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.

This study aimed to assess the accuracy of tooth widths, intermolar widths, and arch lengths acquired through two intraoral scanners, including iTero Element Plus Series (Align Technology, Santa Clara, CA, USA) and Trios 4 (3Shape, Copenhagen, Denmark), specifically on mixed dentition. A total of 30 subjects were divided into 2 groups, each undergoing both alginate impressions and intraoral scanning using either the iTero or Trios scanner. The plaster models were measured with a caliper, while the digital models were measured virtually. In the iTero group, all tooth width measurements exhibited differences compared to the plaster values, except for maxillary left lateral incisors (p = 0.179), mandibular right (p = 0.285), and left (p = 0.073) central incisors. The Trios group did not display significant differences in any of the tooth width measurements. Intermolar width comparisons for both groups indicated differences, except for mandibular primary canine to primary canine values (p = 0.426) in the iTero group. Regarding arch length, the mandibular anterior, maxillary right, and left arch lengths in the iTero group demonstrated larger caliper values than those of iTero. Conversely, in the Trios group, all parameters showed smaller caliper values, especially in upper anterior, maxillary right, mandibular right, and mandibular left arch lengths with significance (p = 0.027, 0.007, 0.003, and 0.047, respectively). Despite the differences between the two groups, digital models might be clinically suitable alternatives for plaster models. Pediatric dentists should carefully assess these differences, as a comprehensive evaluation would result in precise orthodontic treatment planning and favorable outcomes for young patients with mixed dentition.

Purpose: The aims were to investigate potential differences in clinical assessments among acute pain-related temporomandibular disorder (TMD) with different concordance value (CV) between number of self-reported painful site (NSP) and number of painful sites on palpation (NPP), and if it makes sense to treat them differently. Methods: A total of 61 patients were divided into three groups according to CV: 10 patients (concordance poor [CP]), 19 patients (concordance moderate [CM]), and 32 patients (concordance high [CH]). Clinical assessments were conducted using a standardized method in diagnostic criteria for temporomandibular disorders (DC/TMD). We compared collected information including sex, diagnosis, numerical rating scale (NRS), NPP, NSP, sleep duration, DC/TMD Axis II questionnaire, and perceived stress scale among three groups. Results: Among the clinical assessment, NRS, sleep duration, NPP, NSP, total scores of Oral Behaviors Checklist, Patient Health Questionnaire (PHQ)-15, PHQ-9 showed significant differences among 3 groups. NRS, NPP, NSP, PHQ-15, and PHQ-9 were higher in the CP group than in the CM and CH groups. Sleep duration was positively and NPP, NRS were negatively correlated with CV. Conclusions While previous studies suggested differences between chronic and acute TMD in DC/TMD items, our findings propose the CV might be a key factor that could predict the severity and susceptibility of acute-TMD patients. However, Additional studies are required to determine whether their long-term prognosis was similar to that of chronic pain patients and what the response to treatment was among the three groups.

Purpose: The Korean Association of Pediatric Surgeons (KAPS) conducts annual nationwide surveys on various aspects of pediatric surgical diseases, with the results being discussed during KAPS’s annual spring meetings. Methods: KAPS conducted two national surveys, in 1995 and 2016, to investigate esophageal atresia (EA) with or without tracheoesophageal fistula (TEF). The authors analyzed data from these surveys to identify differences or changes in the annual occurrence, demographic characteristics, clinical presentation, preoperative diagnostic methods, anatomical type, associated anomalies, surgical treatment, and postoperative outcomes among patients with EA/TEF treated by KAPS members. Results: The first and second national surveys included 148 and 211 patients with EA/TEF, respectively. Excessive salivation was the most prevalent clinical symptom in both surveys. Type C was the most common form of EA/TEF in both surveys. The first survey included 126 patients, all of whom underwent open surgery. In the second survey, 152 (78.4%) of 194 patients underwent open surgery, while 34 (17.5%) underwent thoracoscopic surgery. Primary esophageal repair was performed on 96 (76.2%) of 126 patients in the first survey and on 160 (82.5%) of 194 patients in the second survey. Anastomotic strictures developed in 21.4% and 32.5% of patients, anastomotic leakage in 22.2% and 10.3%, recurrent fistula in 2.4% and 4.2% during the first and second surveys, respectively. The respective survival rates for group A were 90.2% and 98.3% in the first and second surveys. For group B, the rates were 73.9% and 98.1%, and for group C, they were 34.5% and 68.1%, respectively, according to the Waterston classifications. Conclusion These nationwide surveys provide comprehensive information on the status, detailed treatment, and outcomes for Korean pediatric patients with EA/TEF. They are anticipated to be an invaluable resource and guide for pediatric surgeons seeking to expand their knowledge on EA/TEF and its treatment options.

The first edition of ‘A Standardized Pathology Report for Gastric Cancer’ was initiated by the Gastrointestinal Pathology Study Group of the Korean Society of Pathologists and published 17 years ago. Since then, significant advances have been made in the pathologic diagnosis, molecular genetics, and management of gastric cancer (GC). To reflect those changes, a committee for publishing a second edition of the report was formed within the Gastrointestinal Pathology Study Group of the Korean Society of Pathologists. This second edition consists of two parts: standard data elements and conditional data elements.The standard data elements contain the basic pathologic findings and items necessary to predict the prognosis of GC patients, and they are adequate for routine surgical pathology service. Other diagnostic and prognostic factors relevant to adjuvant therapy, including molecular biomarkers, are classified as conditional data elements to allow each pathologist to selectively choose items appropriate to the environment in their institution. We trust that the standardized pathology report will be helpful for GC diagnosis and facilitate large-scale multidisciplinary collaborative studies.

The first edition of ‘A Standardized Pathology Report for Gastric Cancer’ was initiated by the Gastrointestinal Pathology Study Group of the Korean Society of Pathologists and published 17 years ago. Since then, significant advances have been made in the pathologic diagnosis, molecular genetics, and management of gastric cancer (GC). To reflect those changes, a committee for publishing a second edition of the report was formed within the Gastrointestinal Pathology Study Group of the Korean Society of Pathologists. This second edition consists of two parts: standard data elements and conditional data elements. The standard data elements contain the basic pathologic findings and items necessary to predict the prognosis of GC patients, and they are adequate for routine surgical pathology service. Other diagnostic and prognostic factors relevant to adjuvant therapy, including molecular biomarkers, are classified as conditional data elements to allow each pathologist to selectively choose items appropriate to the environment in their institution. We trust that the standardized pathology report will be helpful for GC diagnosis and facilitate large-scale multidisciplinary collaborative studies.