Hysteroscopy is particularly valuable for the diagnosis of uterine cavity abnormalities through direct visualization. The development of office hysteroscopy has expanded the range of diagnostic and surgical procedures available. These detailed guidelines include patient counseling and the selection and setting of office hysteroscopy, including room, equipment, and medical staff. Analgesia or local anesthesia is often required in selective office hysteroscopy cases. Cervical dilation and preparation using medical or mechanical methods are required for most diagnostic hysteroscopic procedures. Methods for optimizing visualization and choosing suitable distension media are important for a successful office hysteroscopy. It is crucial to adhere to guidelines to prevent complications, such as vasovagal syncope, cervical trauma, uterine perforation, fluid overload, and embolism. Vaginoscopy can be a good alternative option for alleviating pain, especially in cases where the insertion of a vaginal speculum is expected to be challenging.

Background: Active surveillance (AS) has emerged as a viable management strategy for low-risk papillary thyroid microcarcinoma (PTMC), following pioneering trials at Kuma Hospital and the Cancer Institute Hospital in Japan. Numerous prospective cohort studies have since validated AS as a management option for low-risk PTMC, leading to its inclusion in thyroid cancer guidelines across various countries. From 2016 to 2020, the Multicenter Prospective Cohort Study of Active Surveillance on Papillary Thyroid Microcarcinoma (MAeSTro) enrolled 1,177 patients, providing comprehensive data on PTMC progression, sonographic predictors of progression, quality of life, surgical outcomes, and cost-effectiveness when comparing AS to immediate surgery. The second phase of MAeSTro (MAeSTro-EXP) expands AS to low-risk papillary thyroid carcinoma (PTC) tumors larger than 1 cm, driven by the hypothesis that overall risk assessment outweighs absolute tumor size in surgical decision-making. Methods: This protocol aims to address whether limiting AS to tumors smaller than 1 cm may result in unnecessary surgeries for low-risk PTCs detected during their rapid initial growth phase. By expanding the AS criteria to include tumors up to 1.5 cm, while simultaneously refining and standardizing the criteria for risk assessment and disease progression, we aim to minimize overtreatment and maintain rigorous monitoring to improve patient outcomes. Conclusion This study will contribute to optimizing AS guidelines and enhance our understanding of the natural course and appropriate management of low-risk PTCs. Additionally, MAeSTro-EXP involves a multinational collaboration between South Korea and Australia. This cross-country study aims to identify cultural and racial differences in the management of low-risk PTC, thereby enriching the global understanding of AS practices and their applicability across diverse populations.

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: Active surveillance (AS) has emerged as a viable management strategy for low-risk papillary thyroid microcarcinoma (PTMC), following pioneering trials at Kuma Hospital and the Cancer Institute Hospital in Japan. Numerous prospective cohort studies have since validated AS as a management option for low-risk PTMC, leading to its inclusion in thyroid cancer guidelines across various countries. From 2016 to 2020, the Multicenter Prospective Cohort Study of Active Surveillance on Papillary Thyroid Microcarcinoma (MAeSTro) enrolled 1,177 patients, providing comprehensive data on PTMC progression, sonographic predictors of progression, quality of life, surgical outcomes, and cost-effectiveness when comparing AS to immediate surgery. The second phase of MAeSTro (MAeSTro-EXP) expands AS to low-risk papillary thyroid carcinoma (PTC) tumors larger than 1 cm, driven by the hypothesis that overall risk assessment outweighs absolute tumor size in surgical decision-making. Methods: This protocol aims to address whether limiting AS to tumors smaller than 1 cm may result in unnecessary surgeries for low-risk PTCs detected during their rapid initial growth phase. By expanding the AS criteria to include tumors up to 1.5 cm, while simultaneously refining and standardizing the criteria for risk assessment and disease progression, we aim to minimize overtreatment and maintain rigorous monitoring to improve patient outcomes. Conclusion This study will contribute to optimizing AS guidelines and enhance our understanding of the natural course and appropriate management of low-risk PTCs. Additionally, MAeSTro-EXP involves a multinational collaboration between South Korea and Australia. This cross-country study aims to identify cultural and racial differences in the management of low-risk PTC, thereby enriching the global understanding of AS practices and their applicability across diverse populations.

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.

Hysteroscopy is particularly valuable for the diagnosis of uterine cavity abnormalities through direct visualization. The development of office hysteroscopy has expanded the range of diagnostic and surgical procedures available. These detailed guidelines include patient counseling and the selection and setting of office hysteroscopy, including room, equipment, and medical staff. Analgesia or local anesthesia is often required in selective office hysteroscopy cases. Cervical dilation and preparation using medical or mechanical methods are required for most diagnostic hysteroscopic procedures. Methods for optimizing visualization and choosing suitable distension media are important for a successful office hysteroscopy. It is crucial to adhere to guidelines to prevent complications, such as vasovagal syncope, cervical trauma, uterine perforation, fluid overload, and embolism. Vaginoscopy can be a good alternative option for alleviating pain, especially in cases where the insertion of a vaginal speculum is expected to be challenging.

Background: Active surveillance (AS) has emerged as a viable management strategy for low-risk papillary thyroid microcarcinoma (PTMC), following pioneering trials at Kuma Hospital and the Cancer Institute Hospital in Japan. Numerous prospective cohort studies have since validated AS as a management option for low-risk PTMC, leading to its inclusion in thyroid cancer guidelines across various countries. From 2016 to 2020, the Multicenter Prospective Cohort Study of Active Surveillance on Papillary Thyroid Microcarcinoma (MAeSTro) enrolled 1,177 patients, providing comprehensive data on PTMC progression, sonographic predictors of progression, quality of life, surgical outcomes, and cost-effectiveness when comparing AS to immediate surgery. The second phase of MAeSTro (MAeSTro-EXP) expands AS to low-risk papillary thyroid carcinoma (PTC) tumors larger than 1 cm, driven by the hypothesis that overall risk assessment outweighs absolute tumor size in surgical decision-making. Methods: This protocol aims to address whether limiting AS to tumors smaller than 1 cm may result in unnecessary surgeries for low-risk PTCs detected during their rapid initial growth phase. By expanding the AS criteria to include tumors up to 1.5 cm, while simultaneously refining and standardizing the criteria for risk assessment and disease progression, we aim to minimize overtreatment and maintain rigorous monitoring to improve patient outcomes. Conclusion This study will contribute to optimizing AS guidelines and enhance our understanding of the natural course and appropriate management of low-risk PTCs. Additionally, MAeSTro-EXP involves a multinational collaboration between South Korea and Australia. This cross-country study aims to identify cultural and racial differences in the management of low-risk PTC, thereby enriching the global understanding of AS practices and their applicability across diverse populations.

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.

Hysteroscopy is particularly valuable for the diagnosis of uterine cavity abnormalities through direct visualization. The development of office hysteroscopy has expanded the range of diagnostic and surgical procedures available. These detailed guidelines include patient counseling and the selection and setting of office hysteroscopy, including room, equipment, and medical staff. Analgesia or local anesthesia is often required in selective office hysteroscopy cases. Cervical dilation and preparation using medical or mechanical methods are required for most diagnostic hysteroscopic procedures. Methods for optimizing visualization and choosing suitable distension media are important for a successful office hysteroscopy. It is crucial to adhere to guidelines to prevent complications, such as vasovagal syncope, cervical trauma, uterine perforation, fluid overload, and embolism. Vaginoscopy can be a good alternative option for alleviating pain, especially in cases where the insertion of a vaginal speculum is expected to be challenging.

Background: There are few long-term large-scale epidemiologic studies on hypertrophic cardiomyopathy (HCM; 10th revision of the International Statistical Classification of Diseases and Related Health Problems codes: I42.1, I42.2). This analysis used the Korean National Health Insurance Service (KNHIS) data between 2006 and 2017 to evaluate the natural history of HCM over a decade. Methods: KNHIS data and death statistics were evaluated according to age, sex, socioeconomic position, and comorbidities. Survival rates (SRs) and adjusted hazard ratio (HR) were compared with death data of the Korean population from 2006 through 2018. Results: The mean age was 47.3 ± 14.9 years in males and 57.5 ± 15.4 years in females (P < 0.001).The male proportion was 58.9%. The most common cause of death was diseases of the circulatory system. The 10-year SR of HCM was higher in males (75.9% vs. 62.5%, P < 0.001).The adjusted HR for different age groups was significantly high in most age group: 3.67 (95% confidence interval [CI], 2.65–5.10) for 0–9 years, and it gradually increased from the 30s to the 80s group (1.39 [95% CI, 1.05–1.83] for 30–39 years and 48.2 [95% CI, 37.0–62.7] for those older than 80 years). The adjusted HR was 1.12 (95% CI, 1.07–1.17) for males, 1.40 (95% CI, 1.33–1.48) for the lower income level, and 1.18 (95% CI, 1.12–1.25) for the medium income level. In patients with comorbidities, the adjusted HRs were 1.23 (95% CI, 1.16–1.30) for diabetes, 1.45 (95% CI, 1.30–1.62) for myocardial infarction, 1.63 (95% CI, 1.52–1.76) for atrial fibrillation, 1.83 (95% CI, 1.68–1.99) for ischemic stroke, 1.66 (95% CI, 1.31–2.10) for hemorrhagic stroke, 2.42 (95% CI, 2.16–2.70) for chronic kidney disease, and 3.18 (95% CI, 2.87–3.52) for malignant neoplasm. Conclusion HCM in Korea showed a higher prevalence and incidence in males. The 10-year SR of HCM was approximately 70% and lower in females than that in males. The risk of death from HCM increased with age and was significantly higher in males, individuals with low income levels, and patients with various comorbidities. These findings should be considered for the long-term management and allocation of healthcare resources for patients with HCM.