Background: Chromosomal alterations serve as diagnostic and prognostic markers in acute leukemia. Given the evolving landscape of chromosomal abnormalities in acute leukemia, we previously studied these over two periods. In this study, we investigated the frequency of these abnormalities and clinical trends in acute leukemia in Korea across three time periods. Methods: We retrospectively analyzed data from 1,787 patients with acute leukemia (319 children and 1,468 adults) diagnosed between 2006 and 2020. Conventional cytogenetics, FISH, and multiplex quantitative PCR were used for analysis. The patient groups were divided according to the following three study periods: 2006–2009 (I), 2010–2015 (II), and 2016–2020 (III). Results: Chromosomal aberrations were detected in 92% of patients. The PML::RARA translocation was the most frequent. Over the 15-yr period, chromosomal aberrations showed minimal changes, with specific fusion transcripts being common among patients.ALL was more prevalent in children than in adults and correlated significantly with the ETV6::RUNX1 and RUNX1::RUNX1T1 aberrations. The incidence of ALL increased during the three periods, with PML::RARA remaining common. Conclusions The frequency of chromosomal abnormalities in acute leukemia has changed subtly over time. Notably, the age of onset of adult AML has continuously increased. Our results may help in establishing diagnoses and clinical treatment strategies and developing various molecular diagnostic platforms.

Purpose: Since 1995, the Korean Gastric Cancer Association (KGCA) has been periodically conducting nationwide surveys on patients with surgically treated gastric cancer. This study details the results of the survey conducted in 2023. Materials and Methods: The survey was conducted from March to December 2024 using a standardized case report form. Data were collected on 86 items, including patient demographics, tumor characteristics, surgical procedures, and surgical outcomes. The results of the 2023 survey were compared with those of previous surveys. Results: Data from 12,751 cases were collected from 66 institutions. The mean patient age was 64.6 years, and the proportion of patients aged ≥71 years increased from 9.1% in 1995 to 31.7% in 2023. The proportion of upper-third tumors slightly decreased to 16.8% compared to 20.9% in 2019. Early gastric cancer accounted for 63.1% of cases in 2023.Regarding operative procedures, a totally laparoscopic approach was most frequently applied (63.2%) in 2023, while robotic gastrectomy steadily increased to 9.5% from 2.1% in 2014.The most common anastomotic method was the Billroth II procedure (48.8%) after distal gastrectomy and double-tract reconstruction (51.9%) after proximal gastrectomy in 2023.However, the proportion of esophago-gastrostomy with anti-reflux procedures increased to 30.9%. The rates of post-operative mortality and overall complications were 1.0% and 15.3%, respectively. Conclusions The results of the 2023 nationwide survey demonstrate the current status of gastric cancer treatment in Korea. This information will provide a basis for future gastric cancer research.

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: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Emerging evidence suggests that systemic inflammation may play a critical role in neurological disorders. Recent studies have shown the connection between inflammatory bowel diseases (IBD) and neurological disorders, revealing a bidirectional relationship through the gut-brain axis.Immunotherapies, such as Treg cells infusion, have been proposed for IBD. However, the role of adaptive immune cells in IBD-induced neuroinflammation remains unclear. In this study, we established an animal model for IBD in mice with severe combined immune-deficient (SCID), an adaptive immune deficiency, to investigate the role of adaptive immune cells in IBD-induced neuroinflammation. Mice were fed 1%, 3%, or 5% dextran sulfate sodium (DSS) for 5 days. We measured body weight, colon length, disease activity index (DAI), and crypt damage. Pro-inflammatory cytokines were measured in the colon, while microglial morphology, neuronal count, and inflammatory cytokines were analyzed in the brain. In the 3% DSS group, colitis symptoms appeared at day 7, with reduced colon length and increased crypt damage showing colitis-like symptoms. By day 21, colon length and crypt damage persisted, while DAI showed recovery. Although colonic inflammation peaked at day 7, no significant increase in inflammatory cytokines or microglial hyperactivation was observed in the brain. By day 21, neuroinflammation was detected, albeit with a slight delay, in the absence of adaptive immune cells. The colitis-induced neuroinflammation model provides insights into the fundamental immune mechanisms of the gut-brain axis and may contribute to developing immune cell therapies for IBD-induced neuroinflammation.

Background/Aims: This study aimed to evaluate the performance of the Model for End-Stage Liver Disease (MELD) 3.0 for predicting mortality and liver-related complications compared with the Child-Pugh classification, albumin-bilirubin (ALBI) grade, the MELD, and the MELD sodium (MELDNa) score. Methods: We evaluated a multicenter retrospective cohort of incorporated patients with cirrhosis between 2013 and 2019. We conducted comparisons of the area under the receiver operating characteristic curve (AUROC) of the MELD3.0 and other models for predicting 3-month mortality. Additionally, we assessed the risk of cirrhosis-related complications according to the MELD3.0 score. Results: A total of 3,314 patients were included. The mean age was 55.9±11.3 years, and 70.2% of the patients were male. Within the initial 3 months, 220 patients (6.6%) died, and the MELD3.0had the best predictive performance among the tested models, with an AUROC of 0.851, outperforming the Child-Pugh classification, ALBI grade, MELD, and MELDNa. A high MELD3.0score was associated with an increased risk of mortality. Compared with that of the group with a MELD3.0 score <10 points, the adjusted hazard ratio of the group with a score of 10–20 pointswas 2.176, and that for the group with a score of ≥20 points was 4.892. Each 1-point increase inthe MELD3.0 score increased the risk of cirrhosis-related complications by 1.033-fold. The risk of hepatorenal syndrome showed the highest increase, with an adjusted hazard ratio of 1.149, followed by hepatic encephalopathy and ascites. Conclusions The MELD3.0 demonstrated robust prognostic performance in predicting mortality in patients with cirrhosis. Moreover, the MELD3.0 score was linked to cirrhosis-related complications, particularly those involving kidney function, such as hepatorenal syndrome and ascites.

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: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Emerging evidence suggests that systemic inflammation may play a critical role in neurological disorders. Recent studies have shown the connection between inflammatory bowel diseases (IBD) and neurological disorders, revealing a bidirectional relationship through the gut-brain axis.Immunotherapies, such as Treg cells infusion, have been proposed for IBD. However, the role of adaptive immune cells in IBD-induced neuroinflammation remains unclear. In this study, we established an animal model for IBD in mice with severe combined immune-deficient (SCID), an adaptive immune deficiency, to investigate the role of adaptive immune cells in IBD-induced neuroinflammation. Mice were fed 1%, 3%, or 5% dextran sulfate sodium (DSS) for 5 days. We measured body weight, colon length, disease activity index (DAI), and crypt damage. Pro-inflammatory cytokines were measured in the colon, while microglial morphology, neuronal count, and inflammatory cytokines were analyzed in the brain. In the 3% DSS group, colitis symptoms appeared at day 7, with reduced colon length and increased crypt damage showing colitis-like symptoms. By day 21, colon length and crypt damage persisted, while DAI showed recovery. Although colonic inflammation peaked at day 7, no significant increase in inflammatory cytokines or microglial hyperactivation was observed in the brain. By day 21, neuroinflammation was detected, albeit with a slight delay, in the absence of adaptive immune cells. The colitis-induced neuroinflammation model provides insights into the fundamental immune mechanisms of the gut-brain axis and may contribute to developing immune cell therapies for IBD-induced neuroinflammation.