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.

Background/Aims: Early colorectal cancer (ECC) is commonly resected endoscopically. Perforation is a devastating complication of endoscopic resection. We aimed to identify the characteristics and predictive risk factors for perforation related to endoscopic resection of ECC. Methods: This nationwide retrospective multicenter study included patients with ECC who underwent endoscopic resection. We investigated the demographics, endoscopic findings at the time of treatment, and histopathological characteristics of the resected specimens. Logistic regression analysis was used to investigate the clinical factors associated with procedure-related perforations. Survival analysis was conducted to assess the impact of perforation on the overall survival of patients with ECC. Results: This study included 965 participants with a mean age of 63.4 years. The most common endoscopic treatment was conventional endoscopic mucosal resection (n=573, 59.4%), followed by conventional endoscopic submucosal dissection (n=259, 26.8%). Thirty-three patients (3.4%) experienced perforations, most of which were managed endoscopically (n=23/33, 69.7%). Patients who undergo endoscopic submucosal dissection-hybrid and precut endoscopic mucosal resection have a higher risk of perforation than those who undergo conventional endoscopic mucosal resection (odds ratio, 78.65 and 39.72, p<0.05). Procedure-related perforations were not associated with patient survival. Conclusions Perforation after endoscopic resection had no significant impact on the prognosis of ECC. The type of endoscopic resection was a crucial predictor of perforation. Large-scale prospective studies are needed to further investigate endoscopic resection of ECC.

Background/Aims: Early colorectal cancer (ECC) is commonly resected endoscopically. Perforation is a devastating complication of endoscopic resection. We aimed to identify the characteristics and predictive risk factors for perforation related to endoscopic resection of ECC. Methods: This nationwide retrospective multicenter study included patients with ECC who underwent endoscopic resection. We investigated the demographics, endoscopic findings at the time of treatment, and histopathological characteristics of the resected specimens. Logistic regression analysis was used to investigate the clinical factors associated with procedure-related perforations. Survival analysis was conducted to assess the impact of perforation on the overall survival of patients with ECC. Results: This study included 965 participants with a mean age of 63.4 years. The most common endoscopic treatment was conventional endoscopic mucosal resection (n=573, 59.4%), followed by conventional endoscopic submucosal dissection (n=259, 26.8%). Thirty-three patients (3.4%) experienced perforations, most of which were managed endoscopically (n=23/33, 69.7%). Patients who undergo endoscopic submucosal dissection-hybrid and precut endoscopic mucosal resection have a higher risk of perforation than those who undergo conventional endoscopic mucosal resection (odds ratio, 78.65 and 39.72, p<0.05). Procedure-related perforations were not associated with patient survival. Conclusions Perforation after endoscopic resection had no significant impact on the prognosis of ECC. The type of endoscopic resection was a crucial predictor of perforation. Large-scale prospective studies are needed to further investigate endoscopic resection of ECC.

Background/Aims: Early colorectal cancer (ECC) is commonly resected endoscopically. Perforation is a devastating complication of endoscopic resection. We aimed to identify the characteristics and predictive risk factors for perforation related to endoscopic resection of ECC. Methods: This nationwide retrospective multicenter study included patients with ECC who underwent endoscopic resection. We investigated the demographics, endoscopic findings at the time of treatment, and histopathological characteristics of the resected specimens. Logistic regression analysis was used to investigate the clinical factors associated with procedure-related perforations. Survival analysis was conducted to assess the impact of perforation on the overall survival of patients with ECC. Results: This study included 965 participants with a mean age of 63.4 years. The most common endoscopic treatment was conventional endoscopic mucosal resection (n=573, 59.4%), followed by conventional endoscopic submucosal dissection (n=259, 26.8%). Thirty-three patients (3.4%) experienced perforations, most of which were managed endoscopically (n=23/33, 69.7%). Patients who undergo endoscopic submucosal dissection-hybrid and precut endoscopic mucosal resection have a higher risk of perforation than those who undergo conventional endoscopic mucosal resection (odds ratio, 78.65 and 39.72, p<0.05). Procedure-related perforations were not associated with patient survival. Conclusions Perforation after endoscopic resection had no significant impact on the prognosis of ECC. The type of endoscopic resection was a crucial predictor of perforation. Large-scale prospective studies are needed to further investigate endoscopic resection of ECC.

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.

Background/Aims: Early colorectal cancer (ECC) is commonly resected endoscopically. Perforation is a devastating complication of endoscopic resection. We aimed to identify the characteristics and predictive risk factors for perforation related to endoscopic resection of ECC. Methods: This nationwide retrospective multicenter study included patients with ECC who underwent endoscopic resection. We investigated the demographics, endoscopic findings at the time of treatment, and histopathological characteristics of the resected specimens. Logistic regression analysis was used to investigate the clinical factors associated with procedure-related perforations. Survival analysis was conducted to assess the impact of perforation on the overall survival of patients with ECC. Results: This study included 965 participants with a mean age of 63.4 years. The most common endoscopic treatment was conventional endoscopic mucosal resection (n=573, 59.4%), followed by conventional endoscopic submucosal dissection (n=259, 26.8%). Thirty-three patients (3.4%) experienced perforations, most of which were managed endoscopically (n=23/33, 69.7%). Patients who undergo endoscopic submucosal dissection-hybrid and precut endoscopic mucosal resection have a higher risk of perforation than those who undergo conventional endoscopic mucosal resection (odds ratio, 78.65 and 39.72, p<0.05). Procedure-related perforations were not associated with patient survival. Conclusions Perforation after endoscopic resection had no significant impact on the prognosis of ECC. The type of endoscopic resection was a crucial predictor of perforation. Large-scale prospective studies are needed to further investigate endoscopic resection of ECC.

Background: This study aimed to generate a Z score calculation model for coronary artery diameter of normal children and adolescents to be adopted as the standard calculation method with consensus in clinical practice. Methods: This study was a retrospective, multicenter study that collected data from multiple institutions across South Korea. Data were analyzed to determine the model that best fit the relationship between the diameter of coronary arteries and independent demographic parameters. Linear, power, logarithmic, exponential, and square root polynomial models were tested for best fit. Results: Data of 2,030 subjects were collected from 16 institutions. Separate calculation models for each sex were developed because the impact of demographic variables on the diameter of coronary arteries differs according to sex. The final model was the polynomial formula with an exponential relationship between the diameter of coronary arteries and body surface area using the DuBois formula. Conclusion A new coronary artery diameter Z score model was developed and is anticipated to be applicable in clinical practice. The new model will help establish a consensus-based Z score model.