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/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/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/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: /Aim: Atezolizumab plus bevacizumab and lenvatinib are currently available as first-line therapy for the treatment of unresectable hepatocellular carcinoma (HCC). However, comparative efficacy studies are still limited. This study aimed to investigate the effectiveness of these treatments in HCC patients with portal vein tumor thrombosis (PVTT). Methods: We retrospectively included patients who received either atezolizumab plus bevacizumab or lenvatinib as first-line systemic therapy for HCC with PVTT. Primary endpoint was overall survival (OS), and secondary endpoints included progressionfree survival (PFS) and disease control rate (DCR) determined by response evaluation criteria in solid tumors, version 1.1. Results: A total of 52 patients were included: 30 received atezolizumab plus bevacizumab and 22 received lenvatinib. The median follow-up duration was 6.4 months (interquartile range, 3.9-9.8). The median OS was 10.8 months (95% confidence interval [CI], 5.7 to not estimated) with atezolizumab plus bevacizumab and 5.8 months (95% CI, 4.8 to not estimated) with lenvatinib (P=0.26 by log-rank test). There was no statistically significant difference in OS (adjusted hazard ratio [aHR], 0.71; 95% CI, 0.34-1.49; P=0.37). The median PFS was similar (P=0.63 by log-rank test), with 4.1 months (95% CI, 3.3-7.7) for atezolizumab plus bevacizumab and 4.3 months (95% CI, 2.6-5.8) for lenvatinib (aHR, 0.93; 95% CI, 0.51-1.69; P=0.80). HRs were similar after inverse probability treatment weighting. The DCRs were 23.3% and 18.2% in patients receiving atezolizumab plus bevacizumab and lenvatinib, respectively (P=0.74). Conclusion The effectiveness of atezolizumab plus bevacizumab and lenvatinib was comparable for the treatment of HCC with PVTT.

Objective: Dysphagia is a common clinical condition characterized by difficulty in swallowing. It is sub-classified into oropharyngeal dysphagia, which refers to problems in the mouth and pharynx, and esophageal dysphagia, which refers to problems in the esophageal body and esophagogastric junction. Dysphagia can have a significant negative impact one’s physical health and quality of life as its severity increases. Therefore, proper assessment and management of dysphagia are critical for improving swallowing function and preventing complications. Thus a guideline was developed to provide evidence-based recommendations for assessment and management in patients with dysphagia. Methods: Nineteen key questions on dysphagia were developed. These questions dealt with various aspects of problems related to dysphagia, including assessment, management, and complications. A literature search for relevant articles was conducted using Pubmed, Embase, the Cochrane Library, and one domestic database of KoreaMed, until April 2021. The level of evidence and recommendation grade were established according to the Grading of Recommendation Assessment, Development and Evaluation methodology. Results: Early screening and assessment of videofluoroscopic swallowing were recommended for assessing the presence of dysphagia. Therapeutic methods, such as tongue and pharyngeal muscle strengthening exercises and neuromuscular electrical stimulation with swallowing therapy, were effective in improving swallowing function and quality of life in patients with dysphagia. Nutritional intervention and an oral care program were also recommended. Conclusion This guideline presents recommendations for the assessment and management of patients with oropharyngeal dysphagia, including rehabilitative strategies.

Objectives: : This study aimed to assess the possible association of dental pain with suicidal ideation among adolescents by analysing data from the 2018 Korean Youth Risk Behavior Survey, a nationwide online survey. Methods: : Of 62,823 adolescent middle and high school students in Korea, 60,040 participants were selected for analysis, after excluding cases with missing values. Participants were given a questionnaire about their self-evaluation of health including dental pain and suicidal ideation. Logistic regression analysis demonstrated the relationships between dental pain and suicidal ideation after controlling for potential confounding factors. Results: : The proportion of Korean adolescents reporting suicidal ideation was 13.3%. The proportion of adolescents who experienced dental pain was 23.4%. Compared to adolescents who did not report dental pain, adolescents who reported experiencing dental pain were significantly more likely to experience suicidal ideation (OR=1.94, p<0.001). In two multivariate models, the relationships between dental pain and suicidal ideation (AOR=1.24, p<0.001) were statistically significant. Conclusions : Dental pain was associated with increased risk of suicidal ideation among Korean adolescents, even when controlling for sociodemographic factors and other health conditions. It is necessary to consider screening adolescent patients who present with dental pain for suicidal ideation.

Purpose: A critical indicator of the overall survival of patients with high-grade glioma is the successful isolation of tumor mesenchymal stem-like cells (tMSLCs), which play important roles in glioma progression. However, attempts to isolate tMSLCs from surgical specimens have not always been successful, and the reasons for this remain unclear. Considering that the amount of surgical high-grade glioma specimens varies, we hypothesized that larger surgical specimens would be better for tMSLC isolation. Materials and Methods: We assessed 51 fresh, high-grade glioma specimens and divided them into two groups according to the success or failure of tMSLC isolation. The success of tMSLC isolation was confirmed by plastic adherence, presenting antigens, tri-lineage differentiation, and non-tumorigenicity. Differences in characteristics between the two groups were tested using independent two sample t-tests, chi-square tests, or Kaplan-Meier survival analysis. Results: The mean specimen weights of the groups differed from each other (tMSLC-negative group: 469.9±341.9 mg, tMSLC positive group: 546.7±618.9 mg), but the difference was not statistically significant. The optimal cut-off value of specimen weight was 180 mg, and the area under the curve value was 0.599. Conclusion Our results suggested a minimum criterion for specimen collection, and found that the specimen amount was not deeply related to tMSLC detection. Collectively, our findings imply that the ability to isolate tMSLCs is determined by factors other than the specimen amount.

Purpose: A critical indicator of the overall survival of patients with high-grade glioma is the successful isolation of tumor mesenchymal stem-like cells (tMSLCs), which play important roles in glioma progression. However, attempts to isolate tMSLCs from surgical specimens have not always been successful, and the reasons for this remain unclear. Considering that the amount of surgical high-grade glioma specimens varies, we hypothesized that larger surgical specimens would be better for tMSLC isolation. Materials and Methods: We assessed 51 fresh, high-grade glioma specimens and divided them into two groups according to the success or failure of tMSLC isolation. The success of tMSLC isolation was confirmed by plastic adherence, presenting antigens, tri-lineage differentiation, and non-tumorigenicity. Differences in characteristics between the two groups were tested using independent two sample t-tests, chi-square tests, or Kaplan-Meier survival analysis. Results: The mean specimen weights of the groups differed from each other (tMSLC-negative group: 469.9±341.9 mg, tMSLC positive group: 546.7±618.9 mg), but the difference was not statistically significant. The optimal cut-off value of specimen weight was 180 mg, and the area under the curve value was 0.599. Conclusion Our results suggested a minimum criterion for specimen collection, and found that the specimen amount was not deeply related to tMSLC detection. Collectively, our findings imply that the ability to isolate tMSLCs is determined by factors other than the specimen amount.

BACKGROUND/AIMS: Endoscopic submucosal dissection (ESD) has been regarded as a curative treatment for early gastric cancer (EGC) in indicated cases. The aim of this study was to evaluate the nationwide long-term clinical outcomes of ESD for EGC in Korea. METHODS: A prospective multicenter cohort study was performed to evaluate the long-term efficacy of ESD for EGC within pre-defined indications at 12 institutes in Korea. The cases that met the expanded criteria upon pathological review after ESD were followed for 5 years. The primary outcome was 5-year disease specific free survival. RESULTS: Six hundred ninety-seven patients with 722 EGCs treated with ESD were prospectively enrolled and followed for 5 years. Complete resection was achieved in 81.3% of the cases, and curative resection was achieved in 86.1%. During the 5-year follow-up, the overall survival rate was 96.6%, and the disease specific free survival rate was 90.6%. Local recurrence developed in 0.9%, and metachronous tumor development occurred in 7.8%; both conditions were treated by endoscopic or surgical treatment. Distant metastasis developed in 0.5% during follow-up. CONCLUSIONS: ESD showed excellent long-term clinical outcomes and can be accepted as a curative treatment for patients with EGC who meet the expanded criteria in final pathology studies.
Academies and Institutes ; Cohort Studies* ; Follow-Up Studies ; Humans ; Korea ; Neoplasm Metastasis ; Pathology ; Prospective Studies* ; Recurrence ; Stomach Neoplasms* ; Survival Rate