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: This study evaluated the use of brainstem score (BSS) on pre-procedural diffusion-weighted imaging (DWI) to predict outcomes after mechanical thrombectomy (MT) in acute basilar artery occlusion (ABAO) patients and compared its predictive effectiveness to the critical area perfusion score (CAPS) on perfusion magnetic resonance imaging (MRI) using RAPID. Methods: This study focused on ABAO patients who underwent MT after MRI at Dong-A University Hospital from 2013 to 2023. Ischemic lesion volume and DWI BSS were measured for all. For the group that underwent perfusion MRI using RAPID, CAPS were measured. The primary end point was a poor outcome at 90 days (modified Rankin scale [mRS], >2). Results: 71 patients had ABAO and underwent MT after MRI. The poor outcome group (66.2%) had significantly larger ischemic lesion volume and higher DWI BSS compared with the good outcome group. In the multiple logistic regression analysis, DWI BSS (odds ratio, 8.27; 95% confidence interval, 1.93-35.50; p<0.01) was an independent predictor of poor outcomes. In 26 patients, CAPS was measured on perfusion MRI. In this subgroup, poor outcome group (50.0%) had higher DWI BSS and CAPS than the good outcome group. In the multiple logistic regression analysis, DWI BSS remained a valid independent predictor for predicting outcomes, but CAPS did not function as an independent predictor. Conclusion In this study, the DWI BSS before MT in ABAO patients emerged as a useful imaging marker for predicting post-procedural outcomes. Its predictive ability is not only comparable to but even superior to CAPS on perfusion MRI.

Background: This study evaluated the use of brainstem score (BSS) on pre-procedural diffusion-weighted imaging (DWI) to predict outcomes after mechanical thrombectomy (MT) in acute basilar artery occlusion (ABAO) patients and compared its predictive effectiveness to the critical area perfusion score (CAPS) on perfusion magnetic resonance imaging (MRI) using RAPID. Methods: This study focused on ABAO patients who underwent MT after MRI at Dong-A University Hospital from 2013 to 2023. Ischemic lesion volume and DWI BSS were measured for all. For the group that underwent perfusion MRI using RAPID, CAPS were measured. The primary end point was a poor outcome at 90 days (modified Rankin scale [mRS], >2). Results: 71 patients had ABAO and underwent MT after MRI. The poor outcome group (66.2%) had significantly larger ischemic lesion volume and higher DWI BSS compared with the good outcome group. In the multiple logistic regression analysis, DWI BSS (odds ratio, 8.27; 95% confidence interval, 1.93-35.50; p<0.01) was an independent predictor of poor outcomes. In 26 patients, CAPS was measured on perfusion MRI. In this subgroup, poor outcome group (50.0%) had higher DWI BSS and CAPS than the good outcome group. In the multiple logistic regression analysis, DWI BSS remained a valid independent predictor for predicting outcomes, but CAPS did not function as an independent predictor. Conclusion In this study, the DWI BSS before MT in ABAO patients emerged as a useful imaging marker for predicting post-procedural outcomes. Its predictive ability is not only comparable to but even superior to CAPS on perfusion MRI.

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: This study evaluated the use of brainstem score (BSS) on pre-procedural diffusion-weighted imaging (DWI) to predict outcomes after mechanical thrombectomy (MT) in acute basilar artery occlusion (ABAO) patients and compared its predictive effectiveness to the critical area perfusion score (CAPS) on perfusion magnetic resonance imaging (MRI) using RAPID. Methods: This study focused on ABAO patients who underwent MT after MRI at Dong-A University Hospital from 2013 to 2023. Ischemic lesion volume and DWI BSS were measured for all. For the group that underwent perfusion MRI using RAPID, CAPS were measured. The primary end point was a poor outcome at 90 days (modified Rankin scale [mRS], >2). Results: 71 patients had ABAO and underwent MT after MRI. The poor outcome group (66.2%) had significantly larger ischemic lesion volume and higher DWI BSS compared with the good outcome group. In the multiple logistic regression analysis, DWI BSS (odds ratio, 8.27; 95% confidence interval, 1.93-35.50; p<0.01) was an independent predictor of poor outcomes. In 26 patients, CAPS was measured on perfusion MRI. In this subgroup, poor outcome group (50.0%) had higher DWI BSS and CAPS than the good outcome group. In the multiple logistic regression analysis, DWI BSS remained a valid independent predictor for predicting outcomes, but CAPS did not function as an independent predictor. Conclusion In this study, the DWI BSS before MT in ABAO patients emerged as a useful imaging marker for predicting post-procedural outcomes. Its predictive ability is not only comparable to but even superior to CAPS on perfusion MRI.

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.

Radiofrequency ablation (RFA) is progressively expanding its application beyond benign thyroid nodules to include malignant thyroid nodules. However, performing thyroidectomy after RFA in the treatment of malignant thyroid nodules, particularly in the context of histopathological analysis and procedural challenges is not well-documented. Here, we present a case of a 50-year-old female patient who underwent RFA based on ultrasound findings suggesting a benign nature, but subsequent fine needle aspiration results confirmed the presence of papillary thyroid carcinoma (PTC), leading to the decision for surgery.Intraoperative findings demonstrated the presence of inflammation and fibrosis attributable to the previous RFA procedure; however, these sequelae did not engender any technical impediments during the surgical intervention, except for adhesions between the thyroid gland and the strap muscles. This case presents intriguing histologic features from a complete specimen for PTC treated with RFA. While further research is warranted, our findings cautiously suggest that RFA may serve as an alternative therapy for PTC cases requiring surgery, as no significant challenges were observed in subsequent surgical procedures.

Objective: Body scan meditation is a popular mindfulness practice in which a person directs their attention toward internal bodily sensations. Although its neural mechanisms have been investigated using functional magnetic resonance imaging, few studies have used functional near-infrared spectroscopy (fNIRS) to directly measure prefrontal networks during body scan meditation. Methods: In this study, symptoms of depression and anxiety were measured in 40 healthy young adults without prior meditation experience. Participants’ prefrontal networks were evaluated using fNIRS during body scan meditation and resting with nature sounds. Results: Analyses of fNIRS data revealed significant positive prefrontal network connectivity in both conditions, with greater connectivity between the dorsolateral prefrontal cortex and medial prefrontal cortex observed when participants were resting with nature sounds than during body scan meditation. Correlation analyses showed that the left dorsolateral superior frontal gyrus–right medial superior frontal gyrus connectivity during body scan meditation was negatively associated with depressive and anxiety symptoms and positively associated with emotion regulation abilities. Conclusion Enhanced prefrontal networks induced by meditation may have therapeutic implications for mental health.The fNIRS findings, which measured direct changes in prefrontal networks during body scan meditation, could serve as a cornerstone for understanding the neural correlates.