Background: Skin rejuvenation has become an increasingly popular noninvasive approach to address age-related changes such as sagging, wrinkles, and skin laxity. Energy-based devices (EBDs) and injectables are widely used, but their application requires careful customization based on individual patient characteristics to optimize outcomes and minimize potential adverse effects. Objective: This study aimed to explore clinical practice patterns among board-certified dermatologists in South Korea, focusing on their strategies for tailoring skin rejuvenation treatments to individual patients, including the integration of EBDs, injectables, and senotherapeutics. Methods: A structured survey comprising 10 questions was administered to 13 experienced dermatologists specializing in skin rejuvenation. The survey covered treatment strategies for patients with varying facial fat volumes, pain management approaches, and the use of EBDs, injectables and senotherapeutics. Results: High-intensity focused ultrasound (HIFU) and radiofrequency (RF) were the most employed EBDs, often combined with injectables for enhanced outcomes. For patients with higher facial fat, HIFU and deoxycholic acid injections were preferred for contouring and tightening. For those with lower facial fat, biostimulatory agents such as poly-D, L-lactic acid and microneedle RF were favored to restore volume and elasticity. Pain management strategies included topical anesthetics and stepwise protocols. Although less commonly used, senotherapeutics were occasionally prescribed for specific conditions, such as melasma and extensive photoaging. Conclusion Dermatologists in South Korea employ a variety of patient-specific strategies for skin rejuvenation, combining various EBDs, injectables, and senotherapeutics. These findings highlight the importance of personalized treatment protocols and the need for further research to optimize treatment efficacy and safety.

Objective: : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

Objective: : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

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: 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. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

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: 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. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

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: 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. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.