Korean Practice Guidelines for Gastric Cancer 2024: An Evidence-based, Multidisciplinary Approach (Update of 2022 Guideline).

Gastric cancer is one of the most common cancers in both Korea and worldwide. Since 2004, the Korean Practice Guidelines for Gastric Cancer have been regularly updated, with the 4th edition published in 2022. The 4th edition was the result of a collaborative work by an interdisciplinary team, including experts in gastric surgery, gastroenterology, endoscopy, medical oncology, abdominal radiology, pathology, nuclear medicine, radiation oncology, and guideline development methodology.

Multi-omic insights into molecular mechanism and therapeutic targets in spinocerebellar ataxia type 7.

Recent advances in molecular science have significantly enlightened our mechanistic understanding of spinocerebellar ataxia type 7. To further close remaining gaps, we performed a multi-omics analysis using SCA7 mice. Entire brain tissue samples were collected from 12-week-old mice, and RNA sequencing, methylation analysis, and proteomic analysis were performed.

Risk of Osteoporotic Fractures among Patients with Thyroid Cancer: A Nationwide Population-Based Cohort Study.

Background: The associations between thyroid cancer and skeletal outcomes have not been thoroughly investigated. We aimed to investigate the risk of osteoporotic fractures in patients with thyroid cancer compared to that in a matched control group.

Methods: This retrospective cohort study included 2,514 patients with thyroid cancer and 75,420 matched controls from the Korean National Health Insurance Service-National Sample Cohort (NHIS-NSC, 2006-2019).

A Novel Point-of-Care Prediction Model for Steatotic Liver Disease: Expected Role of Mass Screening in the Global Obesity Crisis.

Background/aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD.

A Comprehensive Health Screening Program Reveals the Prevalence of and Risk Factors for Age-Related Macular Degeneration: A Cross-Sectional Analysis.

: We investigated the prevalence of age-related macular degeneration (AMD) and associated risk factors in Korean subjects who underwent comprehensive health screening examinations. : This single health screening center-based cross-sectional study included a total of 73,574 consecutive participants older than 30 years who underwent a health screening examination, including fundus photography, between October 2003 and December 2010. Weighted prevalence and risk factors for AMD were evaluated.

Lateralizing Value of Artificial Intelligence-Based Segmentation Software in MRI-Negative Focal Epilepsy.

Background And Purpose: The magnetic resonance images (MRIs) ability of lesion detection in epilepsy is crucial for a diagnosis and surgical outcome. Using automated artificial intelligence (AI)-based tools for measuring cortical thickness and brain volume originally developed for dementia, we aimed to identify whether it could lateralize epilepsy with normal MRIs.

Methods: Non-lesional 3-Tesla MRIs of 428 patients diagnosed with focal epilepsy, based on semiology and electroencephalography findings, were analyzed.

Obesity and Polycystic Ovary Syndrome.

The core pathophysiology of polycystic ovary syndrome involves an overproduction of androgens primarily originating from ovarian thecal cells. Two major external triggers promote androgen overproduction in the ovaries: the increased secretion of luteinizing hormone, a consequence of aberrant hypothalamic gonadotropin-releasing hormone secretion dynamics, and compensatory hyperinsulinemia resulting from insulin resistance. Obesity interacts with polycystic ovary syndrome in multiple ways, but a major role of obesity in its pathophysiology is the exacerbation of insulin resistance.

Evaluation of prediction errors in nine intraocular lens calculation formulas using an explainable machine learning model.

Background: The purpose of the study was to evaluate the relationship between prediction errors (PEs) and ocular biometric variables in cataract surgery using nine intraocular lens (IOL) formulas with an explainable machine learning model.

Methods: We retrospectively analyzed the medical records of consecutive patients who underwent standard cataract surgery with a Tecnis 1-piece IOL (ZCB00) at a single center. We calculated predicted refraction using the following IOL formulas: Barrett Universal II (BUII), Cooke K6, EVO V2.

Association Between Longer Cecal Intubation Time and Detection and Miss Rate of Colorectal Neoplasms.

A longer cecal intubation time (CIT) occurs during colonoscopy under difficult insertion conditions, which may hinder meticulous mucosal observation. However, whether a longer CIT has detrimental effects on the detection of adenomas remains unclear. We evaluated the effects of CIT on the detection and miss rates of colorectal neoplasms in asymptomatic participants.

Comparison of Population Attributable Fractions of Cancer Incidence and Mortality Linked to Excess Body Weight in Korea from 2015 to 2030.

Backgruound: The increasing rate of excess body weight (EBW) in the global population has led to growing health concerns, including cancer-related EBW. We aimed to estimate the population attributable fraction (PAF) of cancer incidence and deaths linked to EBW in Korean individuals from 2015 to 2030 and to compare its value with various body mass index cutoffs.

Methods: Levin's formula was used to calculate the PAF; the prevalence rates were computed using the Korean National Health and Nutrition Examination Survey data, while the relative risks of specific cancers related to EBW were estimated based on the results of Korean cohort studies.

Artificial intelligence-based body composition analysis using computed tomography images predicts both prevalence and incidence of diabetes mellitus.

Aim/introduction: We assess the efficacy of artificial intelligence (AI)-based, fully automated, volumetric body composition metrics in predicting the risk of diabetes.

Materials And Methods: This was a cross-sectional and 10-year retrospective longitudinal study. The cross-sectional analysis included health check-up data of 15,330 subjects with abdominal computed tomography (CT) images between January 1, 2011, and September 30, 2012.

Estimation of Population Attributable Fraction by Hormone and Reproductive Factors on Female Cancer in the Republic of Korea, 2015 to 2030.

Purpose: Population attributable fractions (PAFs) for hormone and reproductive factors have been estimated in several countries. IARC designated as Group 1 and Group 2A carcinogen for hormone factors in breast, ovarian, endometrial and uterine cervix cancer. This study aimed to estimate the PAFs of hormone/reproductive factor attributed to cancer incidence and deaths in Korean women and projected trends from 2015 to 2030.

Associations of Retinal Vessel Geometry and Optical Coherence Tomography Angiography Metrics With Choroidal Metrics in Diabetic Retinopathy.

Purpose: To elucidate the mechanism underlying changes in choroidal metrics (choroidal thickness [CT], choroidal vascularity index [CVI], and choriocapillaris [CC] flow deficit [FD]) observed in diabetic retinopathy (DR) and examine the association of choroidal metrics with both retinal vessel geometry and optical coherence tomography angiography (OCTA) metrics.

Methods: Overall, 133 eyes of 133 patients were analyzed retrospectively. Retinal vessel geometry parameters were assessed using semiautomated software.

Gender- and Age-Group Specific Association of Persistent Organic Pollutants (POPs) Exposure with Diabetes Mellitus: Korean National Environmental Health Survey (KoNEHS) 2015-2017.

Exposure to persistent organic pollutants (POPs) has been reported as a potential risk factor for diabetes in adults. However, effect modifications by sex and age, especially among Asian populations, have seldom been investigated. In the present study, we assessed associations of major POPs exposure including organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers (PBDEs), with diabetes in Korean adults ( = 1,295), a subset of the Korean National Environmental Health Survey (KoNEHS) Cycle 3 (2015-2017).

Agreement in anterior segment measurements between swept-source optical coherence and dual Scheimpflug tomography devices in keratoconus eyes.

Purpose: To evaluate the agreement of ocular biometry measured using a swept-source optical coherence (Casia 2) and a dual Scheimpflug (Galilei G6) tomography in keratoconus.

Methods: This retrospective study included 102 eyes from 102 keratoconus patient examined using both devices. Parameters compared included flat (Kf) and steep (Ks) keratometry, astigmatism of anterior, posterior, and total keratometry, central (CCT) and thinnest (TCT) corneal thickness.

Accelerated bone loss in late reproductive-aged and perimenopausal women with vitamin D insufficiency.

Introduction: The association between serum vitamin D levels and bone mineral density (BMD) varies by race and gender. This study aimed to evaluate this relationship between serum vitamin D levels and BMD, and changes of BMD over time in Korean women.

Materials And Methods: We analyzed data from 586 generally healthy Korean women aged 29-79 who underwent health check-ups at Seoul National University Gangnam Center between 2010 and 2011 (baseline measurement) and 2015-2016 (follow-up).

Use of Machine Learning to Predict Onset of NAFLD in an All-Comers Cohort-Development and Validation in 2 Large Asian Cohorts.

Background And Aims: Nonalcoholic fatty liver disease (NAFLD) is one of the most common liver diseases. There are no universally accepted models that accurately predict time to onset of NAFLD. Machine learning (ML) models may allow prediction of such time-to-event (ie, survival) outcomes.