Myopia, particularly high myopia, is a significant risk factor for several ocular pathologies including cataract, glaucoma, and retinal detachment. Excessive axial elongation associated with high myopia can induce biomechanical stretching, increasing the risk of serious complications like posterior staphyloma and myopic maculopathy. Global meta-analyses estimate that approximately 10 million people were visually impaired due to myopic maculopathy in 2015, with 3 million being blind. Recent nationwide surveys in South Korea revealed a prevalence of 65.4% for myopia and 6.9% for high myopia in children and adolescents, highlighting the urgent need for effective management. Delaying the onset and slowing the progression of myopia during childhood and adolescence is crucial for reducing the potential lifetime risk of these complications. This consensus statement, prepared by the Korean Myopia Society for the Korean Association for Pediatric Ophthalmology and Strabismus (KAPOS), reviews the current evidence for myopia control interventions and provides management strategies applicable to the South Korean clinical setting. Key interventions covered include lifestyle modifications (outdoor time, near work adjustment), optical methods (myopia-control spectacle lenses, dual-focus soft contact lenses, orthokeratology), and pharmacologic treatment (low-concentration atropine), as well as combination therapies. The statement also addresses patient selection, treatment outcome evaluation using spherical equivalent and axial length changes, and the crucial aspects related to treatment cessation and the rebound effect.

Coronary artery bypass grafting (CABG) remains a key revascularization strategy for ischemic heart disease; however, nationwide trends in the Republic of Korea have not been thoroughly investigated. Using data from the Korean National Health Insurance Service, we analyzed adult patients who underwent isolated CABG between 2005 and 2022. We evaluated surgical volume, patient demographics, procedural strategies (off-pump vs. onpump), and outcomes. International comparisons were conducted using national cardiac surgery registry data. A total of 51,923 CABG cases were identified. Annual surgical volume declined until 2013 but gradually increased thereafter, reaching 3,717 cases in 2022. Despite this recovery, Korea’s per capita CABG rate remains among the lowest worldwide.In contrast, more than 60% of procedures were performed off-pump—the highest rate worldwide. Over time, the average patient age and prevalence of diabetes increased, whereas in-hospital mortality showed a modest decline. Compared with other countries, the Republic of Korea demonstrated a uniquely low procedural volume and a strong preference for off-pump CABG. This nationwide analysis highlights Korea’s distinctive CABG practice patterns and provides valuable insights for optimizing future clinical and policy decisions in cardiac surgical care.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Purpose: This study investigated whether the respiratory phase during pleural puncture in CT-guided percutaneous transthoracic needle biopsy (PTNB) affects complications. Materials and Methods: We conducted a retrospective review of 477 lung biopsy CT scans performed during free breathing. The respiratory phases during pleural puncture were determined based on the table position of the targeted nodule using CT scans obtained during free breathing. We compared the rates of complications among the inspiratory, mid-, and expiratory respiratory phases. Logistic regression analysis was performed to control confounding factors associated with pneumothorax. Results: Among the 477 procedures, pleural puncture was performed during the expiratory phase in 227 (47.6%), during the mid-phase in 108 (22.6%), and during the inspiratory phase in 142 (29.8%). The incidence of pneumothorax was significantly lower in the expiratory puncture group (40/227, 17.6%; p = 0.035) and significantly higher in the mid-phase puncture group (31/108, 28.7%; p = 0.048). After controlling for confounding factors, expiratory-phase puncture was found to be an independent protective factor against pneumothorax (odds ratio = 0.571; 95% confidence interval = 0.360–0.906; p = 0.017). Conclusion Our findings suggest that pleural puncture during the expiratory phase may reduce the risk of pneumothorax during image guided PTNB.

Background/Aims: Chronic obstructive pulmonary disease (COPD) is less prevalent in females than males, but it affects mortality in females. There may be sex differences in the clinical characteristics of COPD. Methods: We analyzed the Korea National Health and Nutrition Examination Survey dataset from 2007 to 2018. We compared the clinical characteristics and comorbidities in subjects with COPD according to sex. We adjusted the multivariate logistic regression of lung cancer prevalence according to COPD and sex by age and smoking amount. Results: Females with COPD tended to be older than males with COPD (64.1 ± 0.4 yr vs. 62.3 ± 0.2 yr, respectively, p < 0.001). Approximately 89% of males with COPD had a smoking history, while 86% of females with COPD were non-smokers (p < 0.001). Household income was lower (p < 0.001) and asthma and overall malignancy were more prevalent in females with COPD than males with COPD (25.5 vs. 11.6%, respectively, p < 0.001; (6.3 vs. 5.4%, respectively, p < 0.001). However, lung cancer was more common in males with COPD than females with COPD (0.9 vs. 0.1%, respectively, p < 0.001). Lung cancer prevalence increased in males with moderate COPD compared to subjects without COPD (OR, 4.409; 95% CI, 1.741–9.419). Conclusions Females with COPD had a lower smoking rate, household income, and lung cancer prevalence than males with COPD. More active COPD screening is needed for women of low socioeconomic status, even if they do not smoke.

Purpose: This study investigated whether the respiratory phase during pleural puncture in CT-guided percutaneous transthoracic needle biopsy (PTNB) affects complications. Materials and Methods: We conducted a retrospective review of 477 lung biopsy CT scans performed during free breathing. The respiratory phases during pleural puncture were determined based on the table position of the targeted nodule using CT scans obtained during free breathing. We compared the rates of complications among the inspiratory, mid-, and expiratory respiratory phases. Logistic regression analysis was performed to control confounding factors associated with pneumothorax. Results: Among the 477 procedures, pleural puncture was performed during the expiratory phase in 227 (47.6%), during the mid-phase in 108 (22.6%), and during the inspiratory phase in 142 (29.8%). The incidence of pneumothorax was significantly lower in the expiratory puncture group (40/227, 17.6%; p = 0.035) and significantly higher in the mid-phase puncture group (31/108, 28.7%; p = 0.048). After controlling for confounding factors, expiratory-phase puncture was found to be an independent protective factor against pneumothorax (odds ratio = 0.571; 95% confidence interval = 0.360–0.906; p = 0.017). Conclusion Our findings suggest that pleural puncture during the expiratory phase may reduce the risk of pneumothorax during image guided PTNB.

Purpose: The female sex is reported to have a higher risk of adverse events (AEs) from cytotoxic chemotherapy. Few studies examined the sex differences in AEs and their impact on the use of medical services during adjuvant chemotherapy. This sub-study aimed to compare the incidence of any grade and grade ≥ 3 AEs, healthcare utilization, chemotherapy completion rate, and dose intensity according to sex. Materials and Methods: This is a sub-study of a multicenter cohort conducted in Korea that evaluated the impact of healthcare reimbursement on AE evaluation in patients who received adjuvant chemotherapy between September 2013 and December 2016 at four hospitals in Korea. Results: A total of 1,170 patients with colorectal, gastric, or non–small cell lung cancer were included in the study. Female patients were younger, had fewer comorbidities, and experienced less postoperative weight loss of > 10%. Females had significantly higher rates of any grade AEs including nausea, abdominal pain, stomatitis, vomiting, and neutropenia, and experienced more grade ≥ 3 neutropenia, nausea, and vomiting. The dose intensity of chemotherapy was significantly lower in females, and they also experienced more frequent dose reduction after the first cycle. Moreover, female patients receiving platinum-containing regimens had significantly higher rates of unscheduled outpatient visits. Conclusion Our study found that females experienced a higher incidence of multiple any-grade AEs and severe neutropenia, nausea, and vomiting, across various cancer types, leading to more frequent dose reductions. Physicians should be aware of sex differences in AEs for chemotherapy decisions.