Background: In Mongolia, cervical cancer accounted for 11.9% of all cancers diagnosed in women and 7.3% of cancer-related deaths in 2017, ranking as the third most common cancer among women. The American Society for Radiation Oncology (ASTRO) guidelines recommend concurrent chemo-radiotherapy (CCRT) as a standard and effective treatment for patients with FIGO stage IB3–IVA cervical cancer. Accurate assessment of tumor response following CCRT is essential for evaluating treatment efficacy, identifying residual disease, and estimating the risk of recurrence. Magnetic resonance imaging (MRI) is widely used to assess tumor size, local extent, parametric involvement, and post-treatment morphological changes. In contrast, 18F-FDG PET/CT provides both anatomical and functional information and enables quantitative assessment of tumor metabolic activity using the standardized uptake value (SUV). Aim: To estimate the relationship between treatment response to concurrent chemo-radiotherapy and radiological parameters obtained from MRI and 18F-FDG PET/CT in patients with cervical cancer. Materials and Methods: This retrospective study included 35 patients with histo-pathologically confirmed cervical carcinoma who received concurrent chemo-radiotherapy at the Department of Radiation Oncology, National Cancer Center, between 2024 and 2025. Pelvic MRI examinations were performed using a 1.5-T Siemens MRI system to evaluate tumor dimensions and local tumor characteristics before and after treatment. 18F-FDG PET/CT was performed to assess the metabolic activity of the primary tumor, and the maximum SUV was measured. Tumor response following CCRT was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. The relationships between tumor response, changes in tumor size, and pre-treatment SUV were statistically analyzed. Result: A total of 35 patients aged 32–73 years were included in the study, with a mean age of 51.2±12.6 years. The mean baseline longitudinal tumor diameter measured by MRI was 54.3±15.8 mm. Twenty-eight patients (80.0%) had tumors larger than 4 cm, whereas 7 (20.0%) had tumors smaller than 4 cm. According to the FIGO staging system, 22 patients (62.9%) had stage IIIC1 disease, 7 (20.0%) had stage IIIC2, 3 (8.6%) had stage IIIB, 2 (5.7%) had stage IIB, and 1 (2.9%) had stage IVA disease. Histopathologically, 31 patients (88.6%) had squamous cell carcinoma, 3 (8.6%) had adenocarcinoma, and 1 (2.9%) had clear cell carcinoma. Of the 35 patients, 23 (65.7%) had reached the scheduled post-treatment follow-up time and were evaluable for treatment response according to RECIST 1.1, whereas 12 patients (34.3%) had not yet reached the scheduled time for post-treatment assessment. Among the 23 evaluable patients, 15 (65.2%) achieved a complete response (CR), while 8 (34.8%) achieved a partial response (PR). Following CCRT, the longest tumor diameter measured by MRI decreased by a mean of 94.8% in the CR group and 65.3% in the PR group. The reduction in tumor diameter was significantly greater in the CR group than in the PR group (p=0.0016). The mean baseline primary tumor SUVmax on 18F-FDG PET/CT was 16.0±9.8 in the CR group and 24.3±14.5 in the PR group. Although the baseline SUVmax tended to be lower in the CR group, the difference did not reach statistical significance (Mann–Whitney U test, p=0.059). A higher baseline SUVmax tended to be associated with a smaller reduction in tumor diameter following CCRT. Conclusion CCRT resulted in a significant reduction in primary tumor size in patients with cervical cancer. The reduction in tumor size was significantly greater among patients who achieved a complete response than among those who achieved a partial response. Although pre-treatment 18F-FDG PET/CT findings suggest that MRI-based tumor size assessment combined with metabolic information from 18F-FDG PET/CT may provide complementary information for evaluating treatment response following CCRT.

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: Chest radiography is a fundamental imaging modality used to diagnose acute and chronic pulmonary diseases. It is relatively inexpensive, rapid, and can be performed at the bedside. Pulmonary abnormalities identified on chest radiographs are generally classified into two main categories: diffuse and focal. Depending on lesion size and image quality, abnormalities can be detected with an estimated accuracy of 70–85%. However, radiologists may overlook findings due to several factors, including heavy workloads, emergency conditions, and the presence of small focal lesions measuring less than 1 cm. Previous studies evaluating the application of artificial intelligence (AI) to chest radiographs for detecting lung cancer, COVID-19, and pneumonia have reported that the sensitivity of diagnostic interpretations improves from 88% to 95% when radiologists and AI work together. Aim: To evaluate selected thoracic abnormalities on chest radiographs using artificial intelligence and compare the results with radiologists’ interpretations. Materials and Methods: This retrospective study was conducted in the Department of Radiology at the Mongolia–Japan Hospital of the Mongolian National University of Medical Sciences. A total of 845 chest radiographs obtained between September 10 and September 30, 2022, were reviewed. After applying the exclusion criteria, 721 chest radiographs were included. The interpretations provided by four radiologists were compared with the results generated by an AI system. Results: A total of 721 posteroanterior chest radiographs were included in the study, comprising 377 images from male patients and 344 from female patients. By age group, the images included 24 from patients aged 10–19 years, 114 from those aged 20–29 years, 150 from those aged 30–39 years, 147 from those aged 40–49 years, 97 from those aged 50–59 years, 96 from those aged 60–69 years, 58 from those aged 70–79 years, and 37 from those aged 80–89 years. Radiologists identified abnormalities in 331 (46%) radiographs and reported no abnormalities in 390 (54%). In comparison, the AI system classified 392 (54%) radiographs as abnormal and 329 (46%) as normal. Using the radiologists’ interpretations as the reference standard, the AI system demonstrated a specificity of 95.8% and a sensitivity of 89% for detecting abnormalities on chest radiographs. Conclusion The AI system demonstrated high sensitivity and specificity in detecting abnormalities on chest radiographs, suggesting its potential utility in clinical practice. Its application may be particularly beneficial in supporting the interpretation of chest radiographs used for preventive screening and early detection of pulmonary abnormalities.