• Journal of the Korean Society for Library and Information Science
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• v.57 no.2
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• pp.435-452
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• 2023

This study experimented with automatic classification of subject headings using BERT-based transfer learning model, and analyzed its performance. This study analyzed the classification performance according to the main class of KDC classification and the category type of subject headings. Six datasets were constructed from Korean national bibliographies based on the frequency of the assignments of subject headings, and titles were used as classification features. As a result, classification performance showed values of 0.6059 and 0.5626 on the micro F1 and macro F1 score, respectively, in the dataset (1,539,076 records) containing 3,506 subject headings. In addition, classification performance by the main class of KDC classification showed good performance in the class General works, Natural science, Technology and Language, and low performance in Religion and Arts. As for the performance by the category type of the subject headings, the categories of plant, legal name and product name showed high performance, whereas national treasure/treasure category showed low performance. In a large dataset, the ratio of subject headings that cannot be assigned increases, resulting in a decrease in final performance, and improvement is needed to increase classification performance for low-frequency subject headings.

• Korean Journal of Radiology
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• v.21 no.1
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• pp.58-67
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• 2020

Objective: Third-generation dual-source computed tomography (3rd-DSCT) allows dynamic myocardial CT perfusion imaging (dynamic CTP) with a 10.5-cm z-axis coverage. Although the increased radiation exposure associated with the 50% wider scan range compared to second-generation DSCT (2nd-DSCT) may be suppressed by using a tube voltage of 70 kV, it remains unclear whether image quality and the ability to quantify myocardial blood flow (MBF) can be maintained under these conditions. This study aimed to compare the image quality, estimated MBF, and radiation dose of dynamic CTP between 2ndDSCT and 3rd-DSCT and to evaluate whether a 10.5-cm coverage is suitable for dynamic CTP. Materials and Methods: We retrospectively analyzed 107 patients who underwent dynamic CTP using 2nd-DSCT at 80 kV (n = 54) or 3rd-DSCT at 70 kV (n = 53). Image quality, estimated MBF, radiation dose, and coverage of left ventricular (LV) myocardium were compared. Results: No significant differences were observed between 3rd-DSCT and 2nd-DSCT in contrast-to-noise ratio (37.4 ± 11.4 vs. 35.5 ± 11.2, p = 0.396). Effective radiation dose was lower with 3rd-DSCT (3.97 ± 0.92 mSv with a conversion factor of 0.017 mSv/mGy∙cm) compared to 2nd-DSCT (5.49 ± 1.36 mSv, p < 0.001). Incomplete coverage was more frequent with 2nd-DSCT than with 3rd-DSCT (1.9% [1/53] vs. 56% [30/54], p < 0.001). In propensity score-matched cohorts, MBF was comparable between 3rd-DSCT and 2nd-DSCT in non-ischemic (146.2 ± 26.5 vs. 157.5 ± 34.9 mL/min/100 g, p = 0.137) as well as ischemic myocardium (92.7 ± 21.1 vs. 90.9 ± 29.7 mL/min/100 g, p = 0.876). Conclusion: The radiation increase inherent to the widened z-axis coverage in 3rd-DSCT can be balanced by using a tube voltage of 70 kV without compromising image quality or MBF quantification. In dynamic CTP, a z-axis coverage of 10.5 cm is sufficient to achieve complete coverage of the LV myocardium in most patients.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1213-1224
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• 2021

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

• Korean Journal of Radiology
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• v.21 no.5
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• pp.582-587
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• 2020

Objective: Endovascular thrombectomy (EVT) fails in approximately 20% of anterior circulation large vessel occlusion (AC-LVO). Nonetheless, the factors that affect clinical outcomes of non-recanalized AC-LVO despite EVT are less studied. The purpose of this study was to identify the factors affecting clinical outcomes in non-recanalized AC-LVO patients despite EVT. Materials and Methods: This was a retrospective analysis of clinical and imaging data from 136 consecutive patients who demonstrated recanalization failure (modified thrombolysis in cerebral ischemia [mTICI], 0-2a) despite EVT for AC-LVO. Data were collected in prospectively maintained registries at 16 stroke centers. Collateral status was categorized into good or poor based on the CT angiogram, and the mTICI was categorized as 0-1 or 2a on the final angiogram. Patients with good (modified Rankin Scale [mRS], 0-2) and poor outcomes (mRS, 3-6) were compared in multivariate analysis to evaluate the factors associated with a good outcome. Results: Thirty-five patients (25.7%) had good outcomes. The good outcome group was younger (odds ratio [OR], 0.962; 95% confidence interval [CI], 0.932-0.992; p = 0.015), had a lower incidence of hypertension (OR, 0.380; 95% CI, 0.173-0.839; p = 0.017) and distal internal carotid artery involvement (OR, 0.149; 95% CI, 0.043-0.520; p = 0.003), lower initial National Institute of Health Stroke Scale (NIHSS) (OR, 0.789; 95% CI, 0.713-0.873; p < 0.001) and good collateral status (OR, 13.818; 95% CI, 3.971-48.090; p < 0.001). In multivariate analysis, the initial NIHSS (OR, 0.760; 95% CI, 0.638-0.905; p = 0.002), good collateral status (OR, 14.130; 95% CI, 2.264-88.212; p = 0.005) and mTICI 2a recanalization (OR, 5.636; 95% CI, 1.216-26.119; p = 0.027) remained as independent factors with good outcome in non-recanalized patients. Conclusion: Baseline NIHSS score, good collateral status, and mTICI 2a recanalization remained independently associated with clinical outcome in non-recanalized patients. mTICI 2a recanalization would benefit patients with good collaterals in non-recanalized AC-LVO patients despite EVT.

• Journal of the Korean earth science society
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• v.45 no.2
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• pp.111-120
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• 2024

Cloud coverage is a key factor in determining whether to proceed with observations. In the past, human judgment played an important role in weather evaluation for observations. However, the development of remote and robotic observation has diminished the role of human judgment. Moreover, it is not easy to evaluate weather conditions automatically because of the diverse cloud shapes and their rapid movement. In this paper, we present the development of a cloud monitoring program by applying a machine learning-based Python module "cloudynight" on all-sky camera images obtained at Miryang Arirang Astronomical Observatory (MAAO). The machine learning model was built by training 39,996 subregions divided from 1,212 images with altitude/azimuth angles and extracting 16 feature spaces. For our training model, the F1-score from the validation samples was 0.97, indicating good performance in identifying clouds in the all-sky image. As a result, this program calculates "Cloudiness" as the ratio of the number of total subregions to the number of subregions predicted to be covered by clouds. In the robotic observation, we set a policy that allows the telescope system to halt the observation when the "Cloudiness" exceeds 0.6 during the last 30 minutes. Following this policy, we found that there were no improper halts in the telescope system due to incorrect program decisions. We expect that robotic observation with the 0.7 m telescope at MAAO can be successfully operated using the cloud monitoring program.

• Information Systems Review
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• v.25 no.1
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• pp.213-231
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• 2023

With the rapid development of live streaming commerce, online host as an information source plays a critical role in affecting live streaming performance. However, the impact of different product types on the relationship between online hosts and live streaming has been less studied. Based on the elaboration likelihood model (ELM) and information source theory, this study aims to empirically investigate what factors influence the sales of live streaming commerce and how product type moderates the relationship between them. The analysis of 11,422 live streaming commerce data collected for four months from October 10, 2021 to February 10, 2022 shows that, among the factors related to source credibility and attractiveness, multi-channel networks (MCN) and the number of followers positively affect the sales volume of live streaming commerce, whereas the reputation score harms the sales. Moreover, the moderating effect of the product type (i.e., ratio of involvement products) on the relationships is confirmed. The findings enrich the literature on live streaming commerce performance. The limitations and future research directions are also discussed.

• Korean Journal of Radiology
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• v.25 no.2
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• pp.189-198
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• 2024

Objective: To investigate the prognostic utility of radiomics features extracted from ¹⁸F-fluorodeoxyglucose (FDG) PET/CT combined with clinical factors and metabolic parameters in predicting progression-free survival (PFS) and overall survival (OS) in individuals diagnosed with extranodal nasal-type NK/T cell lymphoma (ENKTCL). Materials and Methods: A total of 126 adults with ENKTCL who underwent ¹⁸F-FDG PET/CT examination before treatment were retrospectively included and randomly divided into training (n = 88) and validation cohorts (n = 38) at a ratio of 7:3. Least absolute shrinkage and selection operation Cox regression analysis was used to select the best radiomics features and calculate each patient's radiomics scores (RadPFS and RadOS). Kaplan-Meier curve and Log-rank test were used to compare survival between patient groups risk-stratified by the radiomics scores. Various models to predict PFS and OS were constructed, including clinical, metabolic, clinical + metabolic, and clinical + metabolic + radiomics models. The discriminative ability of each model was evaluated using Harrell's C index. The performance of each model in predicting PFS and OS for 1-, 3-, and 5-years was evaluated using the time-dependent receiver operating characteristic (ROC) curve. Results: Kaplan-Meier curve analysis demonstrated that the radiomics scores effectively identified high- and low-risk patients (all P < 0.05). Multivariable Cox analysis showed that the Ann Arbor stage, maximum standardized uptake value (SUVmax), and RadPFS were independent risk factors associated with PFS. Further, β2-microglobulin, Eastern Cooperative Oncology Group performance status score, SUVmax, and RadOS were independent risk factors for OS. The clinical + metabolic + radiomics model exhibited the greatest discriminative ability for both PFS (Harrell's C-index: 0.805 in the validation cohort) and OS (Harrell's C-index: 0.833 in the validation cohort). The time-dependent ROC analysis indicated that the clinical + metabolic + radiomics model had the best predictive performance. Conclusion: The PET/CT-based clinical + metabolic + radiomics model can enhance prognostication among patients with ENKTCL and may be a non-invasive and efficient risk stratification tool for clinical practice.

• Korean Journal of Radiology
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• v.24 no.4
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• pp.338-348
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• 2023

Objective: Patients with a history of ischemic stroke are at risk for a second ischemic stroke. This study aimed to investigate the relationship between carotid plaque enhancement on perfluorobutane microbubble contrast-enhanced ultrasonography (CEUS) and future recurrent stroke, and to determine whether plaque enhancement can contribute to risk assessment for recurrent stroke compared with the Essen Stroke Risk Score (ESRS). Materials and Methods: This prospective study screened 151 patients with recent ischemic stroke and carotid atherosclerotic plaques at our hospital between August 2020 and December 2020. A total of 149 eligible patients underwent carotid CEUS, and 130 patients who were followed up for 15-27 months or until stroke recurrence were analyzed. Plaque enhancement on CEUS was investigated as a possible risk factor for stroke recurrence and as a possible adjunct to ESRS. Results: During follow-up, 25 patients (19.2%) experienced recurrent stroke. Patients with plaque enhancement on CEUS had an increased risk of stroke recurrence events (22/73, 30.1%) compared to those without plaque enhancement (3/57, 5.3%), with an adjusted hazard ratio (HR) of 38.264 (95% confidence interval [CI]:14.975-97.767; P < 0.001) according to a multivariable Cox proportional hazards model analysis, indicating that the presence of carotid plaque enhancement was a significant independent predictor of recurrent stroke. When plaque enhancement was added to the ESRS, the HR for stroke recurrence in the high-risk group compared to that in the low-risk group (2.188; 95% CI, 0.025-3.388) was greater than that of the ESRS alone (1.706; 95% CI, 0.810-9.014). A net of 32.0% of the recurrence group was reclassified upward appropriately by the addition of plaque enhancement to the ESRS. Conclusion: Carotid plaque enhancement was a significant and independent predictor of stroke recurrence in patients with ischemic stroke. Furthermore, the addition of plaque enhancement improved the risk stratification capability of the ESRS.

• Korean Journal of Radiology
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• v.24 no.5
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• pp.465-475
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• 2023

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.

• Korean Journal of Radiology
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• v.23 no.7
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• pp.720-731
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• 2022

Objective: We aimed to develop and test a deep learning algorithm (DLA) for fully automated measurement of the volume and signal intensity (SI) of the liver and spleen using gadoxetic acid-enhanced hepatobiliary phase (HBP)-magnetic resonance imaging (MRI) and to evaluate the clinical utility of DLA-assisted assessment of functional liver capacity. Materials and Methods: The DLA was developed using HBP-MRI data from 1014 patients. Using an independent test dataset (110 internal and 90 external MRI data), the segmentation performance of the DLA was measured using the Dice similarity score (DSS), and the agreement between the DLA and the ground truth for the volume and SI measurements was assessed with a Bland-Altman 95% limit of agreement (LOA). In 276 separate patients (male:female, 191:85; mean age ± standard deviation, 40 ± 15 years) who underwent hepatic resection, we evaluated the correlations between various DLA-based MRI indices, including liver volume normalized by body surface area (LV_BSA), liver-to-spleen SI ratio (LSSR), MRI parameter-adjusted LSSR (aLSSR), LSSR × LV_BSA, and aLSSR × LV_BSA, and the indocyanine green retention rate at 15 minutes (ICG-R15), and determined the diagnostic performance of the DLA-based MRI indices to detect ICG-R15 ≥ 20%. Results: In the test dataset, the mean DSS was 0.977 for liver segmentation and 0.946 for spleen segmentation. The Bland-Altman 95% LOAs were 0.08% ± 3.70% for the liver volume, 0.20% ± 7.89% for the spleen volume, -0.02% ± 1.28% for the liver SI, and -0.01% ± 1.70% for the spleen SI. Among DLA-based MRI indices, aLSSR × LV_BSA showed the strongest correlation with ICG-R15 (r = -0.54, p < 0.001), with area under receiver operating characteristic curve of 0.932 (95% confidence interval, 0.895-0.959) to diagnose ICG-R15 ≥ 20%. Conclusion: Our DLA can accurately measure the volume and SI of the liver and spleen and may be useful for assessing functional liver capacity using gadoxetic acid-enhanced HBP-MRI.