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.

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.

Purpose: We investigated the association between social support, metabolic syndrome, and incident cardio-cerebrovascular disease (CCVD) in rural Koreans aged ≥50 years. Materials and Methods: We conducted a prospective study using the Korean Genome and Epidemiology Study on Atherosclerosis Risk of Rural Areas in the Korean General Population (KoGES-ARIRANG) dataset. From the baseline of 5169 adults, 1682 participants were finally included according to the exclusion criteria. For outcomes, myocardial infarction, angina, and stroke were included. For independent variables, the social support score and metabolic syndrome were used. Descriptive statistics and multivariate logistic regression were performed to investigate the association among the variables. Paired t-test was conducted to analyze the longitudinal variation of social support scores. Results: During the 6.37 years of median follow-up, 137 participants developed CCVD. The adjusted odds ratio (aOR) of metabolic syndrome with persistently high social support was 2.175 [95% confidence interval (CI): 1.479–3.119]. The aOR of metabolic syndrome with persistently low social support was 2.494 (95%CI: 1.141–5.452). The longitudinal variation of the social support score of persistently high social support group was increased significantly by 4.26±26.32. The score of the persistently low social support group was decreased by 1.34±16.87 with no statistical significance. Conclusion The presence of metabolic syndrome increases the likelihood of developing onset CCVD. Within the metabolic syndrome positive group, when social support was persistently low, the cohort developed more cardio-cerebrovascular disease compared to the persistently higher social support group. The social support score of the persistently low social support group could be improved through proper intervention. To prevent CCVD, metabolic syndrome components and low social support should be improved in the study participants.

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.