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 test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Purpose: This study compared the diagnostic performance of two attenuation imaging (ATI) modes—low-frequency (3 MHz) and high-frequency (4 MHz)—for assessing hepatic steatosis, with histopathological hepatic fat fraction (HFF) as the reference standard. Methods: This prospective single-center study enrolled participants with suspected metabolic dysfunction-associated steatotic liver disease (MASLD) scheduled for liver biopsy or surgery between June 2023 and June 2024. Attenuation coefficient (AC) values were consecutively measured using low- and high-frequency ATI modes, while the skin-to-region of interest distance (SRD) was measured simultaneously. Spearman correlation analysis evaluated the relationships of AC with HFF and SRD, and linear regression identified factors affecting AC. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Results: In total, 119 participants (mean age, 37.2±12.0 years; 87 men) were included, with 73 (61.3%) diagnosed with MASLD. HFF ranged from 0% to 50%. The AC values in the lowfrequency mode were significantly higher than those in the high-frequency mode (0.61 vs. 0.54 dB/cm/MHz, P<0.001). HFF significantly influenced AC in both modes, whereas SRD affected AC only in the high-frequency mode (P<0.001). AC correlated positively with HFF in both modes (r_s≥0.514, P<0.001) and negatively with SRD in the high-frequency mode (r_s=-0.338, P<0.001). The AUROC for hepatic steatosis did not differ significantly between the two modes (0.751 vs. 0.771; P=0.609). Conclusion The low-frequency mode produced higher AC values than the high-frequency mode and demonstrated comparable diagnostic accuracy for assessing hepatic steatosis. Unlike the high-frequency mode, the low-frequency mode was not influenced by SRD.

Purpose: This study aimed to evaluate the prognosis of the not evaluated (NE) group by comparing it with the lost to follow-up (LTFU) group among patients with multidrug/rifampin-resistant tuberculosis (MDR/RR-TB). Materials and Methods: This was a retrospective longitudinal follow-up study using an integrated database constructed by data linkage of the three national databases. This database included 7226 cases of MDR/RR-TB notified between 2011 and 2017 in South Korea. Results: Among the 7226 MDR/RR-TB cases, 730 (10.1%) were classified as LTFU group, and 353 (4.9%) as NE group. When comparing NE group with LTFU group, there were no significant differences in the all-cause mortality rate (18.1% vs. 13.8%, p=0.065), median time to death [404 days (interquartile range, IQR 46–850) vs. 443 days (IQR 185–1157), p=0.140], and retreatment rate (26.9% vs.22.2%, p=0.090). After adjusting for potential confounders, the adjusted hazard ratio (aHR) for all-cause mortality (aHR 1.11; 95% confidence interval 0.80-1.53; p=0.531) in NE group was not significantly different than that in LTFU group. Among retreated cases, NE group had a higher treatment success rate (57.9% vs 43.8%, p=0.029) and a lower LTFU rate (11.6% vs 38.3%, p<0.001) compared to LTFU group. Conclusion NE group had an unfavorable outcome comparable to LTFU group, suggesting undetected cases of LTFU or deaths during the referral process. Establishing an efficient patient referral system would contribute to reducing the incidence of NE cases.

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 test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Endometriosis, a prevalent but debilitating condition affecting women, poses significant challenges in diagnosis and management. The current 2024 guideline, developed by the Korean Society of Endometriosis (KSE), builds upon the 2018 KSE guideline. This guideline aims to provide customized recommendations tailored to Korea’s unique clinical aspects and medical environment, and addresses key areas such as diagnosis, medical and surgical management, considerations for special populations, and its complex relationship with cancer.

Objectives: This study aimed to explore factors influencing satisfaction with medical services among medically underserved populations at the free medical clinic, providing data to improve free medical services for these populations. Methods: We employed a descriptive correlational study design involving 112 individuals (aged 19 years and older) from medically underserved populations who visited the clinic. Data were collected through face-to-face surveys from September to October 2023, and statistical analyses (t-tests, analysis of variance, Pearson correlation, and hierarchical multiple regression) were used to identify key predictors of satisfaction. Results: Perceived support from healthcare providers emerged as the strongest predictor ofsatisfaction with medical services, demonstrating a significant positive association. While socialsupport was positively correlated with perceived support from healthcare providers, it did not independently predict satisfaction. Conclusion These findings underscore the importance of healthcare provider and social supportin increasing satisfaction with medical services among medically underserved populations.Developing tailored healthcare programs and specialized healthcare provider training are essential strategies to improve healthcare access and outcomes for these vulnerable groups.

Bezoars in infants are rare owing to their limited dietary diversity. This report presents a novel case of multiple phytobezoars in the small bowel of a 3-month-old boy following ingestion of a synbiotic, a nutritional supplement combining probiotics (bacteria) and prebiotics (fibers that promote bacterial growth), which required a surgical intervention. Given the increasing use of synbiotics, the possibility of such bezoar formation should be considered, particularly in infants with underlying risk factors, such as congenital aganglionic megacolon.

Objective: In South Korea, there is a significant gap in systematic, evidence-based research on intensive case management (ICM) for individuals with severe mental illness (SMI). This study aims to evaluate the effectiveness of ICM through a randomized controlled trial (RCT) comparing ICM with standard case management (non-ICM). Methods: An RCT was conducted to assess the effectiveness of Seoul-intensive case management (S-ICM) vs. non-ICM in individuals with SMI in Seoul. A total of 78 participants were randomly assigned to either the S-ICM group (n=41) or the control group (n=37). Various clinical assessments, including the Brief Psychiatric Rating Scale (BPRS), Montgomery–Åsberg Depression Rating Scale, Health of the Nation Outcome Scale, and Clinical Global Impression-Improvement (CGI-I), along with quality-of-life measures such as the WHO Disability Assessment Schedule, WHO Quality of Life scale, and Multidimensional Scale of Perceived Social Support (MSPSS) were evaluated over a 3-month period. Statistical analyses, including analysis of covariance and logistic regression, were used to determine the effectiveness of S-ICM. Results: The S-ICM group had significantly lower odds of self-harm or suicidal attempts compared to the control group (adjusted odds ratio [aOR]=0.30, 95% confidence interval [CI]: 0.21–1.38). Psychiatric symptoms measured by the BPRS and perceived social support measured by the MSPSS significantly improved in the S-ICM group. The S-ICM group also had significantly higher odds of CGI-I compared to the control group (aOR=8.20, 95% CI: 2.66–25.32). Conclusion This study provides inaugural evidence on the effectiveness of S-ICM services, supporting their standardization and potential nationwide expansion.