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.

BACKGROUND/OBJECTIVES: Green Citrus junos (yuja) peel extract has higher naringin and hesperidin contents and antioxidant activity than yellow yuja peel extract, but its anti-obesity effects are unclear. This study examined the anti-obesity properties of green yuja peel ethanol extract (GYE) in 3T3-L1 cells and high-fat diet (HFD)-induced obese mice.MATERIALS/METHODS: The effects of GYE on adipocyte differentiation were assessed by measuring Oil red O staining, mRNA and protein expression. The beneficial effects of GYE on HFD-induced obese mice were evaluated using the body weight, body composition, visceral fat size, and biochemical analysis. RESULTS: GYE inhibited adipocyte differentiation and lipid accumulation compared to the control cells, as evidenced by Oil red O staining and the triglyceride level, respectively.GYE down-regulated the adipogenic genes CCAAT/enhancer binding protein α (C/EBPα) and peroxisome proliferator-activated receptor γ (PPARγ), and lipogenic gene diacylglycerol O-acyltransferase 2 (DGAT2). GYE at 100 μg/mL downregulated the phosphorylation levels of phosphoinositide 3-kinase (PI3K) and protein kinase B (Akt), and their downstream targets PPARγ and sterol regulatory element-binding protein-1 (SREBP-1c) compared to the control group. In obese mice, GYE (100 mg/kg/day) reduced the body weight, body weight gain, and serum lipid level compared to the control group. Analysis using dual-energy X-ray absorptiometry showed that GYE decreased the fat percentage, fat in tissue, and abdominal circumference, while it increased the lean percentage compared to control group.Furthermore, GYE significantly reduced the visceral fat weight and size compared to the control group. CONCLUSION GYE suppressed adipocyte differentiation by inhibiting the PI3K-Akt pathway in vitro and reduced the body fat mass and visceral adiposity in HFD-induced obese mice.These findings suggest that GYE is a viable natural option for combating obesity.

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.

Background: Small cell lung cancer (SCLC) is called ‘smoker’s disease’ because it is strongly associated with smoking and most cases occur in smokers. However, it can also occur in never smokers. We investigated the clinical features of never smokers with SCLC and compared their treatment outcomes with those of smokers with SCLC. Methods: We retrospectively reviewed the clinical data of patients who had proven SCLC and had received chemotherapy at a single cancer center between July 2002 and April 2021. Results: Of 1,643 patients, 1,416 (86.2%) were enrolled in this study. A total of 162 (11.4%) and 1,254 (88.6%) patients were never smokers and smokers, respectively. There were more female never smokers than smokers (n=130; 80.2% vs. 79, 6.3%, p=0.000), and the incidence of ischemic heart disease was lower among never smokers than among smokers (4/1,416, [2.5%] vs. 83/1,416 [6.6%], p=0.036). Never smokers showed less symptoms at diagnosis than smokers (80.9% vs. 87.2%, p=0.037); however, they showed more toxicity after first-line treatment (61.7% vs. 47.8%, p=0.001). The objective response rate (ORR) was significantly higher in never smokers (74.1% vs. 59.6%, p=0.000). In the multivariate analysis, never smoking and second-line treatment were associated with a better ORR. However, progression-free survival and overall survival were not significantly different between never smokers and smokers. Conclusion In conclusion, never smokers accounted for 11.4% of patients with SCLC. They had distinguishing clinical characteristics and showed better chemotherapeutic responses than smokers.

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.

BACKGROUND/OBJECTIVES: Green Citrus junos (yuja) peel extract has higher naringin and hesperidin contents and antioxidant activity than yellow yuja peel extract, but its anti-obesity effects are unclear. This study examined the anti-obesity properties of green yuja peel ethanol extract (GYE) in 3T3-L1 cells and high-fat diet (HFD)-induced obese mice.MATERIALS/METHODS: The effects of GYE on adipocyte differentiation were assessed by measuring Oil red O staining, mRNA and protein expression. The beneficial effects of GYE on HFD-induced obese mice were evaluated using the body weight, body composition, visceral fat size, and biochemical analysis. RESULTS: GYE inhibited adipocyte differentiation and lipid accumulation compared to the control cells, as evidenced by Oil red O staining and the triglyceride level, respectively.GYE down-regulated the adipogenic genes CCAAT/enhancer binding protein α (C/EBPα) and peroxisome proliferator-activated receptor γ (PPARγ), and lipogenic gene diacylglycerol O-acyltransferase 2 (DGAT2). GYE at 100 μg/mL downregulated the phosphorylation levels of phosphoinositide 3-kinase (PI3K) and protein kinase B (Akt), and their downstream targets PPARγ and sterol regulatory element-binding protein-1 (SREBP-1c) compared to the control group. In obese mice, GYE (100 mg/kg/day) reduced the body weight, body weight gain, and serum lipid level compared to the control group. Analysis using dual-energy X-ray absorptiometry showed that GYE decreased the fat percentage, fat in tissue, and abdominal circumference, while it increased the lean percentage compared to control group.Furthermore, GYE significantly reduced the visceral fat weight and size compared to the control group. CONCLUSION GYE suppressed adipocyte differentiation by inhibiting the PI3K-Akt pathway in vitro and reduced the body fat mass and visceral adiposity in HFD-induced obese mice.These findings suggest that GYE is a viable natural option for combating obesity.

Background: Small cell lung cancer (SCLC) is called ‘smoker’s disease’ because it is strongly associated with smoking and most cases occur in smokers. However, it can also occur in never smokers. We investigated the clinical features of never smokers with SCLC and compared their treatment outcomes with those of smokers with SCLC. Methods: We retrospectively reviewed the clinical data of patients who had proven SCLC and had received chemotherapy at a single cancer center between July 2002 and April 2021. Results: Of 1,643 patients, 1,416 (86.2%) were enrolled in this study. A total of 162 (11.4%) and 1,254 (88.6%) patients were never smokers and smokers, respectively. There were more female never smokers than smokers (n=130; 80.2% vs. 79, 6.3%, p=0.000), and the incidence of ischemic heart disease was lower among never smokers than among smokers (4/1,416, [2.5%] vs. 83/1,416 [6.6%], p=0.036). Never smokers showed less symptoms at diagnosis than smokers (80.9% vs. 87.2%, p=0.037); however, they showed more toxicity after first-line treatment (61.7% vs. 47.8%, p=0.001). The objective response rate (ORR) was significantly higher in never smokers (74.1% vs. 59.6%, p=0.000). In the multivariate analysis, never smoking and second-line treatment were associated with a better ORR. However, progression-free survival and overall survival were not significantly different between never smokers and smokers. Conclusion In conclusion, never smokers accounted for 11.4% of patients with SCLC. They had distinguishing clinical characteristics and showed better chemotherapeutic responses than smokers.

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.

BACKGROUND/OBJECTIVES: Green Citrus junos (yuja) peel extract has higher naringin and hesperidin contents and antioxidant activity than yellow yuja peel extract, but its anti-obesity effects are unclear. This study examined the anti-obesity properties of green yuja peel ethanol extract (GYE) in 3T3-L1 cells and high-fat diet (HFD)-induced obese mice.MATERIALS/METHODS: The effects of GYE on adipocyte differentiation were assessed by measuring Oil red O staining, mRNA and protein expression. The beneficial effects of GYE on HFD-induced obese mice were evaluated using the body weight, body composition, visceral fat size, and biochemical analysis. RESULTS: GYE inhibited adipocyte differentiation and lipid accumulation compared to the control cells, as evidenced by Oil red O staining and the triglyceride level, respectively.GYE down-regulated the adipogenic genes CCAAT/enhancer binding protein α (C/EBPα) and peroxisome proliferator-activated receptor γ (PPARγ), and lipogenic gene diacylglycerol O-acyltransferase 2 (DGAT2). GYE at 100 μg/mL downregulated the phosphorylation levels of phosphoinositide 3-kinase (PI3K) and protein kinase B (Akt), and their downstream targets PPARγ and sterol regulatory element-binding protein-1 (SREBP-1c) compared to the control group. In obese mice, GYE (100 mg/kg/day) reduced the body weight, body weight gain, and serum lipid level compared to the control group. Analysis using dual-energy X-ray absorptiometry showed that GYE decreased the fat percentage, fat in tissue, and abdominal circumference, while it increased the lean percentage compared to control group.Furthermore, GYE significantly reduced the visceral fat weight and size compared to the control group. CONCLUSION GYE suppressed adipocyte differentiation by inhibiting the PI3K-Akt pathway in vitro and reduced the body fat mass and visceral adiposity in HFD-induced obese mice.These findings suggest that GYE is a viable natural option for combating obesity.

Background: Small cell lung cancer (SCLC) is called ‘smoker’s disease’ because it is strongly associated with smoking and most cases occur in smokers. However, it can also occur in never smokers. We investigated the clinical features of never smokers with SCLC and compared their treatment outcomes with those of smokers with SCLC. Methods: We retrospectively reviewed the clinical data of patients who had proven SCLC and had received chemotherapy at a single cancer center between July 2002 and April 2021. Results: Of 1,643 patients, 1,416 (86.2%) were enrolled in this study. A total of 162 (11.4%) and 1,254 (88.6%) patients were never smokers and smokers, respectively. There were more female never smokers than smokers (n=130; 80.2% vs. 79, 6.3%, p=0.000), and the incidence of ischemic heart disease was lower among never smokers than among smokers (4/1,416, [2.5%] vs. 83/1,416 [6.6%], p=0.036). Never smokers showed less symptoms at diagnosis than smokers (80.9% vs. 87.2%, p=0.037); however, they showed more toxicity after first-line treatment (61.7% vs. 47.8%, p=0.001). The objective response rate (ORR) was significantly higher in never smokers (74.1% vs. 59.6%, p=0.000). In the multivariate analysis, never smoking and second-line treatment were associated with a better ORR. However, progression-free survival and overall survival were not significantly different between never smokers and smokers. Conclusion In conclusion, never smokers accounted for 11.4% of patients with SCLC. They had distinguishing clinical characteristics and showed better chemotherapeutic responses than smokers.