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.

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: This study aimed to evaluate long-term treatment outcomes in patients with localized gastric mucosa-associated lymphoid tissue (MALT) lymphoma treated with radiotherapy (RT). Materials and Methods: A total of 229 patients who received RT in 10 tertiary hospitals between 2010 and 2019 were included in this multicenter analysis. Response after RT was based on esophagogastroduodenoscopy after RT. Locoregional relapse-free survival (LRFS) and disease-free survival (DFS), and overall survival (OS) were evaluated. Results: After a median follow-up time of 93.2 months, 5-year LRFS, DFS, and OS rates were 92.8%, 90.4%, and 96.1%, respectively. LRFS, DFS, and OS rates at 10 years were 90.3%, 87.7%, and 92.8%, respectively. Of 229 patients, 228 patients (99.6%) achieved complete remission after RT. Five-year LRFS was significantly lower in patients with stage IIE than in those with stage IE (77.4% vs. 94.2%, p=0.047). Patients with age ≥ 60 had significantly lower LRFS than patients with age < 60 (89.3% vs. 95.1%, p=0.003). In the multivariate analysis, old age (≥ 60 years) was a poor prognostic factor for LRFS (hazard ratio, 3.72; confidence interval, 1.38 to 10.03; p=0.009). Grade 2 or higher gastritis was reported in 69 patients (30.1%). Secondary malignancies including gastric adenocarcinoma, malignant lymphoma, lung cancer, breast cancer, and prostate cancer were observed in 11 patients (4.8%) after RT. Conclusion Patients treated with RT for localized gastric MALT lymphoma showed favorable 10-year outcomes. Radiation therapy is an effective treatment without an increased risk of secondary cancer. The toxicity for RT to the stomach is not high.

Purpose: This study aimed to evaluate long-term treatment outcomes in patients with localized gastric mucosa-associated lymphoid tissue (MALT) lymphoma treated with radiotherapy (RT). Materials and Methods: A total of 229 patients who received RT in 10 tertiary hospitals between 2010 and 2019 were included in this multicenter analysis. Response after RT was based on esophagogastroduodenoscopy after RT. Locoregional relapse-free survival (LRFS) and disease-free survival (DFS), and overall survival (OS) were evaluated. Results: After a median follow-up time of 93.2 months, 5-year LRFS, DFS, and OS rates were 92.8%, 90.4%, and 96.1%, respectively. LRFS, DFS, and OS rates at 10 years were 90.3%, 87.7%, and 92.8%, respectively. Of 229 patients, 228 patients (99.6%) achieved complete remission after RT. Five-year LRFS was significantly lower in patients with stage IIE than in those with stage IE (77.4% vs. 94.2%, p=0.047). Patients with age ≥ 60 had significantly lower LRFS than patients with age < 60 (89.3% vs. 95.1%, p=0.003). In the multivariate analysis, old age (≥ 60 years) was a poor prognostic factor for LRFS (hazard ratio, 3.72; confidence interval, 1.38 to 10.03; p=0.009). Grade 2 or higher gastritis was reported in 69 patients (30.1%). Secondary malignancies including gastric adenocarcinoma, malignant lymphoma, lung cancer, breast cancer, and prostate cancer were observed in 11 patients (4.8%) after RT. Conclusion Patients treated with RT for localized gastric MALT lymphoma showed favorable 10-year outcomes. Radiation therapy is an effective treatment without an increased risk of secondary cancer. The toxicity for RT to the stomach is not high.

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.

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.

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: This study aimed to evaluate long-term treatment outcomes in patients with localized gastric mucosa-associated lymphoid tissue (MALT) lymphoma treated with radiotherapy (RT). Materials and Methods: A total of 229 patients who received RT in 10 tertiary hospitals between 2010 and 2019 were included in this multicenter analysis. Response after RT was based on esophagogastroduodenoscopy after RT. Locoregional relapse-free survival (LRFS) and disease-free survival (DFS), and overall survival (OS) were evaluated. Results: After a median follow-up time of 93.2 months, 5-year LRFS, DFS, and OS rates were 92.8%, 90.4%, and 96.1%, respectively. LRFS, DFS, and OS rates at 10 years were 90.3%, 87.7%, and 92.8%, respectively. Of 229 patients, 228 patients (99.6%) achieved complete remission after RT. Five-year LRFS was significantly lower in patients with stage IIE than in those with stage IE (77.4% vs. 94.2%, p=0.047). Patients with age ≥ 60 had significantly lower LRFS than patients with age < 60 (89.3% vs. 95.1%, p=0.003). In the multivariate analysis, old age (≥ 60 years) was a poor prognostic factor for LRFS (hazard ratio, 3.72; confidence interval, 1.38 to 10.03; p=0.009). Grade 2 or higher gastritis was reported in 69 patients (30.1%). Secondary malignancies including gastric adenocarcinoma, malignant lymphoma, lung cancer, breast cancer, and prostate cancer were observed in 11 patients (4.8%) after RT. Conclusion Patients treated with RT for localized gastric MALT lymphoma showed favorable 10-year outcomes. Radiation therapy is an effective treatment without an increased risk of secondary cancer. The toxicity for RT to the stomach is not high.

Pediatric hypertension (HTN) is a significant, growing health concern worldwide and also in Korea. Diagnosis, evaluation, and treatment of HTN in Korean children and adolescents are uncertain due to limitations in using the current international guidelines, since the recommendations by the American Academy of Pediatrics (AAP) and European Society of Hypertension (ESH) guidelines differ. Furthermore, these are guidelines for Western youth, who are racially and ethnically different from Koreans. In addition, reference blood pressure values for all pediatric age groups, which are essential for the diagnosis of HTN according to these two guidelines, are absent in Korea. Therefore, HTN guidelines for Korean children and adolescents should be established. The Korean Working Group of Pediatric Hypertension established clinical guidelines for the diagnosis, evaluation, and management of HTN in Korean children and adolescents. These guidelines were based on reported clinical evidence, expert recommendations, and AAP and ESH guidelines. The characteristics of Korean youth and the Korean medical and insurance system were considered during the establishment of the guidelines. By providing recommendations suitable for Korean youth, these guidelines will help in the prevention and management of childhood HTN, thus relieving the burden of cardiovascular disease in adulthood in Korea.

Colorectal cancer is the third most common cancer in Korea and the third leading cause of death from cancer. Treatment outcomes for colon cancer are steadily improving due to national health screening programs with advances in diagnostic methods, surgical techniques, and therapeutic agents.. The Korea Colon Cancer Multidisciplinary (KCCM) Committee intends to provide professionals who treat colon cancer with the most up-to-date, evidence-based practice guidelines to improve outcomes and help them make decisions that reflect their patients’ values and preferences. These guidelines have been established by consensus reached by the KCCM Guideline Committee based on a systematic literature review and evidence synthesis and by considering the national health insurance system in real clinical practice settings. Each recommendation is presented with a recommendation strength and level of evidence based on the consensus of the committee.