AE-COPD using conditional logistic regression analysis, adjusting for underlying diseases and smoking history. Results: In patients with AE-COPD, the risk of hip fracture was 2.50 times higher, regardless of systemic corticosteroid use and underlying disease (aOR, 2.50; 95% CI, 1.67 to 3.75). The risk of hip fracture increased if there was one episode of AE in the year before hip fractures (aOR, 2.25; 95% CI, 1.66 to 3.05). Moreover, the risk of hip fracture also increased in patients with more than two episodes of AE the year before hip fractures (aOR, 2.57; 95% CI, 1.61 to 4.10). Conclusions AE-COPD increases the risk of hip fracture regardless of underlying diseases, including osteoporosis, and treatment with systemic corticosteroids.

Background/Aims: We examined the efficacy and safety of tegoprazan as a part of first-line triple therapy for Helicobacter pylori eradication. Methods: A randomized, double-blind, controlled, multicenter study was performed to evaluate whether tegoprazan (50 mg)-based triple therapy (TPZ) was noninferior to lansoprazole (30 mg)-based triple therapy (LPZ) (with amoxicillin 1 g and clarithromycin 500 mg; all administered twice daily for 7 days) for treating H. pylori. The primary endpoint was the H. pylori eradication rate. Subgroup analyses were performed according to the cytochrome P450 (CYP) 2C19 genotype, the minimum inhibitory concentration (MIC) of amoxicillin and clarithromycin, and underlying gastric diseases. Results: In total, 350 H. pylori-positive patients were randomly allocated to the TPZ or LPZ group. The H. pylori eradication rates in the TPZ and LPZ groups were 62.86% (110/175) and 60.57% (106/175) in an intention-to-treat analysis and 69.33% (104/150) and 67.33% (101/150) in a per-protocol analysis (non-inferiority test, p=0.009 and p=0.013), respectively. Subgroup analyses according to MICs or CYP2C19 did not show remarkable differences in eradication rate. Both first-line triple therapies were well-tolerated with no notable differences. Conclusions TPZ is as effective as proton pump inhibitor-based triple therapy and is as safe as first-line H. pylori eradication therapy but does not overcome the clarithromycin resistance of H. pylori in Korea

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.

Purpose: To construct a standard dataset of contrast-enhanced CT images of liver tumors to test the performance and safety of artificial intelligence (AI)-based algorithms for clinical decision support systems (CDSSs). Materials and Methods: A consensus group of medical experts in gastrointestinal radiology from four national tertiary institutions discussed the conditions to be included in a standard dataset. Seventy-five cases of hepatocellular carcinoma, 75 cases of metastasis, and 30–50 cases of benign lesions were retrieved from each institution, and the final dataset consisted of 300 cases of hepatocellular carcinoma, 300 cases of metastasis, and 183 cases of benign lesions.Only pathologically confirmed cases of hepatocellular carcinomas and metastases were enrolled. The medical experts retrieved the medical records of the patients and manually labeled the CT images. The CT images were saved as Digital Imaging and Communications in Medicine (DICOM) files. Results: The medical experts in gastrointestinal radiology constructed the standard dataset of contrast-enhanced CT images for 783 cases of liver tumors. The performance and safety of the AI algorithm can be evaluated by calculating the sensitivity and specificity for detecting and characterizing the lesions. Conclusion The constructed standard dataset can be utilized for evaluating the machine-learningbased AI algorithm for CDSS.

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.