Objective: To evaluate the clinical efficacy of ultrafast dynamic contrast-enhanced (DCE)-MRI using a compressed sensing (CS) technique for differentiating benign and malignant soft-tissue tumors (STTs) and to evaluate the factors related to the grading of malignant STTs. Materials and Methods: A total of 165 patients (96 male; mean age, 61 years), comprising 111 with malignant STTs and 54 with benign STTs according to the 2020 WHO classification, underwent DCE-MRI with CS between June 2018 and June 2023. The clinical, qualitative, and quantitative parameters associated with conventional MRI were also obtained. During post-processing of the early arterial phase of DCE-MRI, the time-to-enhance (TTE), time-to-peak (TTP), initial area under the curve at 60 s (iAUC60), and maximum slope were calculated. Furthermore, the delayed arterial phase parameters of DCEMRI, including Ktrans , Kep, Ve, and iAUC values and time-concentration curve (TCC) types, were determined. Clinical and MRI parameters were statistically analyzed to differentiate between benign and malignant tumors and their correlation with tumor grading. Results: According to logistic regression analysis, the TTE value (P < 0.001) of the early arterial phase and Ve (P = 0.039) and iAUC (P = 0.006) values of the delayed arterial phase, as well as age, location, peritumoral edema, and contrast heterogeneity on conventional MRI, were significant (P = 0.001–0.015) in differentiating benign and malignant tumors. Among all the quantitative parameters, the TTE value had the highest accuracy, with an area under the receiver operating characteristic curve of 0.902. The grading of malignant tumors was significantly correlated with peritumoral edema; CE heterogeneity; visual diffusion restriction; minimum and mean ADC; TTP, Kep, and Ve values; and the TCC graph (all P < 0.05). Conclusion Among the quantitative parameters obtained using ultrafast DCE-MRI, early arterial phase TTE was the most accurate for distinguishing between benign and malignant tumors.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

This study aimed to investigate changes in candidemia incidence, species distribution, antifungal susceptibility, initial antifungal use, and mortality trends in Korea before and during the COVID-19 pandemic. A retrospective analysis was conducted on candidemia cases from two tertiary care hospitals in Korea between 2017 and 2022. Data were compared between the pre-pandemic (2017-2019) and pandemic (2020-2022) periods. Statistical methods included incidence rate ratios (IRRs) and multivariate Cox regression to assess 30-day mortality risk factors. A total of 470 candidemia cases were identified, with 48.7% occurring pre-pandemic and 51.3% during the pandemic. While the overall incidence of candidemia remained similar across the two periods (IRR 1.15;p=0.13), the incidence in intensive care units (ICUs) significantly increased during the pandemic (IRR 1.50; p<0.01). The distribution of Candida species did not differ significantly between the two periods. Fluconazole non-susceptibility in C. albicans markedly decreased (10.0% vs. 0.9%, p<0.01), whereas C. glabrata exhibited a significant rise in caspofungin non-susceptibility during the pandemic (0% vs. 22.4%, p<0.01).Echinocandin use increased (21.8% vs. 34.4%; p<0.01), while fluconazole use declined (48.0% vs. 32.8%; p<0.01). Although the 30-day mortality rate was higher during the pandemic (60.2% vs. 57.2%), the difference was not statistically significant (p=0.57).The findings highlight the need for region-specific surveillance and tailored management strategies to improve candidemia outcomes, especially during healthcare disruptions like the COVID-19 pandemic.

Objective: To evaluate the clinical efficacy of ultrafast dynamic contrast-enhanced (DCE)-MRI using a compressed sensing (CS) technique for differentiating benign and malignant soft-tissue tumors (STTs) and to evaluate the factors related to the grading of malignant STTs. Materials and Methods: A total of 165 patients (96 male; mean age, 61 years), comprising 111 with malignant STTs and 54 with benign STTs according to the 2020 WHO classification, underwent DCE-MRI with CS between June 2018 and June 2023. The clinical, qualitative, and quantitative parameters associated with conventional MRI were also obtained. During post-processing of the early arterial phase of DCE-MRI, the time-to-enhance (TTE), time-to-peak (TTP), initial area under the curve at 60 s (iAUC60), and maximum slope were calculated. Furthermore, the delayed arterial phase parameters of DCEMRI, including Ktrans , Kep, Ve, and iAUC values and time-concentration curve (TCC) types, were determined. Clinical and MRI parameters were statistically analyzed to differentiate between benign and malignant tumors and their correlation with tumor grading. Results: According to logistic regression analysis, the TTE value (P < 0.001) of the early arterial phase and Ve (P = 0.039) and iAUC (P = 0.006) values of the delayed arterial phase, as well as age, location, peritumoral edema, and contrast heterogeneity on conventional MRI, were significant (P = 0.001–0.015) in differentiating benign and malignant tumors. Among all the quantitative parameters, the TTE value had the highest accuracy, with an area under the receiver operating characteristic curve of 0.902. The grading of malignant tumors was significantly correlated with peritumoral edema; CE heterogeneity; visual diffusion restriction; minimum and mean ADC; TTP, Kep, and Ve values; and the TCC graph (all P < 0.05). Conclusion Among the quantitative parameters obtained using ultrafast DCE-MRI, early arterial phase TTE was the most accurate for distinguishing between benign and malignant tumors.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Objective: To evaluate the clinical efficacy of ultrafast dynamic contrast-enhanced (DCE)-MRI using a compressed sensing (CS) technique for differentiating benign and malignant soft-tissue tumors (STTs) and to evaluate the factors related to the grading of malignant STTs. Materials and Methods: A total of 165 patients (96 male; mean age, 61 years), comprising 111 with malignant STTs and 54 with benign STTs according to the 2020 WHO classification, underwent DCE-MRI with CS between June 2018 and June 2023. The clinical, qualitative, and quantitative parameters associated with conventional MRI were also obtained. During post-processing of the early arterial phase of DCE-MRI, the time-to-enhance (TTE), time-to-peak (TTP), initial area under the curve at 60 s (iAUC60), and maximum slope were calculated. Furthermore, the delayed arterial phase parameters of DCEMRI, including Ktrans , Kep, Ve, and iAUC values and time-concentration curve (TCC) types, were determined. Clinical and MRI parameters were statistically analyzed to differentiate between benign and malignant tumors and their correlation with tumor grading. Results: According to logistic regression analysis, the TTE value (P < 0.001) of the early arterial phase and Ve (P = 0.039) and iAUC (P = 0.006) values of the delayed arterial phase, as well as age, location, peritumoral edema, and contrast heterogeneity on conventional MRI, were significant (P = 0.001–0.015) in differentiating benign and malignant tumors. Among all the quantitative parameters, the TTE value had the highest accuracy, with an area under the receiver operating characteristic curve of 0.902. The grading of malignant tumors was significantly correlated with peritumoral edema; CE heterogeneity; visual diffusion restriction; minimum and mean ADC; TTP, Kep, and Ve values; and the TCC graph (all P < 0.05). Conclusion Among the quantitative parameters obtained using ultrafast DCE-MRI, early arterial phase TTE was the most accurate for distinguishing between benign and malignant tumors.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

This study aimed to investigate changes in candidemia incidence, species distribution, antifungal susceptibility, initial antifungal use, and mortality trends in Korea before and during the COVID-19 pandemic. A retrospective analysis was conducted on candidemia cases from two tertiary care hospitals in Korea between 2017 and 2022. Data were compared between the pre-pandemic (2017-2019) and pandemic (2020-2022) periods. Statistical methods included incidence rate ratios (IRRs) and multivariate Cox regression to assess 30-day mortality risk factors. A total of 470 candidemia cases were identified, with 48.7% occurring pre-pandemic and 51.3% during the pandemic. While the overall incidence of candidemia remained similar across the two periods (IRR 1.15;p=0.13), the incidence in intensive care units (ICUs) significantly increased during the pandemic (IRR 1.50; p<0.01). The distribution of Candida species did not differ significantly between the two periods. Fluconazole non-susceptibility in C. albicans markedly decreased (10.0% vs. 0.9%, p<0.01), whereas C. glabrata exhibited a significant rise in caspofungin non-susceptibility during the pandemic (0% vs. 22.4%, p<0.01).Echinocandin use increased (21.8% vs. 34.4%; p<0.01), while fluconazole use declined (48.0% vs. 32.8%; p<0.01). Although the 30-day mortality rate was higher during the pandemic (60.2% vs. 57.2%), the difference was not statistically significant (p=0.57).The findings highlight the need for region-specific surveillance and tailored management strategies to improve candidemia outcomes, especially during healthcare disruptions like the COVID-19 pandemic.

Objective: To evaluate the clinical efficacy of ultrafast dynamic contrast-enhanced (DCE)-MRI using a compressed sensing (CS) technique for differentiating benign and malignant soft-tissue tumors (STTs) and to evaluate the factors related to the grading of malignant STTs. Materials and Methods: A total of 165 patients (96 male; mean age, 61 years), comprising 111 with malignant STTs and 54 with benign STTs according to the 2020 WHO classification, underwent DCE-MRI with CS between June 2018 and June 2023. The clinical, qualitative, and quantitative parameters associated with conventional MRI were also obtained. During post-processing of the early arterial phase of DCE-MRI, the time-to-enhance (TTE), time-to-peak (TTP), initial area under the curve at 60 s (iAUC60), and maximum slope were calculated. Furthermore, the delayed arterial phase parameters of DCEMRI, including Ktrans , Kep, Ve, and iAUC values and time-concentration curve (TCC) types, were determined. Clinical and MRI parameters were statistically analyzed to differentiate between benign and malignant tumors and their correlation with tumor grading. Results: According to logistic regression analysis, the TTE value (P < 0.001) of the early arterial phase and Ve (P = 0.039) and iAUC (P = 0.006) values of the delayed arterial phase, as well as age, location, peritumoral edema, and contrast heterogeneity on conventional MRI, were significant (P = 0.001–0.015) in differentiating benign and malignant tumors. Among all the quantitative parameters, the TTE value had the highest accuracy, with an area under the receiver operating characteristic curve of 0.902. The grading of malignant tumors was significantly correlated with peritumoral edema; CE heterogeneity; visual diffusion restriction; minimum and mean ADC; TTP, Kep, and Ve values; and the TCC graph (all P < 0.05). Conclusion Among the quantitative parameters obtained using ultrafast DCE-MRI, early arterial phase TTE was the most accurate for distinguishing between benign and malignant tumors.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.