Background/Aims: Adiponectin, a hormone primarily produced by adipocytes, typically shows an inverse relationship with body mass index (BMI). However, some studies have reported a positive correlation between the two. Thus, this study aimed to examine the relationship between adiponectin level and BMI in diabetic patients, focusing on the impact of past obesity on current adiponectin levels. Methods: We conducted an observational study analyzing data from 323 diabetic patients at Kyungpook National University Hospital. Based on past and current BMIs, participants were categorized into never-obese (nn, n = 106), previously obese (on, n = 43), and persistently obese (oo, n = 73) groups based on a BMI threshold of 25 kg/m2. Adiponectin level and BMI were key variables. Kaplan–Meier analysis assessed their impact on all-cause mortality up to August 2023, with survival differences based on adiponectin quartiles and follow-up starting from patient enrollment (2010–2015). Results: The analysis revealed a significant inverse correlation between adiponectin level and past maximum BMI. The on group exhibited approximately 10% lower adiponectin levels compared to the nn group. This association remained significant after adjusting for current BMI, age, and sex, highlighting the lasting influence of previous obesity on adiponectin levels. Furthermore, survival analysis indicated that patients in the lowest adiponectin quartile had reduced survival, with a statistically significant trend (p = 0.062). Conclusions Findings of this study suggest that lower adiponectin levels, potentially reflecting past obesity, are associated with decreased survival in diabetic patients, underscoring a critical role of adiponectin in long-term health outcomes.

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: To assess the feasibility of ultrafast brain magnetic resonance imaging (MRI) in pediatric patients. Materials and Methods: We retrospectively reviewed 194 pediatric patients aged 0 to 19 years (median 10.2 years) who underwent both ultrafast and conventional brain MRI between May 2019 and August 2020. Ultrafast MRI sequences included T1 and T2-weighted images (T1WI and T2WI), fluid-attenuated inversion recovery (FLAIR), T2*-weighted image (T2*WI), and diffusion-weighted image (DWI). Qualitative image quality and lesion evaluations were conducted on 5-point Likert scales by two blinded radiologists, with quantitative assessment of lesion count and size on T1WI, T2WI, and FLAIR sequences for each protocol. Wilcoxon signed-rank tests and intraclass correlation coefficient (ICC) analyses were used for comparison. Results: The total scan times for equivalent image contrasts were 1 minute 44 seconds for ultrafast MRI and 15 minutes 30 seconds for conventional MRI. Overall, image quality was lower in ultrafast MRI than in conventional MRI, with mean quality scores ranging from 2.0 to 4.8 for ultrafast MRI and 4.8 to 5.0 for conventional MRI across sequences (P < 0.001 for T1WI, T2WI, FLAIR, and T2*WI for both readers; P = 0.018 [reader 1] and 0.031 [reader 2] for DWI). Lesion detection rates on ultrafast MRI relative to conventional MRI were as follows: T1WI, 97.1%; T2WI, 99.6%; FLAIR, 92.9%; T2*WI, 74.1%; and DWI, 100%. The ICC (95% confidence interval) for lesion size measurements between ultrafast and conventional MRI was as follows: T1WI, 0.998 (0.996–0.999); T2WI, 0.998 (0.997–0.999); and FLAIR, 0.99 (0.985–0.994). Conclusion Ultrafast MRI significantly reduces scan time and provides acceptable results, albeit with slightly lower image quality than conventional MRI, for evaluating intracranial abnormalities in pediatric patients.

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.

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: To assess the feasibility of ultrafast brain magnetic resonance imaging (MRI) in pediatric patients. Materials and Methods: We retrospectively reviewed 194 pediatric patients aged 0 to 19 years (median 10.2 years) who underwent both ultrafast and conventional brain MRI between May 2019 and August 2020. Ultrafast MRI sequences included T1 and T2-weighted images (T1WI and T2WI), fluid-attenuated inversion recovery (FLAIR), T2*-weighted image (T2*WI), and diffusion-weighted image (DWI). Qualitative image quality and lesion evaluations were conducted on 5-point Likert scales by two blinded radiologists, with quantitative assessment of lesion count and size on T1WI, T2WI, and FLAIR sequences for each protocol. Wilcoxon signed-rank tests and intraclass correlation coefficient (ICC) analyses were used for comparison. Results: The total scan times for equivalent image contrasts were 1 minute 44 seconds for ultrafast MRI and 15 minutes 30 seconds for conventional MRI. Overall, image quality was lower in ultrafast MRI than in conventional MRI, with mean quality scores ranging from 2.0 to 4.8 for ultrafast MRI and 4.8 to 5.0 for conventional MRI across sequences (P < 0.001 for T1WI, T2WI, FLAIR, and T2*WI for both readers; P = 0.018 [reader 1] and 0.031 [reader 2] for DWI). Lesion detection rates on ultrafast MRI relative to conventional MRI were as follows: T1WI, 97.1%; T2WI, 99.6%; FLAIR, 92.9%; T2*WI, 74.1%; and DWI, 100%. The ICC (95% confidence interval) for lesion size measurements between ultrafast and conventional MRI was as follows: T1WI, 0.998 (0.996–0.999); T2WI, 0.998 (0.997–0.999); and FLAIR, 0.99 (0.985–0.994). Conclusion Ultrafast MRI significantly reduces scan time and provides acceptable results, albeit with slightly lower image quality than conventional MRI, for evaluating intracranial abnormalities in pediatric patients.

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.