• Korean Journal of Radiology
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• v.23 no.12
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• pp.1241-1250
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• 2022

Objective: To conduct a simulation study to determine whether artificial intelligence (AI)-aided mammography reading can reduce unnecessary recalls while maintaining cancer detection ability in women recalled after mammography screening. Materials and Methods: A retrospective reader study was performed by screening mammographies of 793 women (mean age ± standard deviation, 50 ± 9 years) recalled to obtain supplemental mammographic views regarding screening mammography-detected abnormalities between January 2016 and December 2019 at two screening centers. Initial screening mammography examinations were interpreted by three dedicated breast radiologists sequentially, case by case, with and without AI aid, in a single session. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and recall rate for breast cancer diagnosis were obtained and compared between the two reading modes. Results: Fifty-four mammograms with cancer (35 invasive cancers and 19 ductal carcinomas in situ) and 739 mammograms with benign or negative findings were included. The reader-averaged AUC improved after AI aid, from 0.79 (95% confidence interval [CI], 0.74-0.85) to 0.89 (95% CI, 0.85-0.94) (p < 0.001). The reader-averaged specificities before and after AI aid were 41.9% (95% CI, 39.3%-44.5%) and 53.9% (95% CI, 50.9%-56.9%), respectively (p < 0.001). The reader-averaged sensitivity was not statistically different between AI-unaided and AI-aided readings: 89.5% (95% CI, 83.1%-95.9%) vs. 92.6% (95% CI, 86.2%-99.0%) (p = 0.053), although the sensitivities of the least experienced radiologists before and after AI aid were 79.6% (43 of 54 [95% CI, 66.5%-89.4%]) and 90.7% (49 of 54 [95% CI, 79.7%-96.9%]), respectively (p = 0.031). With AI aid, the reader-averaged recall rate decreased by from 60.4% (95% CI, 57.8%-62.9%) to 49.5% (95% CI, 46.5%-52.4%) (p < 0.001). Conclusion: AI-aided reading reduced the number of recalls and improved the diagnostic performance in our simulation using women initially recalled for supplemental mammographic views after mammography screening.

• Korean Journal of Radiology
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• v.23 no.12
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• pp.1251-1259
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• 2022

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.

• Korean Journal of Radiology
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• v.23 no.12
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• pp.1281-1289
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• 2022

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.

• Korean Journal of Radiology
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• v.23 no.11
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• pp.1102-1111
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• 2022

Objective: To evaluate the ultrasonography (US) features for diagnosing metastasis in cervical lymph nodes (LNs) in patients with thyroid cancer and compare the US classification of risk of LN metastasis between European and Korean guidelines. Materials and Methods: From January 2014 to December 2018, US-guided fine-needle aspiration was performed on 836 LNs from 714 patients for the preoperative nodal staging of thyroid cancer. The US features of LNs were retrospectively reviewed for the following features: size, presence of hilum, margin, orientation, cystic change, punctate echogenic foci (PEF), large echogenic foci, eccentric cortical thickening, abnormal vascularity, and cortical hyperechogenicity. A multiple logistic regression analysis was performed to identify the independent US features for the diagnosis of metastatic LNs. The diagnostic performance of independent US features was subsequently evaluated. LNs were categorized according to the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) and European Thyroid Association (ETA) guidelines, and the correlation between the two sets of classifications was assessed. Results: Absence of the hilum, presence of cystic changes, PEF, abnormal vascularity, and cortical hyperechogenicity were independent US features of metastatic LNs. Cystic changes, PEF, abnormal vascularity, and cortical hyperechogenicity showed high specificity (86.8%-99.6%). The absence of the hilum had the highest sensitivity yet low specificity (66.4%). When LNs were classified according to the ETA guidelines and K-TIRADS, they yielded similar categorizations of malignancy risks and were strongly correlated (Spearman coefficient, 0.9766 [95% confidence interval, 0.973-0.979]). According to the ETA guidelines, 9.8% (82/836) of LNs were classified as "not specified." Conclusion: Absence of hilum, cystic changes, PEF, abnormal vascularity, and cortical hyperechogenicity were independent US features suggestive of metastatic LNs in thyroid cancer. Both K-TIRADS and the ETA guidelines provided similar risk stratification for metastatic LNs with a high correlation; however, the ETA guidelines failed to classify 9.8% of LNs into a specific risk stratum. These results may provide a basis for revising LN classification in future guidelines.

• Korean Journal of Radiology
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• v.24 no.11
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• pp.1142-1150
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• 2023

Objective: To evaluate ^99mtechnetium-three polyethylene glycol spacers-arginine-glycine-aspartic acid (^99mTc-3PRGD₂) single-photon emission computed tomography (SPECT)/computed tomography (CT) imaging for diagnosing lymph node metastasis of primary malignant lung neoplasms. Materials and Methods: We prospectively enrolled 26 patients with primary malignant lung tumors who underwent ^99mTc-3PRGD₂ SPECT/CT and ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET)/CT imaging. Both imaging methods were analyzed in qualitative (visual dichotomous and 5-point grades for lymph nodes and lung tumors, respectively) and semiquantitative (maximum tissue-to-background radioactive count) manners for the lymph nodes and lung tumors. The performance of the differentiation of lymph nodes with and without metastasis was determined at the per-lymph node station and per-patient levels using histopathological results as the reference standard. Results: Total 42 stations had metastatic lymph nodes and 136 stations had benign lymph nodes. The differences between metastatic and benign lymph nodes in the visual qualitative and semiquantitative analyses of ^99mTc-3PRGD₂ SPECT/CT and ¹⁸F-FDG PET/CT were statistically significant (all P < 0.001). The area under the receiver operating characteristic curve (AUC) in the semi-quantitative analysis of ^99mTc-3PRGD₂ SPECT/CT was 0.908 (95% confidence interval [CI], 0.851-0.966), and the sensitivity, specificity, positive predictive value, and negative predictive value were 0.86 (36/42), 0.88 (120/136), 0.69 (36/52), and 0.95 (120/126), respectively. Among the 26 patients (including two patients each with two lung tumors), 15 had pathologically confirmed lymph node metastasis. The difference between primary lung lesions in patients with and without lymph node metastasis was statistically significant only in the semi-quantitative analysis of ^99mTc-3PRGD₂ SPECT/CT (P = 0.007), with an AUC of 0.807 (95% CI, 0.641-0.974). Conclusion: ^99mTc-3PRGD₂ SPECT/CT imaging may notably perform in the direct diagnosis of lymph node metastasis of primary malignant lung tumors and indirectly predict the presence of lymph node metastasis through uptake in the primary lesions.

• Korean Journal of Radiology
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• v.20 no.5
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• pp.759-772
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• 2019

Objective: To investigate the value of ultrasound (US) microflow assessment in distinguishing malignant from benign solid breast masses as well as the association between US parameters and histologic microvessel density (MVD). Materials and Methods: Ninety-eight breast masses (57 benign and 41 malignant) were examined using Superb Microvascular Imaging (SMI) and contrast-enhanced US (CEUS) before biopsy. Two radiologists evaluated the quantitative and qualitative vascular parameters on SMI (vascular index, morphology, distribution, and penetration) and CEUS (time-intensity curve analysis and enhancement characteristics). US parameters were compared between benign and malignant masses and the diagnostic performance was compared between SMI and CEUS. Subgroup analysis was performed according to lesion size. The effect of vascular parameters on downgrading Breast Imaging Reporting and Data System (BI-RADS) category 4A masses was evaluated. The association between histologic MVD and US parameters was analyzed. Results: Malignant masses were associated with a higher vascular index (15.1 ± 7.3 vs. 5.9 ± 5.6), complex vessel morphology (82.9% vs. 42.1%), central vascularity (95.1% vs. 59.6%), penetrating vessels (80.5% vs. 31.6%) on SMI (all, p < 0.001), as well as higher peak intensity (37.1 ± 25.7 vs. 17.0 ± 15.8, p < 0.001), slope (10.6 ± 11.2 vs. 3.9 ± 4.2, p = 0.001), area (1035.7 ± 726.9 vs. 458.2 ± 410.2, p < 0.001), hyperenhancement (95.1% vs. 70.2%, p = 0.005), centripetal enhancement (70.7% vs. 45.6%, p = 0.023), penetrating vessels (65.9% vs. 22.8%, p < 0.001), and perfusion defects (31.7% vs. 3.5%, p < 0.001) on CEUS (p ≤ 0.023). The areas under the receiver operating characteristic curve (AUCs) of SMI and CEUS were 0.853 and 0.841, respectively (p = 0.803). In 19 masses measuring < 10 mm, central vascularity on SMI was associated with malignancy (100% vs. 38.5%, p = 0.018). Considering all benign SMI parameters on the BI-RADS assessment, unnecessary biopsies could be avoided in 12 category 4A masses with improved AUCs (0.500 vs. 0.605, p < 0.001). US vascular parameters associated with malignancy showed higher MVD (p ≤ 0.016). MVD was higher in malignant masses than in benign masses, and malignant masses negative for estrogen receptor or positive for Ki67 had higher MVD (p < 0.05). Conclusion: US microflow assessment using SMI and CEUS is valuable in distinguishing malignant from benign solid breast masses, and US vascular parameters are associated with histologic MVD.

• Korean Journal of Radiology
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• v.20 no.5
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• pp.719-728
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• 2019

Objective: To investigate the diagnostic validity of coronary computed tomography angiography (cCTA) in vasospastic angina (VA) and factors associated with discrepant results between invasive coronary angiography with the ergonovine provocation test (iCAG-EPT) and cCTA. Materials and Methods: Of the 1397 patients diagnosed with VA from 2006 to 2016, 33 patients (75 lesions) with available cCTA data from within 6 months before iCAG-EPT were included. The severity of spasm (% diameter stenosis [%DS]) on iCAGEPT and cCTA was assessed, and the difference in %DS (Δ%DS) was calculated. Δ%DS was compared after classifying the lesions according to pre-cCTA-administered sublingual nitroglycerin (SL-NG) or beta-blockers. The lesions were further categorized with %DS ≥ 50% on iCAG-EPT or cCTA defined as a significant spasm, and the diagnostic performance of cCTA on identifying significant spasm relative to iCAG-EPT was assessed. Results: Compared to lesions without SL-NG treatment, those with SL-NG treatment showed a higher Δ%DS (39.2% vs. 22.1%, p = 0.002). However, there was no difference in Δ%DS with or without beta-blocker treatment (35.1% vs. 32.6%, p = 0.643). The significant difference in Δ%DS associated with SL-NG was more prominent in patients who were aged < 60 years, were male, had body mass index < 25 kg/m², and had no history of hypertension, diabetes, or dyslipidemia. Based on iCAG-EPT as the reference, the per-lesion-based sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of cCTA for VA diagnosis were 7.5%, 94.0%, 60.0%, 47.1%, and 48.0%, respectively. Conclusion: For patients with clinically suspected VA, confirmation with iCAG-EPT needs to be considered without completely excluding the diagnosis of VA simply based on cCTA results, although further prospective studies are required for confirmation.

• Korean Journal of Radiology
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• v.20 no.8
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• pp.1275-1284
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• 2019

Objective: To develop algorithms using convolutional neural networks (CNNs) for automatic segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) and compare them with conventional algorithms, including a thresholding-based segmentation. Materials and Methods: Between September 2005 and August 2015, 429 patients presenting with acute cerebral ischemia (training:validation:test set = 246:89:94) were retrospectively enrolled in this study, which was performed under Institutional Review Board approval. Ground truth segmentations for acute ischemic lesions on DWI were manually drawn under the consensus of two expert radiologists. CNN algorithms were developed using two-dimensional U-Net with squeeze-and-excitation blocks (U-Net) and a DenseNet with squeeze-and-excitation blocks (DenseNet) with squeeze-and-excitation operations for automatic segmentation of acute ischemic lesions on DWI. The CNN algorithms were compared with conventional algorithms based on DWI and the apparent diffusion coefficient (ADC) signal intensity. The performances of the algorithms were assessed using the Dice index with 5-fold cross-validation. The Dice indices were analyzed according to infarct volumes (< 10 mL, ≥ 10 mL), number of infarcts (≤ 5, 6-10, ≥ 11), and b-value of 1000 (b1000) signal intensities (< 50, 50-100, > 100), time intervals to DWI, and DWI protocols. Results: The CNN algorithms were significantly superior to conventional algorithms (p < 0.001). Dice indices for the CNN algorithms were 0.85 for U-Net and DenseNet and 0.86 for an ensemble of U-Net and DenseNet, while the indices were 0.58 for ADC-b1000 and b1000-ADC and 0.52 for the commercial ADC algorithm. The Dice indices for small and large lesions, respectively, were 0.81 and 0.88 with U-Net, 0.80 and 0.88 with DenseNet, and 0.82 and 0.89 with the ensemble of U-Net and DenseNet. The CNN algorithms showed significant differences in Dice indices according to infarct volumes (p < 0.001). Conclusion: The CNN algorithm for automatic segmentation of acute ischemic lesions on DWI achieved Dice indices greater than or equal to 0.85 and showed superior performance to conventional algorithms.

• Korean Journal of Radiology
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• v.20 no.6
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• pp.916-930
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• 2019

Objective: To investigate the relationships of T₂^*-corrected 6-echo Dixon volumetric interpolated breath-hold examination (VIBE) imaging-based fat fraction (FF) and R₂^* values with bone mineral density (BMD); determine their associations with sex, age, and menopause; and evaluate the diagnostic performance of the FF and R₂^* for predicting osteopenia and osteoporosis. Materials and Methods: This study included 153 subjects who had undergone magnetic resonance (MR) imaging, including MR spectroscopy (MRS) and T₂^*-corrected 6-echo Dixon VIBE imaging. The FF and R₂^* were measured at the L4 vertebra. The male and female groups were divided into two subgroups according to age or menopause. Lin's concordance and Pearson's correlation coefficients, Bland-Altman 95% limits of agreement, and the area under the curve (AUC) were calculated. Results: The correlation between the spectroscopic and 6-echo Dixon VIBE imaging-based FF values was statistically significant for both readers (p_c = 0.940 [reader 1], 0.908 [reader 2]; both p < 0.001). A small measurement bias was observed for the MRS-based FF for both readers (mean difference = -0.3% [reader 1], 0.1% [reader 2]). We found a moderate negative correlation between BMD and the FF (r = -0.411 [reader 1], -0.436 [reader 2]; both p <0.001) with younger men and premenopausal women showing higher correlations. R₂^* and BMD were more significantly correlated in women than in men, and the highest correlation was observed in postmenopausal women (r = 0.626 [reader 1], 0.644 [reader 2]; both p < 0.001). For predicting osteopenia and osteoporosis, the FF had a higher AUC in men and R₂^* had a higher AUC in women. The AUC for predicting osteoporosis was highest with a combination of the FF and R₂^* in postmenopausal women (AUC = 0.872 [reader 1], 0.867 [reader 2]; both p < 0.001). Conclusion: The FF and R₂^* measured using T₂^*-corrected 6-echo Dixon VIBE imaging can serve as predictors of osteopenia and osteoporosis. R₂^* might be useful for predicting osteoporosis, especially in postmenopausal women.

• Korean Journal of Radiology
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• v.20 no.5
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• pp.791-800
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• 2019

Objective: To compare various models of diffusion-weighted imaging including monoexponential apparent diffusion coefficient (ADC), biexponential (fast diffusion coefficient [D_f], slow diffusion coefficient [D_s], and fraction of fast diffusion), stretched-exponential (distributed diffusion coefficient and anomalous exponent term [α]), and kurtosis (mean diffusivity and mean kurtosis [MK]) models in the differentiation of renal solid masses. Materials and Methods: A total of 81 patients (56 men and 25 women; mean age, 57 years; age range, 30-69 years) with 18 benign and 63 malignant lesions were imaged using 3T diffusion-weighted MRI. Diffusion model selection was investigated in each lesion using the Akaike information criteria. Mann-Whitney U test and receiver operating characteristic (ROC) analysis were used for statistical evaluations. Results: Goodness-of-fit analysis showed that the stretched-exponential model had the highest voxel percentages in benign and malignant lesions (90.7% and 51.4%, respectively). ADC, D_s, and MK showed significant differences between benign and malignant lesions (p < 0.05) and between low- and high-grade clear cell renal cell carcinoma (ccRCC) (p < 0.05). α was significantly lower in the benign group than in the malignant group (p < 0.05). All diffusion measures showed significant differences between ccRCC and non-ccRCC (p < 0.05) except D_f and α (p = 0.143 and 0.112, respectively). α showed the highest diagnostic accuracy in differentiating benign and malignant lesions with an area under the ROC curve of 0.923, but none of the parameters from these advanced models revealed significantly better performance over ADC in discriminating subtypes or grades of renal cell carcinoma (RCC) (p > 0.05). Conclusion: Compared with conventional diffusion parameters, α may provide additional information for differentiating benign and malignant renal masses, while ADC remains the most valuable parameter for differentiation of RCC subtypes and for ccRCC grading.