Purpose: This 6-year post-marketing surveillance (PMS) study was conducted in South Korea to evaluate the real-world safety and effectiveness of eribulin in patients with advanced or metastatic breast cancer previously treated with anthracyclines and taxanes. Materials and Methods: During the study period (17 August 2012 to 16 August 2018), case-report files (CRFs) of patients receiving eribulin were collected. The main study endpoint was to assess the safety of eribulin. Evaluation of the effectiveness of eribulin was an exploratory endpoint. Patients were followed for 1 year after eribulin initiation. Results: CRFs were collected from 64 investigators at 64 sites for 1,079 patients. The safety analysis set (SAS) included 1,001 eribulin recipients; effectiveness was assessed in 244 patients. In the SAS, patients were predominantly female (99.6%), with a median age of 53.0 years, and diagnosed with metastatic breast cancer (92.0%). Eribulin was administered as a median 4th line chemotherapy. A total of 2,124 treatment-emergent adverse events (TEAEs) were reported in 661 patients (66.0%). Neutropenia was the most common TEAE (32.5% of patients), occurring at a median of 9-11 days from initial eribulin administration. Overall response and disease control rates were 31.7% and 95.6%, respectively, and the median duration of eribulin use (time to treatment failure) was 3.0 months. Conclusion This large real-world PMS analysis in patients with advanced or metastatic breast cancer demonstrated the effectiveness of eribulin and found no new safety concerns relative to safety information from prior clinical and real-world studies, and approvals in South Korea and other countries.

In patients with differentiated thyroid cancer (DTC), initial recurrence risk stratification based on clinical, histopathological, and perioperative data remains the key determinant for guiding management strategies during the first 1-2 years post-treatment. However, the adoption of ongoing risk stratification (ORS), which dynamically reassesses risk by integrating longitudinal clinical data and treatment response, enables more precise long-term prognostic assessment and facilitates highly individualized management. Building upon recent guidelines, the 2026 KTA guideline has been further refined by incorporating robust evidence from large-scale national cohorts and comprehensive systematic reviews. These updated recommendations outline contemporary concepts of ORS, risk-adapted TSH suppression targets, optimized surveillance modalities for recurrence detection, and disease-specific long-term follow-up strategies. Reflecting the paradigm shift toward de-escalated treatment, this revision integrates evolved perspectives on TSH suppression intensity, the clinical interpretation of thyroglobulin levels, and tailored follow-up intervals. These evidence-based recommendations aim to minimize unnecessary treatment and excessive surveillance in the large proportion of patients with excellent prognosis after initial therapy, while ensuring that each patient receives appropriately tailored and effective long-term management.

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.

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.

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.

Percutaneous renal denervation(RDN)is a minimally invasive interventional treatment for hypertension that has been used in clinics at home and abroad,primarily for patients with resistant hypertension or who cannot tolerate antihypertensive drugs.Current clinical studies have confirmed the safety and efficacy of this technique.As RDN is still in its promotional and popularization stage,the expert group has written this clinical pathway to promote the orderly implementation of this technology,standardize the operating process,and standardize diagnostic and therapeutic behaviors.The pathway describes the clinical assessment of patients in detail(including indications,contraindications,and the patient screening process),the standardized operating process for RDN(including perioperative medication,anaesthesia,ablation,and management of complications),and the postoperative follow-up plan(including follow-up content,methods,and timing).It is hoped that this will serve as a reference for physicians involved in RDN clinical work,standardize diagnostic and therapeutic behaviors,and promote the standardized,precise,and sustainable development of the RDN discipline.

Objective This study aims to conduct a comprehensive analysis of the domestic and international standards for prefilled syringes to advance the scientific and international alignment of China's PFS standard system.Methods A thorough review of the framework,scope,and quality control key points of the 2015 national pharmaceutical packaging material standards(abbreviated as YBB standards),the ISO 11040 international standard system and the United States Pharmacopeia standard system was conducted.Comparative analysis was employed to reveal the similarities and differences among the three,thereby uncovering the unique characteristics of each.Results The YBB standards emphasize quality control for prefilled syringes,offering clear and operational guidelines that provide companies with precise directives for production and testing.However,the scope of these standards is relatively limited.ISO standards apply to a wide range of products,featuring comprehensive coverage and a degree of flexibility.They emphasize the universality of products and compatibility with other international standards,which facilitates global production and trade.USP includes several chapters related to prefilled syringes but lacks a certain level of specificity and systematic approach.Conclusion By thoroughly studying and drawing upon the quality control philosophies,standard frameworks,and key control points of prefilled syringes from both domestic and international standards,we aim to provide valuable references for the development of a robust prefilled syringe standard system within the Chinese Pharmacopoeia.

Objective: To investigate the relationship between serum alanine transaminase (ALT) and carotid intima media thickness (cIMT) in adolescents, providing evidence for early prevention and control strategies for cardiovascular structural abnormalities in adolescents. Methods: Data were obtained from the third follow up survey (from October to November 2023) of the "Huantai Children Cardiovascular Health Cohort", including 1 153 healthy adolescents with complete information. Analysis of variance (ANOVA) was used to compare cIMT across different ALT level groups, and multiple linear regression was employed to analyze the relationship between serum ALT levels and cIMT. Results: Significant differences in cIMT were observed among Q 1, Q 2, and Q 3 ALT level groups [(0.56±0.04) (0.57±0.04) (0.59±0.04)mm, respectively; F=3.61, P <0.01]. After adjusting for covariates, multiple linear regression revealed a positive correlation between ALT levels and cIMT ( β=0.18, P <0.01). Gender subgroup analysis showed similar results in males ( β=0.19, P <0.01), but no statistically significant association was found in females ( β=0.07, P = 0.54). Conclusions Elevated serum ALT levels are associated with an increased risk of vascular structural damage in adolescents, especially in boys. Early detection and control of abnormal serum ALT levels can help to reduce early vascular structural damage and further reduce the risk of adverse cardiovascular events.

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.

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.