Objective: To evaluate the performance of supplemental automated breast ultrasound (ABUS) added to mammography-based breast cancer screening for women with nondense breasts. Materials and Methods: A retrospective search of radiology databases at two tertiary institutions identified asymptomatic women with nondense breasts who underwent breast cancer screening using both digital mammography (DM) and supplemental ABUS between January 2020 and December 2023. We excluded women without sufficient follow-up data or those without an established final diagnosis, including histopathologic results. The performance measures of DM alone and ABUS combined with DM (ABUS plus DM) were compared. The primary outcome was the cancer detection rate (CDR), and the secondary outcomes were sensitivity and specificity. Subgroup analyses were performed for women with scattered fibroglandular density and almost entirely fatty breasts. Results: A total of 2,904 pairs of screening examinations were performed in 1,683 women (59 ± 10 years), detecting 26 cancers. In comparison with DM alone, ABUS plus DM showed higher CDR (9.0 vs. 7.9 per 1,000 examinations, P < 0.001), higher sensitivity (100% [26/26] vs. 88.5% [23/26], P < 0.001), and lower specificity (95.0% [2,735/2,878] vs. 97.9% [2,817/2,878], P < 0.001). In women with scattered fibroglandular density, ABUS increased the CDR from 7.4 to 8.5 per 1,000 examinations and improved the sensitivity from 87.0% [20/23] to 100% [23/23] (P < 0.001). In women with almost entirely fatty breasts, ABUS plus DM showed the same CDR (16.4 per 1,000 examinations) and sensitivity (100% [3/3]) as DM alone. Three cancers (11.5% [3/26]), all of which were stage T1N0, were detected only by supplemental ABUS. Conclusion Supplemental ABUS improved cancer detection and sensitivity in women with nondense breasts, with the benefits primarily observed in those with scattered fibroglandular density.

Background: and Purpose Although gastrointestinal (GI) surgery can affect drug efficacy, its impact on oral anticoagulants (OACs) remains unclear. This study investigated the impact of GI surgery on the comparative effectiveness and safety of direct oral anticoagulants (DOACs) versus warfarin in atrial fibrillation (AF). Methods: Using a nationwide South Korean claims database, AF patients prescribed OACs between 2015 and 2021 were analyzed. The primary outcome was ischemic stroke, and secondary outcomes were major bleeding, all-cause death, and composite outcome (ischemic stroke, major bleeding, all-cause death). Outcomes were compared by OAC type (DOAC vs. warfarin) and GI surgery status using inverse probability of treatment weighting and a Fine–Gray subdistribution hazard model with time-varying covariates. Results: Among 388,214 AF patients (mean age, 71.9 yr; 42.7% male), 6,907 (1.8%) underwent GI surgery. The effect of GI surgery differed between warfarin and DOACs for ischemic stroke (Pint= 0.018), with increased risk for warfarin (adjusted HR [aHR]: 2.32; 95% confidence interval [CI] 1.17–4.62), but not for DOACs (aHR: 0.97 [95% CI 0.77–1.22]) after surgery. Effects of surgery on major bleeding, all-cause death, and composite outcome did not differ. In post–GI surgery setting, DOACs demonstrated lower ischemic stroke risk (aHR: 0.35 [95% CI 0.17–0.72]) and comparable risks for other outcomes. Exploratory analyses by surgery location suggested a more favorable profile of DOACs after upper GI surgery. Conclusions In AF patients, GI surgery significantly increased ischemic stroke risk among warfarin users, but not among DOAC users. DOACs showed generally favorable profiles after GI surgery and may remain a reasonable anticoagulant, with potentially more favorable profile after upper GI surgery.

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.

Purpose: The associated factors for hypertensive retinopathy (HTR) are rarely investigated. This study aimed to identify the associated factors for HTR using a systematic review. Methods: The review included cross-sectional, case-controlled, and cohort studies on HTR risk factors published in Korean and English with full texts available from PubMed, Embase, CINAHL, Web of Science, and Korean databases. Methodological quality was assessed using the Joanna Briggs Institute (JBI) checklist. Results: Eleven studies were finally selected, and three studies including patients with hypertension without diabetes mellitus, older age, male sex, alcohol consumption, the duration of hypertension, hyperglycemia, dyslipidemia, microalbuminuria, high creatinine levels, chronic kidney disease, and cardiovascular changes were identified as factors associated with HTR. Conversely, in the remaining eight studies, younger age, non-smoking status, and renal function indicators (albuminuria, high creatinine levels, chronic kidney disease, and uric acid) were identified as associated factors. Conclusions Regardless of the inclusion of patients with diabetes mellitus, impaired kidney functions were determined as significant factors associated with retinopathy in patients with HTR. However, considering a limited number of evidence and lack of evidence to confirm causality, we recommend further research on renal function and HTR.

Background: Primary cutaneous CD30+ lymphoproliferative disorders (pcCD30-LPDs) are a diseases with various clinical and prognostic characteristics. Objective: Increasing our knowledge of the clinical characteristics of pcCD30-LPDs and identifying potential prognostic variables in an Asian population. Methods: Clinicopathological features and survival data of pcCD30-LPD cases obtained from 22 hospitals in South Korea were examined. Results: A total of 413 cases of pcCD30-LPDs (lymphomatoid papulosis [LYP], n=237; primary cutaneous anaplastic large cell lymphoma [C-ALCL], n=176) were included. Ninety percent of LYP patients and roughly 50% of C-ALCL patients presented with multiple skin lesions. Both LYP and C-ALCL affected the lower limbs most frequently. Multiplicity and advanced T stage of LYP lesions were associated with a chronic course longer than 6 months. Clinical morphology with patch lesions and elevated serum lactate dehydrogenase were significantly associated with LPDs during follow-up in LYP patients. Extracutaneous involvement of C-ALCL occurred in 13.2% of patients. Lesions larger than 5 cm and increased serum lactate dehydrogenase were associated with a poor prognosis in C-ALCL. The survival of patients with C-ALCL was unaffected by the anatomical locations of skin lesions or other pathological factors. Conclusion The multiplicity or size of skin lesions was associated with a chronic course of LYP and survival among patients with C-ALCL.

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.

Purpose: The associated factors for hypertensive retinopathy (HTR) are rarely investigated. This study aimed to identify the associated factors for HTR using a systematic review. Methods: The review included cross-sectional, case-controlled, and cohort studies on HTR risk factors published in Korean and English with full texts available from PubMed, Embase, CINAHL, Web of Science, and Korean databases. Methodological quality was assessed using the Joanna Briggs Institute (JBI) checklist. Results: Eleven studies were finally selected, and three studies including patients with hypertension without diabetes mellitus, older age, male sex, alcohol consumption, the duration of hypertension, hyperglycemia, dyslipidemia, microalbuminuria, high creatinine levels, chronic kidney disease, and cardiovascular changes were identified as factors associated with HTR. Conversely, in the remaining eight studies, younger age, non-smoking status, and renal function indicators (albuminuria, high creatinine levels, chronic kidney disease, and uric acid) were identified as associated factors. Conclusions Regardless of the inclusion of patients with diabetes mellitus, impaired kidney functions were determined as significant factors associated with retinopathy in patients with HTR. However, considering a limited number of evidence and lack of evidence to confirm causality, we recommend further research on renal function and HTR.

Purpose: To evaluate the differences in the level of concerns regarding exotropia surgery according to the perspectives regarding surgery and basic characteristics of parents of pediatric patients with intermittent exotropia in South Korea. Methods: This study included the parents of pediatric patients with intermittent exotropia who underwent surgery at five hospitals, between June 2022 and February 2023. Parental perspectives, basic characteristics, and levels of concern regarding surgery were assessed using a questionnaire. We investigated the differences in concern levels according to perspectives regarding surgery and basic characteristics among parents, such as sex, age, residential area, and the most influential factors in the decision-making for surgery. Results: A total of 266 parents were included (228 mothers; age, 40.0±4.7 years). Parents who chose surgery for subjective symptoms had higher levels of concern about hemorrhage, conjunctival redness, and persistent overcorrection than did those who chose surgery for cosmetic reasons (all p < 0.05). Fathers were more concerned about postoperative pain, compared to mothers (p = 0.039). Parents in their 40s and 50s had higher levels of concern about the hospital environment compared with those in their 20s and 30s (p = 0.003). Concern did not significantly differ by residential area. Conclusions The level of concern regarding surgery differed according to the perspectives and characteristics of the parents of pediatric patients with intermittent exotropia. Parents who chose surgery for subjective symptoms of exotropia had a higher level of concern than did those who chose surgery for cosmetic reasons. The concern level differed according to the parents’ sex and age but not their residential area.

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.