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Objective: This study aimed to evaluate the diagnostic efficacy and safety of low-contrast-dose, dual-source dual-energy CT before transcatheter aortic valve replacement (TAVR) in patients with compromised renal function., Materials and Methods: A total of 54 consecutive patients (female:male, 26:38; 81.9 ± 7.3 years) with reduced renal function underwent pre-TAVR dual-energy CT with a 30-mL contrast agent between June 2022 and March 2023. Monochromatic (40- and 50-keV) and conventional (120-kVp) images were reconstructed and analyzed. The subjective quality score, vascular attenuation, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were compared among the imaging techniques using the Friedman test and post-hoc analysis. Interobserver reliability for aortic annular measurement was assessed using the intraclass correlation coefficient (ICC) and Bland-Altman analysis. The procedural outcomes and incidence of post-contrast acute kidney injury (AKI) were assessed., Results: Monochromatic images achieved diagnostic quality in all patients. The 50-keV images achieved superior vascular attenuation and CNR ( P < 0.001 in all) while maintaining a similar SNR compared to conventional CT. For aortic annular measurement, the 50-keV images showed higher interobserver reliability compared to conventional CT: ICC, 0.98 vs. 0.90 for area and 0.97 vs. 0.95 for perimeter; 95% limits of agreement width, 0.63 cm² vs. 0.92 cm² for area and 5.78 mm vs. 8.50 mm for perimeter. The size of the implanted device matched CT-measured values in all patients, achieving a procedural success rate of 92.6%. No patient experienced a serum creatinine increase of ≥ 1.5 times baseline in the 48-72 hours following CT. However, one patient had a procedural delay due to gradual renal function deterioration., Conclusion: Low-contrast-dose imaging with 50-keV reconstruction enables precise pre-TAVR evaluation with improved image quality and minimal risk of post-contrast AKI. This approach may be an effective and safe option for pre-TAVR evaluation in patients with compromised renal function., Competing Interests: Kyunghwa Han, who holds the respective position of Statistical Consultant of the Korean Journal of Radiology, was not involved in the editorial evaluation or decision to publish this article. The remaining author has declared no conflicts of interest., (Copyright © 2024 The Korean Society of Radiology.)

Objective: In Korea, radiology has been positioned towards the early adoption of artificial intelligence-based software as medical devices (AI-SaMDs); however, little is known about the current usage, implementation, and future needs of AI-SaMDs. We surveyed the current trends and expectations for AI-SaMDs among members of the Korean Society of Radiology (KSR)., Materials and Methods: An anonymous and voluntary online survey was open to all KSR members between April 17 and May 15, 2023. The survey was focused on the experiences of using AI-SaMDs, patterns of usage, levels of satisfaction, and expectations regarding the use of AI-SaMDs, including the roles of the industry, government, and KSR regarding the clinical use of AI-SaMDs., Results: Among the 370 respondents (response rate: 7.7% [370/4792]; 340 board-certified radiologists; 210 from academic institutions), 60.3% (223/370) had experience using AI-SaMDs. The two most common use-case of AI-SaMDs among the respondents were lesion detection (82.1%, 183/223), lesion diagnosis/classification (55.2%, 123/223), with the target imaging modalities being plain radiography (62.3%, 139/223), CT (42.6%, 95/223), mammography (29.1%, 65/223), and MRI (28.7%, 64/223). Most users were satisfied with AI-SaMDs (67.6% [115/170, for improvement of patient management] to 85.1% [189/222, for performance]). Regarding the expansion of clinical applications, most respondents expressed a preference for AI-SaMDs to assist in detection/diagnosis (77.0%, 285/370) and to perform automated measurement/quantification (63.5%, 235/370). Most respondents indicated that future development of AI-SaMDs should focus on improving practice efficiency (81.9%, 303/370) and quality (71.4%, 264/370). Overall, 91.9% of the respondents (340/370) agreed that there is a need for education or guidelines driven by the KSR regarding the use of AI-SaMDs., Conclusion: The penetration rate of AI-SaMDs in clinical practice and the corresponding satisfaction levels were high among members of the KSR. Most AI-SaMDs have been used for lesion detection, diagnosis, and classification. Most respondents requested KSR-driven education or guidelines on the use of AI-SaMDs., Competing Interests: Eui Jin Hwang reports research grants from Lunit, Coreline Soft, and Monitor Corporation outside the present work. Chang Min Park reports research grants from Lunit and Coreline Soft outside the present work, and stock of Promedius, Lunit and Coreline Soft and stock options of Lunit and Coreline Soft., (Copyright © 2024 The Korean Society of Radiology.)

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., Competing Interests: Dr. Hye-Jeong Lee, and Dr. Jin Hur, the contributing editors of the Korean Journal of Radiology, and the Statistical Consultant for Korean Journal of Radiology, Prof. Kyunghwa Han, were 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: This study aimed to develop and validate models using radiomics features on a native T1 map from cardiac magnetic resonance (CMR) to predict left ventricular reverse remodeling (LVRR) in patients with nonischemic dilated cardiomyopathy (NIDCM)., Materials and Methods: Data from 274 patients with NIDCM who underwent CMR imaging with T1 mapping at Severance Hospital between April 2012 and December 2018 were retrospectively reviewed. Radiomic features were extracted from the native T1 maps. LVRR was determined using echocardiography performed ≥ 180 days after the CMR. The radiomics score was generated using the least absolute shrinkage and selection operator logistic regression models. Clinical, clinical + late gadolinium enhancement (LGE), clinical + radiomics, and clinical + LGE + radiomics models were built using a logistic regression method to predict LVRR. For internal validation of the result, bootstrap validation with 1000 resampling iterations was performed, and the optimism-corrected area under the receiver operating characteristic curve (AUC) with 95% confidence interval (CI) was computed. Model performance was compared using AUC with the DeLong test and bootstrap., Results: Among 274 patients, 123 (44.9%) were classified as LVRR-positive and 151 (55.1%) as LVRR-negative. The optimism-corrected AUC of the radiomics model in internal validation with bootstrapping was 0.753 (95% CI, 0.698-0.813). The clinical + radiomics model revealed a higher optimism-corrected AUC than that of the clinical + LGE model (0.794 vs. 0.716; difference, 0.078 [99% CI, 0.003-0.151]). The clinical + LGE + radiomics model significantly improved the prediction of LVRR compared with the clinical + LGE model (optimism-corrected AUC of 0.811 vs. 0.716; difference, 0.095 [99% CI, 0.022-0.139])., Conclusion: The radiomic characteristics extracted from a non-enhanced T1 map may improve the prediction of LVRR and offer added value over traditional LGE in patients with NIDCM. Additional external validation research is required., 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: To evaluate the feasibility of ultrasound shear wave elastography (SWE) for predicting hepatic fibrosis and native liver outcomes in patients with biliary atresia., Materials and Methods: This prospective study included 33 consecutive patients with biliary atresia (median age, 8 weeks [interquartile range, 6-10 weeks]; male:female ratio, 15:18) from Severance Children's Hospital between May 2019 and February 2022. Preoperative (within 1 week from surgery) and immediate postoperative (on postoperative days [PODs] 3, 5, and 7) ultrasonographic findings were obtained and analyzed, including the SWE of the liver and spleen. Hepatic fibrosis, according to the METAVIR score at the time of Kasai portoenterostomy and native liver outcomes during postsurgical follow-up, were compared and correlated with imaging and laboratory findings. Poor outcomes were defined as intractable cholangitis or liver transplantation. The diagnostic performance of SWE in predicting METAVIR F3-F4 and poor hepatic outcomes was analyzed using receiver operating characteristic (ROC) analyses., Results: All patients were analyzed without exclusion. Perioperative advanced hepatic fibrosis (F3-F4) was associated with older age and higher preoperative direct bilirubin and SWE values in the liver and spleen. Preoperative liver SWE showed a ROC area of 0.806 and 63.6% (7/11) sensitivity and 86.4% (19/22) specificity at a cutoff of 17.5 kPa for diagnosing F3-F4. The poor outcome group included five patients with intractable cholangitis and three undergoing liver transplantation who showed high postoperative liver SWE values. Liver SWE on PODs 3-7 showed ROC areas of 0.783-0.891 for predicting poor outcomes, and a cutoff value of 10.3 kPa for SWE on POD 3 had 100% (8/8) sensitivity and 73.9% (17/23) specificity., Conclusion: Preoperative liver SWE can predict advanced hepatic fibrosis, and immediate postoperative liver SWE can predict poor native liver outcomes in patients with biliary atresia., Competing Interests: Kyunghwa Han and Mi-Jung Lee, contributing editors of the Korean Journal of Radiology, were 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: 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.)

Purpose: This study aimed to investigate radiomics analysis of ultrasonographic images to develop a potential biomarker for predicting lymph node metastasis in papillary thyroid carcinoma (PTC) patients., Materials and Methods: This study included 431 PTC patients from August 2013 to May 2014 and classified them into the training and validation sets. A total of 730 radiomics features, including texture matrices of gray-level co-occurrence matrix and gray-level run-length matrix and single-level discrete two-dimensional wavelet transform and other functions, were obtained. The least absolute shrinkage and selection operator method was used for selecting the most predictive features in the training data set., Results: Lymph node metastasis was associated with the radiomics score ( p < 0.001). It was also associated with other clinical variables such as young age ( p = 0.007) and large tumor size ( p = 0.007). The area under the receiver operating characteristic curve was 0.687 (95% confidence interval: 0.616-0.759) for the training set and 0.650 (95% confidence interval: 0.575-0.726) for the validation set., Conclusion: This study showed the potential of ultrasonography-based radiomics to predict cervical lymph node metastasis in patients with PTC; thus, ultrasonography-based radiomics can act as a biomarker for PTC., Competing Interests: Conflicts of Interest: The authors have no potential conflicts of interest to disclose., (Copyrights © 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: Radiomic modeling using multiple regions of interest in MRI of the brain to diagnose juvenile myoclonic epilepsy (JME) has not yet been investigated. This study aimed to develop and validate radiomics prediction models to distinguish patients with JME from healthy controls (HCs), and to evaluate the feasibility of a radiomics approach using MRI for diagnosing JME., Materials and Methods: A total of 97 JME patients (25.6 ± 8.5 years; female, 45.5%) and 32 HCs (28.9 ± 11.4 years; female, 50.0%) were randomly split (7:3 ratio) into a training (n = 90) and a test set (n = 39) group. Radiomic features were extracted from 22 regions of interest in the brain using the T1-weighted MRI based on clinical evidence. Predictive models were trained using seven modeling methods, including a light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, with radiomics features in the training set. The performance of the models was validated and compared to the test set. The model with the highest area under the receiver operating curve (AUROC) was chosen, and important features in the model were identified., Results: The seven tested radiomics models, including light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, showed AUROC values of 0.817, 0.807, 0.783, 0.779, 0.767, 0.762, and 0.672, respectively. The light gradient boosting machine with the highest AUROC, albeit without statistically significant differences from the other models in pairwise comparisons, had accuracy, precision, recall, and F1 scores of 0.795, 0.818, 0.931, and 0.871, respectively. Radiomic features, including the putamen and ventral diencephalon, were ranked as the most important for suggesting JME., Conclusion: Radiomic models using MRI were able to differentiate JME from HCs., 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. All remaining authors have declared no conflicts of interest., (Copyright © 2022 The Korean Society of Radiology.)

Clinical prediction models has been increasingly published in radiology research. In particular, as a radiomics research is being actively conducted, the prediction model is developed based on the traditional statistical model, as well as machine learning, to account for the high-dimensional data. In this review, we investigated the statistical and machine learning methods used in clinical prediction model research, and briefly summarized each analytical method for statistical model, machine learning, and statistical learning. Finally, we discussed several considerations for choosing the prediction modeling method., Competing Interests: Conflicts of Interest: The authors have no potential conflicts of interest to disclose., (Copyrights © 2022 The Korean Society of Radiology.)

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., 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. All remaining authors have declared no conflicts of interest., (Copyright © 2022 The Korean Society of Radiology.)

Objective: To assess the feasibility of quantitatively assessing pancreatic steatosis using magnetic resonance imaging (MRI) and its correlation with obesity and metabolic risk factors in pediatric patients., Materials and Methods: Pediatric patients (≤ 18 years) who underwent liver fat quantification MRI between January 2016 and June 2019 were retrospectively included and divided into the obesity and control groups. Pancreatic proton density fat fraction (P-PDFF) was measured as the average value for three circular regions of interest (ROIs) drawn in the pancreatic head, body, and tail. Age, weight, laboratory results, and mean liver MRI values including liver PDFF (L-PDFF), stiffness on MR elastography, and T2 * values were assessed for their correlation with P-PDFF using linear regression analysis. The associations between P-PDFF and metabolic risk factors, including obesity, hypertension, diabetes mellitus (DM), and dyslipidemia, were assessed using logistic regression analysis., Results: A total of 172 patients (male:female = 125:47; mean ± standard deviation [SD], 13.2 ± 3.1 years) were included. The mean P-PDFF was significantly higher in the obesity group than in the control group (mean ± SD, 4.2 ± 2.5% vs. 3.4 ± 2.4%; p = 0.037). L-PDFF and liver stiffness values showed no significant correlation with P-PDFF ( p = 0.235 and p = 0.567, respectively). P-PDFF was significantly associated with obesity (odds ratio 1.146, 95% confidence interval 1.006-1.307, p = 0.041), but there was no significant association with hypertension, DM, and dyslipidemia., Conclusion: MRI can be used to quantitatively measure pancreatic steatosis in children. P-PDFF is significantly associated with obesity in pediatric patients., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2021 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: To compare the performance of the deep learning-based lesion detection algorithm (DLLD) in detecting liver metastasis with that of radiologists., Materials and Methods: This clinical retrospective study used 4386-slice computed tomography (CT) images and labels from a training cohort (502 patients with colorectal cancer [CRC] from November 2005 to December 2010) to train the DLLD for detecting liver metastasis, and used CT images of a validation cohort (40 patients with 99 liver metastatic lesions and 45 patients without liver metastasis from January 2011 to December 2011) for comparing the performance of the DLLD with that of readers (three abdominal radiologists and three radiology residents). For per-lesion binary classification, the sensitivity and false positives per patient were measured., Results: A total of 85 patients with CRC were included in the validation cohort. In the comparison based on per-lesion binary classification, the sensitivity of DLLD (81.82%, [81/99]) was comparable to that of abdominal radiologists (80.81%, p = 0.80) and radiology residents (79.46%, p = 0.57). However, the false positives per patient with DLLD (1.330) was higher than that of abdominal radiologists (0.357, p < 0.001) and radiology residents (0.667, p < 0.001)., Conclusion: DLLD showed a sensitivity comparable to that of radiologists when detecting liver metastasis in patients initially diagnosed with CRC. However, the false positives of DLLD were higher than those of radiologists. Therefore, DLLD could serve as an assistant tool for detecting liver metastasis instead of a standalone diagnostic tool., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2021 The Korean Society of Radiology.)

Objective: This study aimed to investigate the regional amyloid burden and myocardial deformation using T1 mapping and strain values in patients with cardiac amyloidosis (CA) according to late gadolinium enhancement (LGE) patterns., Materials and Methods: Forty patients with CA were divided into 2 groups per LGE pattern, and 15 healthy subjects were enrolled. Global and regional native T1 and T2 mapping, extracellular volume (ECV), and cardiac magnetic resonance (CMR)-feature tracking strain values were compared in an intergroup and interregional manner., Results: Of the patients with CA, 32 had diffuse global LGE (group 2), and 8 had focal patchy or no LGE (group 1). Global native T1, T2, and ECV were significantly higher in groups 1 and 2 than in the control group (native T1: 1384.4 ms vs. 1466.8 ms vs. 1230.5 ms; T2: 53.8 ms vs. 54.2 ms vs. 48.9 ms; and ECV: 36.9% vs. 51.4% vs. 26.0%, respectively; all, p < 0.001). Basal ECV (53.7%) was significantly higher than the mid and apical ECVs (50.1% and 50.0%, respectively; p < 0.001) in group 2. Basal and mid peak radial strains (PRSs) and peak circumferential strains (PCSs) were significantly lower than the apical PRS and PCS, respectively (PRS, 15.6% vs. 16.7% vs. 26.9%; and PCS, -9.7% vs. -10.9% vs. -15.0%; all, p < 0.001). Basal ECV and basal strain (2-dimensional PRS) in group 2 showed a significant negative correlation ( r = -0.623, p < 0.001). Group 1 showed no regional ECV differences (basal, 37.0%; mid, 35.9%; and apical, 38.3%; p = 0.184)., Conclusion: Quantitative T1 mapping parameters such as native T1 and ECV may help diagnose early CA. ECV, in particular, can reflect regional differences in the amyloid deposition in patients with advanced CA, and increased basal ECV is related to decreased basal strain. Therefore, quantitative CMR parameters may help diagnose CA and determine its severity in patients with or without LGE., Competing Interests: The authors report no relationships that could be construed as a conflict of interest., (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.)

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: To compare native and post-contrast T1 mapping with late gadolinium enhancement (LGE) imaging for detecting and measuring the microvascular obstruction (MVO) area in reperfused acute myocardial infarction (MI)., Materials and Methods: This study included 20 patients with acute MI who had undergone 1.5T cardiovascular magnetic resonance imaging (CMR) after reperfusion therapy. CMR included cine imaging, LGE, and T1 mapping (modified look-locker inversion recovery). MI size was calculated from LGE by full-width at half-maximum technique. MVO was defined as an area with low signal intensity (LGE) or as a region of visually distinguishable T1 values (T1 maps) within infarcted myocardium. Regional T1 values were measured in MVO, infarcted, and remote myocardium on T1 maps. MVO area was measured on and compared among LGE, native, and post-contrast T1 maps., Results: The mean MI size was 27.1 ± 9.7% of the left ventricular mass. Of the 20 identified MVOs, 18 (90%) were detected on native T1 maps, while 10 (50%) were recognized on post-contrast T1 maps. The mean native T1 values of MVO, infarcted, and remote myocardium were 1013.5 ± 58.5, 1240.9 ± 55.8 ( p < 0.001), and 1062.2 ± 55.8 ms ( p = 0.169), respectively, while the mean post-contrast T1 values were 466.7 ± 26.8, 399.1 ± 21.3, and 585.2 ± 21.3 ms, respectively ( p < 0.001). The mean MVO areas on LGE, native, and post-contrast T1 maps were 134.1 ± 81.2, 133.7 ± 80.4, and 117.1 ± 53.3 mm², respectively. The median (interquartile range) MVO areas on LGE, native, and post-contrast T1 maps were 128.0 (58.1-215.4), 110.5 (67.7-227.9), and 143.0 (76.7-155.3) mm², respectively ( p = 0.002). Concordance correlation coefficients for the MVO area between LGE and native T1 maps, LGE and post-contrast T1 maps, and native and post-contrast T1 maps were 0.770, 0.375, and 0.565, respectively., Conclusion: MVO areas were accurately delineated on native T1 maps and showed high concordance with the areas measured on LGE. However, post-contrast T1 maps had low detection rates and underestimated MVO areas. Collectively, native T1 mapping is a useful tool for detecting MVO within the infarcted myocardium., 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 examine time trends in ultrasonography (US)-guided 14-gauge core needle biopsy (CNB) for breast lesions based on the lesion size, Breast Imaging-Reporting and Data System (BI-RADS) category, and pathologic findings., Materials and Methods: We retrospectively reviewed consecutive US-guided 14-gauge CNBs performed from January 2005 to December 2016 at our institution. A total of 22,297 breast lesions were included. The total number of biopsies, tumor size (≤ 10 mm to > 40 mm), BI-RADS category (1 to 5), and pathologic findings (benign, high risk, ductal carcinoma in situ [DCIS], invasive cancer) were examined annually, and the malignancy rate was analyzed based on the BI-RADS category., Results: Both the total number of US scans and US-guided CNBs increased while the proportion of US-guided CNBs to the total number of US scans decreased significantly. The number of biopsies classified based on the tumor size, BI-RADS category, and pathologic findings all increased over time, except for BI-RADS categories 1 or 2 and category 3 (odds ratio [OR] = 0.951 per year, 95% confidence interval [CI]: 0.902, 1.002 and odds ratio = 0.979, 95% CI: 0.970, 0.988, respectively). Both the unadjusted and adjusted total malignancy rates and the DCIS rate increased significantly over time. BI-RADS categories 4a, 4b, and 4c showed a significant increasing trend in the total malignancy rate and DCIS rate., Conclusion: The malignancy rate in the results of US-guided 14-gauge CNB for breast lesions increased as the total number of biopsies increased from 2005 to 2016. This trend persisted after adjusting for the BI-RADS category., Competing Interests: All authors have no conflict of interest including financial or consultant, institutional and other relationship in this study., (Copyright © 2020 The Korean Society of Radiology.)

Objective: To identify an imaging predictor for the assessment of early treatment response to yttrium-90 transarterial radioembolization (TARE) in patients with hepatocellular carcinoma (HCC), using a quantitative assessment of dynamic computed tomography (CT) images., Materials and Methods: Dynamic contrast-enhanced CT was obtained pre- and 4 weeks post-TARE in 44 patients (34 men, 10 women; mean age, 60 years) with HCC. Computer software was developed for measuring the percentage increase in the combined delayed-enhancing area and necrotic area (pD + N) and the percentage increase in the necrotic area (pNI) in the tumor-containing segments pre- and post-TARE. Local progression-free survival (PFS) was compared between patient groups using Cox regression and Kaplan-Meier analyses., Results: Post-TARE HCC with pD + N ≥ 35.5% showed significantly longer PFS than those with pD + N < 35.5% ( p = 0.001). The local tumor progression hazard ratio was 17.3 ( p = 0.009) for pD + N < 35.5% versus pD + N ≥ 35.5% groups. HCCs with a high pNI tended to have longer PFS, although this difference did not reach statistical significance., Conclusion: HCCs with a larger pD + N are less likely to develop local progression after TARE., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2019 The Korean Society of Radiology.)

Objective: To measure inter-protocol agreement and analyze interchangeability on nodule classification between low-dose unenhanced CT and standard-dose enhanced CT., Materials and Methods: From nodule libraries containing both low-dose unenhanced and standard-dose enhanced CT, 80 solid and 80 subsolid (40 part-solid, 40 non-solid) nodules of 135 patients were selected. Five thoracic radiologists categorized each nodule into solid, part-solid or non-solid. Inter-protocol agreement between low-dose unenhanced and standard-dose enhanced images was measured by pooling κ values for classification into two (solid, subsolid) and three (solid, part-solid, non-solid) categories. Interchangeability between low-dose unenhanced and standard-dose enhanced CT for the classification into two categories was assessed using a pre-defined equivalence limit of 8 percent., Results: Inter-protocol agreement for the classification into two categories {κ, 0.96 (95% confidence interval [CI], 0.94-0.98)} and that into three categories (κ, 0.88 [95% CI, 0.85-0.92]) was considerably high. The probability of agreement between readers with standard-dose enhanced CT was 95.6% (95% CI, 94.5-96.6%), and that between low-dose unenhanced and standard-dose enhanced CT was 95.4% (95% CI, 94.7-96.0%). The difference between the two proportions was 0.25% (95% CI, -0.85-1.5%), wherein the upper bound CI was markedly below 8 percent., Conclusion: Inter-protocol agreement for nodule classification was considerably high. Low-dose unenhanced CT can be used interchangeably with standard-dose enhanced CT for nodule classification.

Objective: To evaluate the frequency and adequacy of statistical analyses in a general radiology journal when reporting a reliability analysis for a diagnostic test., Materials and Methods: Sixty-three studies of diagnostic test accuracy (DTA) and 36 studies reporting reliability analyses published in the Korean Journal of Radiology between 2012 and 2016 were analyzed. Studies were judged using the methodological guidelines of the Radiological Society of North America-Quantitative Imaging Biomarkers Alliance (RSNA-QIBA), and COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) initiative. DTA studies were evaluated by nine editorial board members of the journal. Reliability studies were evaluated by study reviewers experienced with reliability analysis., Results: Thirty-one (49.2%) of the 63 DTA studies did not include a reliability analysis when deemed necessary. Among the 36 reliability studies, proper statistical methods were used in all (5/5) studies dealing with dichotomous/nominal data, 46.7% (7/15) of studies dealing with ordinal data, and 95.2% (20/21) of studies dealing with continuous data. Statistical methods were described in sufficient detail regarding weighted kappa in 28.6% (2/7) of studies and regarding the model and assumptions of intraclass correlation coefficient in 35.3% (6/17) and 29.4% (5/17) of studies, respectively. Reliability parameters were used as if they were agreement parameters in 23.1% (3/13) of studies. Reproducibility and repeatability were used incorrectly in 20% (3/15) of studies., Conclusion: Greater attention to the importance of reporting reliability, thorough description of the related statistical methods, efforts not to neglect agreement parameters, and better use of relevant terminology is necessary.

Objective: To prospectively evaluate the performance of diffusion-weighted imaging (DWI) to monitor bowel inflammation after medical therapy for Crohn's disease (CD)., Materials and Methods: Before and following 1-2 years of medical therapy, between October 2012 and May 2015, 18 randomly selected adult CD patients (male:female, 13:5; mean age ± SD, 25.8 ± 7.9 years at the time of enrollment) prospectively underwent MR enterography (MRE) including DWI (b = 900 s/mm 2 ) and ileocolonoscopy. Thirty-seven prospectively defined index lesions (one contiguous endoscopy-confirmed inflamed area chosen from each inflamed anatomical bowel segment; 1-4 index lesions per patient; median, 2 lesions) were assessed on pre- and post-treatment MRE and endoscopy. Visual assessment of treatment responses on DWI in 4 categories including complete remission and reduced, unchanged or increased inflammation, and measurements of changes in apparent diffusion coefficient (ΔADC), i.e., pre-treatment-post-treatment, were performed by 2 independent readers. Endoscopic findings and CD MRI activity index (CDMI) obtained using conventional MRE served as reference standards., Results: ΔADC significantly differed between improved (i.e., complete remission and reduced inflammation) and unimproved (i.e., unchanged or increased inflammation) lesions: mean ± SD (× 10 -3 mm 2 /s) of -0.65 ± 0.58 vs. 0.06 ± 0.15 for reader 1 ( p = 0.022) and -0.68 ± 0.56 vs. 0.10 ± 0.26 for reader 2 ( p = 0.025). DWI accuracy for diagnosing complete remission or improved inflammation ranged from 76% (28/37) to 84% (31/37). A significant negative correlation was noted between ΔADC and ΔCDMI for both readers with correlation coefficients of -0.438 and -0.461, respectively ( p < 0.05)., Conclusion: DWI is potentially a feasible tool to monitor quantitatively and qualitatively bowel inflammation of CD after medical treatment.

Clinical prediction models are developed to calculate estimates of the probability of the presence/occurrence or future course of a particular prognostic or diagnostic outcome from multiple clinical or non-clinical parameters. Radiologic imaging techniques are being developed for accurate detection and early diagnosis of disease, which will eventually affect patient outcomes. Hence, results obtained by radiological means, especially diagnostic imaging, are frequently incorporated into a clinical prediction model as important predictive parameters, and the performance of the prediction model may improve in both diagnostic and prognostic settings. This article explains in a conceptual manner the overall process of developing and validating a clinical prediction model involving radiological parameters in relation to the study design and statistical methods. Collection of a raw dataset; selection of an appropriate statistical model; predictor selection; evaluation of model performance using a calibration plot, Hosmer-Lemeshow test and c-index; internal and external validation; comparison of different models using c-index, net reclassification improvement, and integrated discrimination improvement; and a method to create an easy-to-use prediction score system will be addressed. This article may serve as a practical methodological reference for clinical researchers.