Purpose: To investigate ocular manifestation of immune reconstitution inflammatory syndrome (IRIS) in HIV patients after starting highly active antiretroviral therapy (HAART) and its relationship to T cell immunity. Methods: HIV patients with ocular IRIS after HAART were retrospectively reviewed. Clinical presentations with previous opportunistic infection, duration from initiation of HAART to IRIS, blood CD4+, CD8+ T cell count, and HIV RNA copies before HAART and at IRIS were analyzed. Results: Among 19 patients (27 eyes) included, the most common previous opportunistic infection was cytomegalovirus (17 patients, 89.5%) followed by tuberculosis choroiditis (2 patients, 10.5%). The clinical manifestations included vitritis (20 eyes, 74.0%), retinitis (14 eyes, 51.9%), and anterior uveitis (5 eyes, 18.5%). The median duration from initiation of HAART to IRIS was 70 days. CD4+ T cell count before HAART increased at IRIS (p < 0.001). CD8+ T cell count before HAART was negatively correlated with duration from HAART to IRIS (p < 0.001). The cutoff value of CD8+ T cell count for discerning early or late onset of ocular IRIS was 258/mm3 (p = 0.001). When divided into two groups by CD8+ T cell count of 258/mm3, 90% patients with CD8+ T cell count higher than 258/mm3 before HAART developed ocular IRIS within 70 days. Conclusions There was a negative relationship between CD8+ T cell count before HAART and duration from HAART to ocular IRIS. Ocular IRIS with higher CD8+ T cell count before HAART developed earlier after HAART initiation compared to those with lower CD8+ T cell count.

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.

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 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.

Purpose: This retrospective cohort study aimed to investigate the association between the use of pain medications with varying cyclooxygenase-2 (COX-2) selectivity and the incidence of endometriosis (EMS) in women. Materials and Methods: Medical records from January 1, 1994, to December 31, 2022, were retrospectively analyzed. The cohort included 33406 patients diagnosed with any pain-related condition who were prescribed either selective COX-2 inhibitors or nonsteroidal anti-inflammatory drugs (NSAIDs). Patients were followed for up to 5 years from the cohort entry date. The incidence of EMS was compared between the two medication groups using Cox proportional hazards models, adjusting for confounding factors such as age, past drug use, and prior diagnosis. Results: The incidence rates of EMS were 3.00 per 1000 person-years in the COX-2 inhibitor group and 3.97 per 1000 person-years in the NSAIDs group. After adjustment for confounders, the hazard ratio for EMS incidence in the COX-2 inhibitor group compared to the NSAIDs group was 0.77 [95% confidence interval (CI), 0.63 to 0.93; p<0.01], indicating a significantly lower risk in the COX-2 inhibitor group. Subgroup analysis revealed that this association was particularly significant in younger women aged 20– 44 years, with a hazard ratio of 0.71 (95% CI, 0.54 to 0.95; p<0.05) in this age group. Conclusion The findings suggest that COX-2 inhibitors may reduce the incidence of EMS compared to traditional NSAIDs, highlighting their potential as a strategic option for managing EMS, particularly among younger women. Further prospective studies are needed to confirm these findings.

Purpose: To investigate ocular manifestation of immune reconstitution inflammatory syndrome (IRIS) in HIV patients after starting highly active antiretroviral therapy (HAART) and its relationship to T cell immunity. Methods: HIV patients with ocular IRIS after HAART were retrospectively reviewed. Clinical presentations with previous opportunistic infection, duration from initiation of HAART to IRIS, blood CD4+, CD8+ T cell count, and HIV RNA copies before HAART and at IRIS were analyzed. Results: Among 19 patients (27 eyes) included, the most common previous opportunistic infection was cytomegalovirus (17 patients, 89.5%) followed by tuberculosis choroiditis (2 patients, 10.5%). The clinical manifestations included vitritis (20 eyes, 74.0%), retinitis (14 eyes, 51.9%), and anterior uveitis (5 eyes, 18.5%). The median duration from initiation of HAART to IRIS was 70 days. CD4+ T cell count before HAART increased at IRIS (p < 0.001). CD8+ T cell count before HAART was negatively correlated with duration from HAART to IRIS (p < 0.001). The cutoff value of CD8+ T cell count for discerning early or late onset of ocular IRIS was 258/mm3 (p = 0.001). When divided into two groups by CD8+ T cell count of 258/mm3, 90% patients with CD8+ T cell count higher than 258/mm3 before HAART developed ocular IRIS within 70 days. Conclusions There was a negative relationship between CD8+ T cell count before HAART and duration from HAART to ocular IRIS. Ocular IRIS with higher CD8+ T cell count before HAART developed earlier after HAART initiation compared to those with lower CD8+ T cell count.

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.

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.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.