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.

In forensic science, age estimation is essential for identifying both living and deceased individuals. Teeth and jawbones serve as reliable indicators due to their gradual age-related changes and resistance to environmental factors. Among the various methods, attrition and pulp cavity size are commonly used to estimate adult age. This study aimed to enhance the accuracy of age estimation in Korean adults by combining measurements of tooth attrition and pulp cavity size obtained from panoramic radiographs of mandibular first molars. We evaluated 118 patients (62 male, 56 female) who visited Pusan National University Dental Hospital between 2010 and 2024. Radiographs and clinical photographs were analyzed for grade C teeth with exposed dentin using Takei’s method, and the pulp chamber height ratio (PCHR) and width ratio (PCWR) were measured using Jeon’s method. Intraobserver reliability was high (intraclass correlation coefficient>0.6), with no significant sex-based differences in PCHR and PCWR. Both ratios negatively correlated with age, with PCWR showing a stronger correlation, particularly in females (r=–0.606). This study derived an improved age estimation formula with R² values ranging from 0.540 to 0.546 when both PCHR and PCWR were combined. Despite the limitations of this study, such as its small sample size and reliance on panoramic radiographs, the findings suggest that combining tooth wear and pulp cavity size offers a more robust tool for age estimation in clinical and forensic settings.

Accessory breast tissue can appear along the mammary ridge, extending from the axilla to the groin, with the axilla being the most common site. Malignancies arising in accessory breast tissue are rare, representing approximately 0.3%–0.6% of all breast cancer cases.When evaluating accessory breasts, it is essential to apply the same management strategies used for conventionally positioned breasts. This report presents a case of ductal carcinoma in situ originating from accessory breast tissue in the axilla of a 55-year-old woman. The diagnosis was established through a comprehensive assessment involving mammography, ultrasound, and magnetic resonance imaging. Since the axillary accessory breast tissue wasn’t initially included in the routine mammography, we added an axillary tail view to evaluate the extent and morphology of malignant microcalcifications.

Purpose: Claudin 18.2 (CLDN18.2) has emerged as a promising therapeutic target for CLDN18.2-expressing gastric cancer (GC). We sought to examine the heterogeneity of CLDN18.2 expression between primary GC (PGC) and metastatic GC (MGC) using various scoring methods. Materials and Methods: We retrospectively analyzed data from 102 patients with pathologically confirmed paired primary and metastatic gastric or gastroesophageal junction adenocarcinomas. CLDN18.2 expression was evaluated through immunohistochemistry on formalin-fixed paraffin-embedded tissue samples. We assessed CLDN18.2 positivity using multiple scoring approaches, including the immunoreactivity score, H-score, and the percentage of tumor cells showing moderate-to-strong staining intensity. We analyzed the concordance rates between PGC and MGC and the association of CLDN18.2 positivity with clinicopathological features. Results: CLDN18.2 positivity varied from 25% to 65% depending on the scoring method, with PGC consistently showing higher expression levels than MGC. Intratumoral heterogeneity was noted in 25.5% of PGCs and 19.6% of MGCs. Intertumoral heterogeneity, manifesting as discordance in CLDN18.2 positivity between PGC and MGC, was observed in about 20% of cases, with moderate agreement across scoring methods (κ=0.47 to 0.60).In PGC, higher CLDN18.2 positivity correlated with synchronous metastasis, presence of peritoneal metastasis, poorly differentiated grade, and biopsy specimens. In MGC, positivity was associated with synchronous metastasis, presence of peritoneal metastasis, and metastatic peritoneal tissues. Conclusions CLDN18.2 expression demonstrates significant heterogeneity between PGC and MGC, with a 20% discordance rate. Comprehensive tissue sampling and reassessment of CLDN18.2 status are crucial, especially before initiating CLDN18.2-targeted therapies.

Purpose: Temporomandibular disorder (TMD) is a multifactorial condition influenced by biological, psychological, and social factors. Some patients perceive dental treatment as the cause of their TMD, which may impact symptom perception and treatment response.This study aims to compare the psychosocial and clinical characteristics of patients who attribute their TMD onset to prior dental treatment (DT group) with those who do not (NT group). Methods: A total of 250 patients diagnosed with TMD at Pusan National University Dental Hospital between January and October 2024 were included. Patients were classified into the DT group (n=92) if they attributed their symptoms to previous dental procedures, while the remaining NT group (n=158) consisted of patients with other primary complaints. Patients were additionally categorized into Generation X (1965-1980), Generation Y (1981-1999), and Generation Z (2000-2012). Clinical assessments were conducted following the Diagnostic Criteria for Temporomandibular Disorders (DC/TMD), and psychosocial factors were evaluated using the Oral Behaviors Checklist (OBC), Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and Patient Health Questionnaire-15 (PHQ-15). Results: The DT group had a significantly higher mean age (54.16±16.87 years) compared to the NT group (39.71±19.12 years, p<0.001). Psychological distress scores (PHQ-9, GAD-7, and PHQ-15) were significantly higher in the DT group, particularly among Generation X patients (p<0.05). Muscle-related pain was more prevalent in the DT group (75%) than in the NT group. Interestingly, OBC scores were higher in the NT group (p<0.05). Conclusions Patients who attributed their TMD onset to dental treatment reported higher psychological distress and a greater prevalence of muscle-related pain, underscoring the need for a biopsychosocial approach to TMD management. Clinicians should consider the impact of patient perception on TMD symptoms and integrate psychological assessment and counseling to enhance treatment efficacy and patient adherence.

Accessory breast tissue can appear along the mammary ridge, extending from the axilla to the groin, with the axilla being the most common site. Malignancies arising in accessory breast tissue are rare, representing approximately 0.3%–0.6% of all breast cancer cases.When evaluating accessory breasts, it is essential to apply the same management strategies used for conventionally positioned breasts. This report presents a case of ductal carcinoma in situ originating from accessory breast tissue in the axilla of a 55-year-old woman. The diagnosis was established through a comprehensive assessment involving mammography, ultrasound, and magnetic resonance imaging. Since the axillary accessory breast tissue wasn’t initially included in the routine mammography, we added an axillary tail view to evaluate the extent and morphology of malignant microcalcifications.

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.

In forensic science, age estimation is essential for identifying both living and deceased individuals. Teeth and jawbones serve as reliable indicators due to their gradual age-related changes and resistance to environmental factors. Among the various methods, attrition and pulp cavity size are commonly used to estimate adult age. This study aimed to enhance the accuracy of age estimation in Korean adults by combining measurements of tooth attrition and pulp cavity size obtained from panoramic radiographs of mandibular first molars. We evaluated 118 patients (62 male, 56 female) who visited Pusan National University Dental Hospital between 2010 and 2024. Radiographs and clinical photographs were analyzed for grade C teeth with exposed dentin using Takei’s method, and the pulp chamber height ratio (PCHR) and width ratio (PCWR) were measured using Jeon’s method. Intraobserver reliability was high (intraclass correlation coefficient>0.6), with no significant sex-based differences in PCHR and PCWR. Both ratios negatively correlated with age, with PCWR showing a stronger correlation, particularly in females (r=–0.606). This study derived an improved age estimation formula with R² values ranging from 0.540 to 0.546 when both PCHR and PCWR were combined. Despite the limitations of this study, such as its small sample size and reliance on panoramic radiographs, the findings suggest that combining tooth wear and pulp cavity size offers a more robust tool for age estimation in clinical and forensic settings.

Conventional full-protocol breast magnetic resonance imaging (MRI) includes T2-weighted imaging and dynamic contrast-enhanced MRI, consisting of pre-contrast and four to six post-contrast T1-weighted images. By contrast, abbreviated breast MRI consists of pre-contrast and a single post-contrast T1-weighted image as core sequences, along with reconstructed subtraction and maximum intensity projection images. Additional sequences, such as T2-weighted imaging, diffusion-weighted imaging, and a second postcontrast T1-weighted image, can be included in the abbreviated protocol based on user preferences; however, the recommended total acquisition time is less than 10 minutes.Using abbreviated MRI, the scan time is reduced, and MRI throughput increases, which can lower costs and improve accessibility. Moreover, the current evidence consistently suggests that the accuracy of abbreviated breast MRI in detecting breast cancer is comparable to that of full-protocol MRI. With these advantages, abbreviated breast MRI may be increasingly used for screening women at average to intermediate risk. This review discusses the background of abbreviated MRI, the results of clinical studies, and outstanding issues for future consideration.