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.

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.

Lymphoma is an uncommon type of breast malignancy, with low prevalence. The ultrasonographic findings of breast lymphoma have been described as nonspecific. Breast lymphoma most commonly appears as a solitary hypoechoic mass on US, and usually shows hypervascularity on color Doppler US. Herein, we report an unusual case of breast lymphoma that presented as multiple bilateral hyperechoic nodules on US.

Background and Objectives: Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a life-threatening inherited arrhythmogenic disorder. Recently, RYR2, the major CPVTcausative gene, was associated with neuropsychiatric manifestations. We aimed to analyze the clinical presentations, neuropsychiatric manifestations, and treatment outcomes of children with CPVT. Methods: We retrospectively reviewed 23 patients diagnosed with CPVT before 19 years of age. Genetic analysis, history of neuropsychiatric manifestations, changes in ventricular arrhythmia burden before and after treatment, occurrence of cardiac events, and overall survival (OS) were investigated. Results: RYR2 variants were identified in 17 patients, and 14 were classified as pathogenic or likely pathogenic. Neuropsychiatric manifestations, including intellectual disability and attention deficit hyperactivity disorder, were identified in 10 patients (43.5%). The 5-year cardiac event-free survival rate was 31.2%, and the 10-year OS rate was 73.1%. Patients diagnosed since 2009 had a higher cardiac event-free survival rate than those diagnosed before 2009 (p=0.0028).Combined beta-blocker and flecainide therapy demonstrated a lower risk of cardiac events than beta-blocker monotherapy (hazard ratio [HR], 0.08; 95% confidence interval [CI], 0.02–0.38;p=0.002). Left cardiac sympathetic denervation (LCSD) reduced the ventricular arrhythmia burden in Holter monitoring. Occurrence of near-fatal cardiac events after diagnosis was an independent predictor of death (HR, 33.40; 95% CI, 6.23–179.95; p<0.001). Conclusions Neuropsychiatric manifestations are common in children with CPVT. Flecainide and/or LCSD, when added to beta-blocker therapy, reduce the ventricular arrhythmia burden and cardiac events, thereby improving treatment outcomes in recent years.

Purpose: The leaves of Perilla frutescens var. acuta (PFA) are generally reported to have antioxidant, anti-allergic, anti-inflammatory, and antitumor effects and commonly used as a traditional medicine in East Asia. This study aimed to investigate the protective effect and antioxidant activity of PFA on busulfan-induced testicular dysfunction, histological damage, oxidative stress (OS), sperm quality, and hormone levels using a mouse model. Materials and Methods: C57BL/6 male mice were divided into four groups: control, busulfan-only treated, and varying concentrations of PFA (100 and 200 mg/kg) with busulfan. In the busulfan group, 40 mg/kg of busulfan was intraperitoneally injected to induce azoospermia. Mice were orally administered PFA for 35 consecutive days after busulfan administration.Samples were collected and assessed for testis/body weight, testicular histopathology, sperm quality, serum hormone levels, and OS to evaluate the effects of PFA treatment on spermatogenesis dysfunction induced by busulfan. Results: The busulfan-induced testicular dysfunction model showed reduced testis weight, adverse histological changes, significantly decreased sex hormones and sperm quality, and attenuated OS. These results indicate that PFA treatment significantly increased testis weight, testis/body weight, epididymal sperm count, motility, and testosterone level compared with busulfan alone. PFA treatment also attenuated the busulfan-induced histological changes. Furthermore, compared with mice treated with busulfan alone, PFA supplementation upregulated the testicular mRNA expression of the antioxidant enzymes superoxide dismutase 1 (Sod1) and glutathione peroxidase 1 (Gpx1), with a decrease in malondialdehyde (MDA) production and an increase in SOD and GPx activities. Conclusions This study shows that PFA exerts a protective effect against testicular damage by attenuating OS induced by busulfan. Our results suggest that PFA is a potentially relevant drug used to decrease the side effects induced by busulfan on testicular function and sperm during cancer chemotherapy.

Purpose: Robotic single-site plus one-port myomectomy (RSOM) was designed to reduce the number of incision sites for greater cosmetic satisfaction of patients while retaining the benefits of conventional robotic multi-site myomectomy (CRM). Robotic single-site plus two-port myomectomy (RSTM) eliminated one port relative to conventional CRM, and RSOM achieved the same advantage with respect to RSTM. This study aimed to compare RSOM with RSTM in terms of their respective methodologies and surgical outcomes. Materials and Methods: The medical records of 230 patients who had undergone RSOM and 146 patients who had undergone RSTM were reviewed. The groups’ surgical outcomes were compared using propensity score matching (PSM) analysis. Results: In the total data, RSOM had a shorter operative time (135.1±57.4 min vs. 149.9±46.2 min, p=0.009) and a shorter hospital stay (5.2±0.5 days vs. 5.4±0.7 days, p=0.033) relative to RSTM. The PSM analysis showed that there were no statistically significant intergroup differences in the patients’ baseline characteristics. Regarding the surgical outcomes, the RSOM group showed shorter operative time (129.2±49.3 min vs. 148.7±46.3 min, p=0.001) compared to the RSTM group. Conclusion Compared with RSTM, RSOM was associated with shorter operative time. Additionally, more detailed comparative and prospective studies are needed to evaluate RSOM relative to RSTM.