• Korean Journal of Radiology
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• v.24 no.10
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• pp.1006-1016
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• 2023

Objective: Computed tomography (CT) is an established method for the diagnosis, staging, and treatment of multiple myeloma. Here, we investigated the potential of photon-counting detector computed tomography (PCD-CT) in terms of image quality, diagnostic confidence, and radiation dose compared with energy-integrating detector CT (EID-CT). Materials and Methods: In this prospective study, patients with known multiple myeloma underwent clinically indicated whole-body PCD-CT. The image quality of PCD-CT was assessed qualitatively by three independent radiologists for overall image quality, edge sharpness, image noise, lesion conspicuity, and diagnostic confidence using a 5-point Likert scale (5 = excellent), and quantitatively for signal homogeneity using the coefficient of variation (CV) of Hounsfield Units (HU) values and modulation transfer function (MTF) via the full width at half maximum (FWHM) in the frequency space. The results were compared with those of the current clinical standard EID-CT protocols as controls. Additionally, the radiation dose (CTDIvol) was determined. Results: We enrolled 35 patients with multiple myeloma (mean age 69.8 ± 9.1 years; 18 [51%] males). Qualitative image analysis revealed superior scores (median [interquartile range]) for PCD-CT regarding overall image quality (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]), edge sharpness (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]), image noise (4.0 [4.0-4.0] vs. 3.0 [3.0-4.0]), lesion conspicuity (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]), and diagnostic confidence (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]) compared with EID-CT (P ≤ 0.004). In quantitative image analyses, PCD-CT compared with EID-CT revealed a substantially lower FWHM (2.89 vs. 25.68 cy/pixel) and a significantly more homogeneous signal (mean CV ± standard deviation [SD], 0.99 ± 0.65 vs. 1.66 ± 0.5; P < 0.001) at a significantly lower radiation dose (mean CTDIvol ± SD, 3.33 ± 0.82 vs. 7.19 ± 3.57 mGy; P < 0.001). Conclusion: Whole-body PCD-CT provides significantly higher subjective and objective image quality at significantly reduced radiation doses than the current clinical standard EID-CT protocols, along with readily available multi-spectral data, facilitating the potential for further advanced post-processing.

• IMMUNE NETWORK
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• v.21 no.4
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• pp.29.1-29.9
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• 2021

There are limited data directly comparing humoral and T cell responses to the ChAdOx1 nCoV-19 and BNT162b2 vaccines. We compared Ab and T cell responses after first doses of ChAdOx1 nCoV-19 vs. BNT162b2 vaccines. We enrolled healthcare workers who received ChAdOx1 nCoV-19 or BNT162b2 vaccine in Seoul, Korea. Anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) S1 protein-specific IgG Abs (S1-IgG), neutralizing Abs (NT Abs), and SARS-CoV-2-specific T cell response were evaluated before vaccination and at 1-wk intervals for 3 wks after vaccination. A total of 76 persons, comprising 40 injected with the ChAdOx1 vaccine and 36 injected with the BNT162b2 vaccine, participated in this study. At 3 wks after vaccination, the mean levels (±SD) of S1-IgG and NT Abs in the BNT162b2 participants were significantly higher than in the ChAdOx1 participants (S1-IgG, 14.03±7.20 vs. 6.28±8.87, p<0.0001; NT Ab, 183.1±155.6 vs. 116.6±116.2, p=0.035), respectively. However, the mean values of the T cell responses in the 2 groups were comparable after 2 wks. The humoral immune response after the 1st dose of BNT162b2 developed faster and was stronger than after the 1st dose of ChAdOx1. However, the T cell responses to BNT162b2 and ChAdOx1 were similar.

• IMMUNE NETWORK
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• v.22 no.3
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• pp.24.1-24.12
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• 2022

Coronavirus disease 2019 (COVID-19) vaccination in immunocompromised, especially transplant recipients, may induce a weaker immune response. But there are limited data on the immune response after COVID-19 vaccination in liver transplant (LT) recipients, especially on the comparison of Ab responses after different vaccine platforms between mRNA and adenoviral vector vaccines. Thus, we conducted a prospective study on LT recipients who received two doses of the ChAdOx1 nCoV-19 (ChAdOx1), mRNA-1273, or BNT162b2 vaccines compared with healthy healthcare workers (HCWs). SARS-CoV-2 S1-specific IgG Ab titers were measured using ELISA. Overall, 89 LT recipients (ChAdOx1, n=16 [18%]) or mRNA vaccines (mRNA-1273 vaccine, n=23 [26%]; BNT162b2 vaccine, n=50 [56%]) received 3 different vaccines. Of them, 16 (18%) had a positive Ab response after one dose of COVID-19 vaccine and 62 (73%) after 2 doses. However, the median Ab titer after two doses of mRNA vaccines was significantly higher (44.6 IU/ml) than after two doses of ChAdOx1 (19.2 IU/ml, p=0.04). The longer time interval from transplantation was significantly associated with high Ab titers after two doses of vaccine (p=0.003). However, mycophenolic acid use was not associated with Ab titers (p=0.53). In conclusion, about 3-quarters of LT recipients had a positive Ab response after 2 doses of vaccine, and the mRNA vaccines induced higher Ab responses than the ChAdOx1 vaccine.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.149-157
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• 2024

Despite continuous efforts by healthcare institutions and professionals, incidents threatening patient safety continue to occur. Policies related to patient safety are being strengthened, and nursing students are recognized as key personnel in patient safety management. Identifying factors influencing patient safety management behavior can enhance competency in patient safety management and prevent and improve patient safety incidents. Therefore, the purpose of this study is to clarify the impact of nursing students' knowledge, attitudes, and performance confidence related to patient safety management on their patient safety management behavior. A descriptive survey study was conducted, and data collection targeted 138 fourth-year nursing students in K region from October 25th to October 28th, 2022. Statistical analysis was performed using SPSS 25.0 program. The research findings showed that knowledge, attitudes, and confidence regarding patient safety management were positively correlated with patient safety management behavior. Factors influencing patient safety management behavior were identified as patient safety management education experience (β=.22, p<.001) and confidence (β=.66, p<.001). Based on these results, it is suggested that educational programs aimed at improving patient safety management behavior among nursing students should focus on enhancing patient safety management education experience and confidence.

• Korean Journal of Radiology
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• v.21 no.9
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• pp.1077-1086
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• 2020

Objective: To evaluate the effect of perfluorobutane microbubbles (Sonazoid^®, GE Healthcare) on steam popping during radiofrequency (RF) ablation for treating hepatocellular carcinoma (HCC), and to assess whether popping affects treatment outcomes. Materials and Methods: The institutional review board approved this retrospective study, which included 90 consecutive patients with single HCC, who received percutaneous RF ablation as the first-line treatment. The patients were divided into two groups, based on the presence or absence of the popping phenomenon, which was defined as an audible sound with a simultaneous sudden explosion within the ablation zone as detected via ultrasonography during the procedure. The factors contributing to the popping phenomenon were identified using multivariable logistic regression analysis. Local tumor progression (LTP) and disease-free survival (DFS) were assessed using the Kaplan-Meier method with the log-rank test for performing comparisons between the two groups. Results: The overall incidence of the popping phenomenon was 25.8% (24/93). Sonazoid^® was used in 1 patient (4.2%) in the popping group (n = 24), while it was used in 15 patients (21.7%) in the non-popping group (n = 69). Multivariable analysis revealed that the use of Sonazoid^® was the only significant factor for absence of the popping phenomenon (odds ratio = 0.10, p = 0.048). There were no significant differences in cumulative LTP and DFS between the two groups (p = 0.479 and p = 0.424, respectively). Conclusion: The use of Sonazoid^® has a suppressive effect on the popping phenomenon during RF ablation in patients with HCC. However, the presence of the popping phenomenon may not affect clinical outcomes.

• Korean Journal of Radiology
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• v.21 no.6
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• pp.670-683
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• 2020

Objective: The presence of coagulative necrosis (CN) in clear cell renal cell carcinoma (ccRCC) indicates a poor prognosis, while the absence of CN indicates a good prognosis. The purpose of this study was to build and validate a radiomics signature based on preoperative CT imaging data to estimate CN status in ccRCC. Materials and Methods: Altogether, 105 patients with pathologically confirmed ccRCC were retrospectively enrolled in this study and then divided into training (n = 72) and validation (n = 33) sets. Thereafter, 385 radiomics features were extracted from the three-dimensional volumes of interest of each tumor, and 10 traditional features were assessed by two experienced radiologists using triple-phase CT-enhanced images. A multivariate logistic regression algorithm was used to build the radiomics score and traditional predictors in the training set, and their performance was assessed and then tested in the validation set. The radiomics signature to distinguish CN status was then developed by incorporating the radiomics score and the selected traditional predictors. The receiver operating characteristic (ROC) curve was plotted to evaluate the predictive performance. Results: The area under the ROC curve (AUC) of the radiomics score, which consisted of 7 radiomics features, was 0.855 in the training set and 0.885 in the validation set. The AUC of the traditional predictor, which consisted of 2 traditional features, was 0.843 in the training set and 0.858 in the validation set. The radiomics signature showed the best performance with an AUC of 0.942 in the training set, which was then confirmed with an AUC of 0.969 in the validation set. Conclusion: The CT-based radiomics signature that incorporated radiomics and traditional features has the potential to be used as a non-invasive tool for preoperative prediction of CN in ccRCC.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.819-825
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• 2024

The reason for recording dose data when using a diagnostic radiation source is to record and manage the dose to healthcare personnel and patients. The purpose of this study was to verify the difference in radiation dose when using diagnostic radiation generating devices and to inform users' awareness of dose reduction through measurement and analysis of dose in situations with and without shielding. The dose analysis of each equipment for two Korean C-arms and two German C-arms showed that the Korean FPD type C-arm had the highest dose value, followed by the German I.I type C-arm, German FPD type C-arm, Korean, and I.I type C-arm. The results of the dose analysis with and without shielding showed that the dose to the human phantom in a normal atmosphere increased by about 2 times due to scattered radiation, but the dose to the human phantom was reduced by about 5 times by wearing a shield (0.5mm/lead apron). More important than the management of radiation dose is the study of how to reduce exposure when using radiation, and since the radiation dose output from different equipment is different, it is necessary to provide dose information with and without shielding.

• Therapeutic Science for Rehabilitation
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• v.13 no.2
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• pp.69-84
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• 2024

Objective : This study aimed to categorize the lifestyles of older adults into two types - healthy and unhealthy, and use machine learning to identify the personal values that influence these lifestyles. Methods : This cross-sectional study targeting middle-aged and older adults (55 years and above) living in local communities in South Korea. Data were collected from 300 participants through online surveys. Lifestyle types were dichotomized by the Yonsei Lifestyle Profile (YLP)-Active, Balanced, Connected, and Diverse (ABCD) responses using latent profile analysis. Personal value information was collected using YLP-Values (YLP-V) and analyzed using machine learning to identify the relative importance of personal values on lifestyle types. Results : The lifestyle of older adults was categorized into healthy (48.87%) and unhealthy (51.13%). These two types showed the most significant difference in social relationship characteristics. Among the machine learning models used in this study, the support vector machine showed the highest classification performance, achieving 96% accuracy and 95% area under the receiver operating characteristic (ROC) curve. The model indicated that individuals who prioritized a healthy diet, sought health information, and engaged in hobbies or cultural activities were more likely to have a healthy lifestyle. Conclusion : This study suggests the need to encourage the expansion of social networks among older adults. Furthermore, it highlights the necessity to comprehensively intervene in individuals' perceptions and values that primarily influence lifestyle adherence.

• Annals of Occupational and Environmental Medicine
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• v.34
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• pp.40.1-40.9
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• 2022

Background: According to the occupational accident status analysis in 2020, of 1,180 occupational deaths, 463 were caused by cardiovascular disease (CVD). Workers should be assessed for CVD risk at regular intervals to prevent work-related CVD in accordance with the rules on occupational safety and health standards. However, no previous study has addressed risk and mortality. Therefore, this longitudinal study was conducted to evaluate the relationship between 10-year cardiovascular risk of the general health checkup and mortality. Methods: The study included 545,859 participants who visited Kangbuk Samsung Total Healthcare Centers from January 1, 2002, to December 31, 2017. We performed 10-year cardiovascular risk assessment for the participants and the risk was divided into 4 groups (low, moderate, high, and very high). The study used death data from the Korea National Statistical Office for survival status as an outcome variable by December 31, 2019, and the cause of death based on the International Classification of Diseases, 10^th Revision (ICD-10) was identified. Statistical analysis was performed using Cox proportional hazards regression analysis, and the sum of the periods from the first visit to the date of death or December 31, 2019, was used as a time scale. We also performed a stratified analysis for age at baseline and sex. Results: During 5,253,627.9 person-years, 4,738 overall deaths and 654 cardiovascular deaths occurred. When the low-risk group was set as a reference, in the multivariable-adjusted model, the hazard ratios (HRs) (95% confidence interval [CI]) for overall mortality were 3.36 (2.87-3.95) in the moderate-risk group, 11.08 (9.27-13.25) in the high-risk group, and 21.20 (17.42-25.79) in the very-high-risk group, all of which were statistically significant. In cardiovascular deaths, the difference according to the risk classification was more pronounced. The HRs (95% CI) were 8.57 (4.95-14.83), 38.95 (21.77-69.69), and 78.81 (42.62-145.71) in each group. As a result of a subgroup analysis by age and sex, the HRs of all-cause mortality and cardiovascular mortality tended to be higher in the high-risk group. Conclusions: This large-scale longitudinal study confirmed that the risk of death increases with the 10-year cardiovascular risk of general health checkup.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.