Background: Differentiating brain abscesses from necrotic cystic tumors is critical for clinical management but remains challenging due to overlapping features on conventional MRI. Aim: To evaluate the diagnostic performance of diffusion-weighted and morphological MRI biomarkers in differentiating brain abscesses from necrotic cystic brain tumors. Materials and Methods: This retrospective study included 54 patients with ring-enhancing brain lesions (brain abscess, n=13; metastasis, n=18; glioblastoma, n=23). MRI protocols (T1W, T2W, DWI, SWI, and T1+CE) were analyzed for lesion morphology, diffusion characteristics, capsule thickness, and the “dual rim sign.” Group comparisons were performed using ANOVA, chi-square, and Fisher’s exact tests. Diagnostic performance and optimal thresholds were determined using ROC curve analysis (p<0.05). Results: 1. Comparison of Diffusion Parameters: The core ADC ratio min was significantly lower in the brain abscess group than in the cystic-necrotic tumor group (0.812±0.228 vs 1.902±0.535 for glioblastoma, 1.590±0.394 for metastasis; p<0.001). The area under the curve (AUC) for core ADC ratio min was 0.977, indicating excellent diagnostic performance. 2. Capsule Characteristics: On SWI, the “dual rim sign” was detected in 92.3% of brain abscesses but was absent (0%) in all cystic-necrotic tumors (p<0.001). The abscess capsules were significantly thinner (mean 3.3 mm) and more uniform compared to the irregular, thicker walls of necrotic tumors. 3. Diagnostic Thresholds: The optimal cut-off value for the core ADC ratio min was 0.94 (sensitivity 92.3%, specificity 92.7%). For capsule thickness, the cut-off value was 3.3 mm with an AUC of 0.955. Conclusion Core ADC ratio and capsule thickness are reliable quantitative MRI biomarkers for differentiating brain abscesses from cystic-necrotic tumors. Integrating diffusion metrics with susceptibility-based and morphological features, such as the “dual rim sign,” significantly enhances diagnostic accuracy in clinical practice, facilitating non-invasive and timely diagnosis.

Background: Early and accurate differentiation of benign and malignant breast nodules is crucial for determining appropriate therapeutic strategies. Recently, artificial intelligence (AI)-based diagnostic systems have been increasingly utilized to enhance the diagnostic performance of ultrasound imaging. Aim: To evaluate the diagnostic value of artificial intelligence-based ultrasound (AI-US) in differentiating benign and malignant breast nodules and to compare its performance with conventional ultrasound (US). Materials and Methods: This retrospective study included 101 patients with 103 breast nodules (58 malignant, 45 benign) confirmed by histopathological analysis (biopsy or surgery). Diagnostic performance was evaluated across three groups: 1) Conventional ultrasound (US), 2) AI system (AI group), and 3) Combined US and AI system (US+AI group). Sensitivity (SE), specificity (SP), accuracy (ACC), positive predictive value (PPV), negative predictive value (NPV), and the area under the ROC curve (AUC) were calculated and compared. Results: Of the 103 nodules, 56.3% were malignant and 43.7% were benign. The AI group demonstrated higher diagnostic metrics than the US group—SE (82.76%), SP (80.00%), ACC (81.55%), and AUC (0.814)—though the difference was not statistically significant (p>0.05). However, the combined US+AI group showed significant improvement, reaching an SE of 87.93%, SP of 86.67%, ACC of 87.38%, and an AUC of 0.874. These results were statistically superior to the conventional US group (p<0.05). Conclusion The AI-based ultrasound diagnostic system significantly improves the accuracy of differentiating breast nodules. This technology serves as a reliable decision-support tool, particularly for reducing diagnostic errors among less experienced physicians and minimizing unnecessary invasive biopsies.

Background: Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder associated with cognitive impairment and structural brain alterations. White matter hyperintensities (WMH) detected on magnetic resonance imaging (MRI) serve as critical neuroimaging markers of cerebral small vessel disease. However, research evaluating the WMH burden in the Mongolian population with T2DM using AI-based automated quantitative methods and comparing these findings with a control group remains limited. This research gap provides the rationale and necessity for the current study. Aim: To evaluate and compare the burden of brain white matter hyperintensities (WMH) in adults with type 2 diabetes mellitus (T2DM) versus a control group. Material and Methods: This analytical cross-sectional, retrospective comparative study included participants aged 40 and older who underwent brain MRI at the Mongolia-Japan Hospital of MNUMS between 2019 and 2025. The T2DM group consisted of patients with a confirmed diagnosis, while the control group included individuals without diabetes or prediabetes. After excluding cases with major structural abnormalities, poor image quality, failed automated processing, or incomplete clinical data, 257 out of an initial 283 participants were included in the final analysis (T2DM: 71, Control: 186). 3D T1-weighted MPRAGE and T2-FLAIR MRI sequences were processed using Neurophet AQUA software (version 3.1) to assess total, deep, and periventricular Fazekas scores. Age- and sex-adjusted ordinal logistic regression analysis was performed using SPSS 25.0, with p<0.05 considered statistically significant. Results: A total of 257 participants (T2DM=71, Control=186) were analyzed. There were significant differences between groups in sex distribution (p=0.046) and age categories (p<0.001). The T2DM group exhibited significantly higher total and deep Fazekas scores compared to the control group (total Fazekas: aOR 10.81, 95% CI 5.20-22.49; deep Fazekas: aOR 8.21, 95% CI 4.32-15.60; both p<0.001). Increasing age was associated with higher total (aOR 1.07, 95% CI 1.03-1.11, p=0.001) and deep (aOR 1.04, 95% CI 1.00-1.08, p=0.037) Fazekas scores. Additionally, female sex was associated with higher total (aOR 1.95, 95% CI 1.04–3.63, p=0.036) and deep (aOR 1.95, 95% CI 1.08–3.51, p=0.026) Fazekas score categories. Conclusion Assessment of WMH burden using AI-based automated quantitative MRI processing revealed that T2DM is significantly associated with higher total and deep Fazekas scores. This association remained significant after adjusting for age and sex. These findings suggest that AI-based MRI processing is a suitable method for the objective, quantitative, and standardized assessment of brain WMH burden associated with T2DM.