Incidentally detected gallbladder polyps (GBPs) and gallbladder wall thickening (GBWT) are frequently encountered in clinical practice. However, characterizing GBPs and GBWT in asymptomatic patients can be challenging and may result in overtreatment, including unnecessary follow-ups or surgeries. The Korean Society of Abdominal Radiology (KSAR) Clinical Practice Guideline Committee has developed expert recommendations that focus on standardized imaging interpretation and follow-up strategies for both GBPs and GBWT, with support from the Korean Society of Radiology and KSAR. These guidelines, which address 24 key questions, aim to standardize the approach for the interpretation of imaging findings, reporting, imaging-based workups, and surveillance of incidentally detected GBPs and GBWT. This recommendation promotes evidence-based practice, facilitates communication between radiologists and referring physicians, and reduces unnecessary interventions.

This guideline outlines the use of 18F-fluoro-2-deoxyglucose positron emission tomography / computed tomography for the diagnosis and management of infectious and inflammatory diseases. It provides detailed recommendations for healthcare providers on patient preparation, imaging procedures, and the interpretation of results. Adapted from international standards and tailored to local clinical practices, the guideline emphasizes safety, quality control, and effective use of the technology in various conditions, including spinal infections, diabetic foot, osteomyelitis, vasculitis, and cardiac inflammation. The aim is to assist nuclear medicine professionals in delivering accurate diagnoses and improving patient outcomes while allowing flexibility to adapt to individual patient needs, technological advancements, and evolving medical knowledge. This document is a comprehensive resource for enhancing the quality and safety of 18F-fluoro-2-deoxyglucose positron emission tomography / computed tomography for the evaluation of infectious and inflammatory diseases.Preamble The Korean Society of Nuclear Medicine (KSNM) was established in 1961 to promote the clinical and technological advancement of nuclear medicine in South Korea, with members that include nuclear medicine physicians and associated scientists. The KSNM regularly formulates and revises procedural guidelines for nuclear medicine examinations to enhance the field and improve the quality of patient care. These guidelines are designed to support healthcare professionals in providing appropriate medical care to patients. However, they are not immutable rules or mandatory requirements for conducting examinations.Therefore, KSNM states that these guidelines should not be used in legal actions challenging a healthcare professional’s medical decisions. The ultimate judgment regarding specific procedures or appropriate measures should be made by nuclear medicine physicians, considering the unique circumstances of each case. Deviation from these guidelines does not imply substandard medical practice. Rather, reasonable judgments differing from the guidelines can be made based on the patient’s condition, available resources, and advancements in knowledge or technology. Due to the diversity and complexity of patients, it is often challenging to predict the most appropriate diagnostic and accurate therapeutic responses. Thus, adherence to these guidelines does not always guarantee an exact diagnosis or successful outcomes.The purpose of this guideline is to assist healthcare providers in making reasonable decisions and conducting effective and safe examinations based on current medical knowledge, available resources, and patient needs when performing 18F-fluoro-2-deoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) examinations for infectious/ inflammatory diseases.

Background: Histone deacetylase (HDAC) inhibition offers potential anticancer effects across diverse cancers due to HDAC's significant role in cancer development and progression. Consequently, we demonstrated the therapeutic efficacy of the novel HDAC inhibitor, CG-745, in comparison with existing inhibitors such as suberoylanilide hydroxamic acid (SAHA) in non-small cell lung cancer (NSCLC) cells. Methods: CG-745's effect on apoptosis and reactive oxygen species (ROS)-dependent mitochondrial dysfunction was investigated using annexin V assay, MitoSoX, and Western blot in human A549 and H460 cells. Additionally, HDAC expression was analyzed through real-time polymerase chain reaction. We also evaluated the inhibitory effect of CG-745 on epithelial-mesenchymal transition (EMT) induced by transforming growth factor β1 (TGF-β1) via Western blot, scratch analysis, and matrigel invasion analysis. Results: Compared to SAHA, CG-745 inhibited cell viability and mRNA expression of HDACs such as HDAC1, HDAC2, HDAC3, and HDAC8. It also induced apoptosis, ROS, and mitochondrial dysfunction in a concentration-dependent manner. CG-745 reversed EMT triggered by TGF-β1 in A549 and H460 cells, and curtailed the migration and invasion enhanced by TGF-β1. CG-745 has demonstrably inhibited EMT and induced apoptosis in NSCLC cells. Conclusion CG-745 may represent a novel therapeutic strategy for NSCLC treatment.

Purpose: The study investigated whether incorporating magnetic resonance imaging (MRI) alongside ultrasonography (US) in the preoperative evaluation is associated with differing survival outcomes between male and female breast cancer patients in a matched analysis. Additionally, clinicopathological prognostic factors were analyzed. Methods: Between January 2005 and December 2020, 93 male and 28,191 female patients who underwent breast surgery were screened. Exact matching analysis was conducted for age, pathologic T and N stages, and molecular subtypes. The clinicopathological characteristics and preoperative imaging methods of the matched cohorts were reviewed. Disease-free survival (DFS) and overall survival (OS) were assessed using Kaplan-Meier analysis, and Cox proportional hazards regression analysis was used to identify prognostic factors. Results: A total of 328 breast cancer patients (61 men and 267 women) were included in the matched analysis. Male patients had worse DFS (10-year DFS, 70.6% vs. 89.2%; P=0.001) and OS (10-year OS, 64.4% vs. 96.3%; P<0.001) than female patients. The pathologic index cancer size (hazard ratio [HR], 2.013; 95% confidence interval [CI], 1.063 to 3.810; P=0.032) was associated with worse DFS, whereas there were no significant factors associated with OS. Adding MRI to US for preoperative evaluation was not associated with DFS (HR, 1.117; 95% CI, 0.223 to 5.583; P=0.893) or OS (HR, 1.529; 95% CI, 0.300 to 7.781; P=0.609) in male patients. Conclusion Adding breast MRI to US in the preoperative evaluation was not associated with survival outcomes in male breast cancer patients, and the pathologic index cancer size was associated with worse DFS.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Background: Histone deacetylase (HDAC) inhibition offers potential anticancer effects across diverse cancers due to HDAC's significant role in cancer development and progression. Consequently, we demonstrated the therapeutic efficacy of the novel HDAC inhibitor, CG-745, in comparison with existing inhibitors such as suberoylanilide hydroxamic acid (SAHA) in non-small cell lung cancer (NSCLC) cells. Methods: CG-745's effect on apoptosis and reactive oxygen species (ROS)-dependent mitochondrial dysfunction was investigated using annexin V assay, MitoSoX, and Western blot in human A549 and H460 cells. Additionally, HDAC expression was analyzed through real-time polymerase chain reaction. We also evaluated the inhibitory effect of CG-745 on epithelial-mesenchymal transition (EMT) induced by transforming growth factor β1 (TGF-β1) via Western blot, scratch analysis, and matrigel invasion analysis. Results: Compared to SAHA, CG-745 inhibited cell viability and mRNA expression of HDACs such as HDAC1, HDAC2, HDAC3, and HDAC8. It also induced apoptosis, ROS, and mitochondrial dysfunction in a concentration-dependent manner. CG-745 reversed EMT triggered by TGF-β1 in A549 and H460 cells, and curtailed the migration and invasion enhanced by TGF-β1. CG-745 has demonstrably inhibited EMT and induced apoptosis in NSCLC cells. Conclusion CG-745 may represent a novel therapeutic strategy for NSCLC treatment.

Purpose: The study investigated whether incorporating magnetic resonance imaging (MRI) alongside ultrasonography (US) in the preoperative evaluation is associated with differing survival outcomes between male and female breast cancer patients in a matched analysis. Additionally, clinicopathological prognostic factors were analyzed. Methods: Between January 2005 and December 2020, 93 male and 28,191 female patients who underwent breast surgery were screened. Exact matching analysis was conducted for age, pathologic T and N stages, and molecular subtypes. The clinicopathological characteristics and preoperative imaging methods of the matched cohorts were reviewed. Disease-free survival (DFS) and overall survival (OS) were assessed using Kaplan-Meier analysis, and Cox proportional hazards regression analysis was used to identify prognostic factors. Results: A total of 328 breast cancer patients (61 men and 267 women) were included in the matched analysis. Male patients had worse DFS (10-year DFS, 70.6% vs. 89.2%; P=0.001) and OS (10-year OS, 64.4% vs. 96.3%; P<0.001) than female patients. The pathologic index cancer size (hazard ratio [HR], 2.013; 95% confidence interval [CI], 1.063 to 3.810; P=0.032) was associated with worse DFS, whereas there were no significant factors associated with OS. Adding MRI to US for preoperative evaluation was not associated with DFS (HR, 1.117; 95% CI, 0.223 to 5.583; P=0.893) or OS (HR, 1.529; 95% CI, 0.300 to 7.781; P=0.609) in male patients. Conclusion Adding breast MRI to US in the preoperative evaluation was not associated with survival outcomes in male breast cancer patients, and the pathologic index cancer size was associated with worse DFS.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Background: s/Aims: Hepatocellular carcinoma (HCC) is the sixth most common cancer and second leading cause of cancer-related deaths in South Korea. This study evaluated the characteristics of Korean patients newly diagnosed with HCC in 2016-2018. Methods: Data from the Korean Primary Liver Cancer Registry (KPLCR), a representative database of patients newly diagnosed with HCC in South Korea, were analyzed. This study investigated 4,462 patients with HCC registered in the KPLCR in 2016-2018. Results: The median patient age was 63 years (interquartile range, 55-72). 79.7% of patients were male. Hepatitis B infection was the most common underlying liver disease (54.5%). The Barcelona Clinic Liver Cancer (BCLC) staging system classified patients as follows: stage 0 (14.9%), A (28.8%), B (7.5%), C (39.0%), and D (9.8%). The median overall survival was 3.72 years (95% confidence interval, 3.47-4.14), with 1-, 3-, and 5-year overall survival rates of 71.3%, 54.1%, and 44.3%, respectively. In 2016-2018, there was a significant shift toward BCLC stage 0-A and Child-Turcotte-Pugh liver function class A (P<0.05), although survival rates did not differ by diagnosis year. In the treatment group (n=4,389), the most common initial treatments were transarterial therapy (31.7%), surgical resection (24.9%), best supportive care (18.9%), and local ablation therapy (10.5%). Conclusions Between 2016 and 2018, HCC tended to be diagnosed at earlier stages, with better liver function in later years. However, since approximately half of the patients remained diagnosed at an advanced stage, more rigorous and optimized HCC screening strategies should be implemented.