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.

Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

Objective: To establish local diagnostic reference levels (DRLs) for pediatric neck CT based on age, weight, and water-equivalent diameter (WED) across multiple university hospitals in South Korea. Materials and Methods: This retrospective study analyzed pediatric neck CT examinations from nine university hospitals, involving patients aged 0–18 years. Data were categorized by age, weight, and WED, and radiation dose metrics, including volume CT dose index (CTDIvol) and dose length product, were recorded. Data retrieval and analysis were conducted using a commercially available dose-management system (Radimetrics, Bayer Healthcare). Local DRLs were established following the International Commission on Radiological Protection guidelines, using the 75th percentile as the reference value. Results: A total of 1159 CT examinations were analyzed, including 169 scans from Institution 1, 132 from Institution 2, 126 from Institution 3, 129 from Institution 4, 128 from Institution 5, 105 from Institution 6, 162 from Institution 7, 127 from Institution 8, and 81 from Institution 9. Radiation dose metrics increased with age, weight, and WED, showing significant variability both within and across institutions. For patients weighing less than 10 kg, the DRL for CTDIvol was 5.2 mGy. In the 10–19 kg group, the DRL was 5.8 mGy; in the 20–39 kg group, 7.6 mGy; in the 40–59 kg group, 11.0 mGy; and for patients weighing 60 kg or more, 16.2 mGy. DRLs for CTDIvol by age groups were as follows: 5.3 mGy for infants under 1 year, 5.7 mGy for children aged 1–4 years, 7.6 mGy for ages 5–9 years, 11.2 mGy for ages 10–14 years, and 15.6 mGy for patients 15 years or older. Conclusion Local DRLs for pediatric neck CT were established based on age, weight, and WED across nine university hospitals in South Korea.

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.

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.

Radiofrequency ablation (RFA) is a minimally invasive treatment modality used as an alternative to surgery in patients with benign thyroid nodules, recurrent thyroid cancers (RTCs), and primary thyroid microcarcinomas. The Korean Society of Thyroid Radiology (KSThR) initially developed recommendations for the optimal use of RFA for thyroid tumors in 2009 and revised them in 2012 and 2017. As new meaningful evidence has accumulated since 2017 and in response to a growing global interest in the use of RFA for treating malignant thyroid lesions, the task force committee members of the KSThR decided to update the guidelines on the use of RFA for the management of RTCs based on a comprehensive analysis of current literature and expert consensus.

Objective: To establish local diagnostic reference levels (DRLs) for pediatric neck CT based on age, weight, and water-equivalent diameter (WED) across multiple university hospitals in South Korea. Materials and Methods: This retrospective study analyzed pediatric neck CT examinations from nine university hospitals, involving patients aged 0–18 years. Data were categorized by age, weight, and WED, and radiation dose metrics, including volume CT dose index (CTDIvol) and dose length product, were recorded. Data retrieval and analysis were conducted using a commercially available dose-management system (Radimetrics, Bayer Healthcare). Local DRLs were established following the International Commission on Radiological Protection guidelines, using the 75th percentile as the reference value. Results: A total of 1159 CT examinations were analyzed, including 169 scans from Institution 1, 132 from Institution 2, 126 from Institution 3, 129 from Institution 4, 128 from Institution 5, 105 from Institution 6, 162 from Institution 7, 127 from Institution 8, and 81 from Institution 9. Radiation dose metrics increased with age, weight, and WED, showing significant variability both within and across institutions. For patients weighing less than 10 kg, the DRL for CTDIvol was 5.2 mGy. In the 10–19 kg group, the DRL was 5.8 mGy; in the 20–39 kg group, 7.6 mGy; in the 40–59 kg group, 11.0 mGy; and for patients weighing 60 kg or more, 16.2 mGy. DRLs for CTDIvol by age groups were as follows: 5.3 mGy for infants under 1 year, 5.7 mGy for children aged 1–4 years, 7.6 mGy for ages 5–9 years, 11.2 mGy for ages 10–14 years, and 15.6 mGy for patients 15 years or older. Conclusion Local DRLs for pediatric neck CT were established based on age, weight, and WED across nine university hospitals in South Korea.

Objective: This study aims to understand the structure of meaning in life among patients with cancer through the validation of the Meaning in Life Scale among Korean patients (K-MiLS) with cancer. Methods: From August 2021 to November 2022, participants were recruited from multiple sites in South Korea. Participants completed related questionnaires, including the MiLS, on the web or mobile. Test-retest reliability was assessed between 2 and 4 weeks after the initial assessment. Exploratory and confirmatory factor analyses and Pearson’s correlations were used to evaluate the reliability and validity of the MiLS. A multiple regression analysis was conducted to examine the sociodemographic and disease-related variables correlated with the MiLS. Regarding concurrent validity, a hierarchical regression analysis was performed. Results: The results (n=345) indicated that the K-MiLS has a four-factor structure: Harmony and Peace; Life Perspective, Purpose, and Goals; Confusion and Lessened Meaning; and Benefits of Spirituality. Regarding convergent and discriminant validity, K-MiLS was negatively correlated with Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, and Fear of Cancer Recurrence Inventory while showing a significantly positive correlation with the Posttraumatic Growth Inventory, Self-Compassion Scale, Functional Assessment of Cancer Therapy-General, and Functional Social Support Questionnaire. Hierarchical regression analysis revealed that the demographic variable influencing MiLS was religious affiliation. Conclusion The K-MiLS had a multidimensional four-factor structure similar to that of the original version. It is also a reliable and valid measure for assessing cancer survivors’ meaning in life after a cancer diagnosis.

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: 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.