Background: Renal cell carcinoma (RCC) accounts for 85–90% of all primary renal malignancies in adults and 1–3% of all cancers worldwide, with incidence rising steadily year by year. Clear cell (ccRCC), papillary (pRCC), and chromophobe (chRCC) subtypes differ significantly in genetic alterations, biological behavior, and treatment response, making preoperative subtype identification clinically important. Conventional qualitative CT assessment is limited, and contrast-enhanced CT-based radiomics may represent a novel diagnostic approach. Aim: To develop a model for preoperative differentiation of ccRCC from non-ccRCC subtypes using contrast-enhanced CT-based radiomics, and to evaluate its diagnostic performance through internal and external validation. Materials and Methods: A retrospective study was conducted on 121 patients with histopathologically confirmed RCC treated at the Urology–Andrology Center of the First Central Hospital of Mongolia between 2021 and 2024. Patients were categorized into ccRCC (n=84) and non-ccRCC (n=37) groups. Tumor segmentation was performed on corticomedullary phase CT images using 3D Slicer software, and a total of 122 radiomic features were extracted, including first-order uniformity, gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), shape flatness, and GLCM maximum probability. Diagnostic performance was assessed by ROC analysis, and model generalizability was evaluated through external validation (n=73). Results: GLRLM demonstrated a statistically significant difference between the ccRCC (mean 0.1230) and non-ccRCC (mean 0.0170) groups (p<0.001). ROC analysis yielded an optimal cut-off value of 0.1493 for GLRLM, with an internal validation AUC of 0.9103, sensitivity of 83.3%, and specificity of 92.3% (p<0.01). External validation (n=73) achieved an AUC of 0.860 and an accuracy of 86.0%, confirming the model’s generalizability. First-order uniformity (AUC=0.880) and variance (AUC=0.862) showed comparable discriminative performance to GLRLM, whereas flatness (AUC=0.584) and GLCM Imc2 (AUC=0.621) demonstrated relatively poor discriminative ability. Confusion matrix analysis yielded an accuracy of 86.0% in both internal and external validation cohorts. Conclusion Non-invasive CT-based radiomics demonstrates the ability to accurately differentiate RCC subtypes preoperatively with high precision (AUC=0.92), as confirmed by both internal and external validation.

Background: The discovery of insulin over a century ago significantly advanced the treatment of diabetes mellitus (DM). Effective glycemic control in patients on insulin therapy is influenced by a variety of factors. While developed countries have transitioned fully to insulin analogs, abandoning recombinant insulin, Mongolia lacks comprehensive research on how different insulin formulations influence glycemic control. This gap highlights the need for targeted improvements in therapy effectiveness and service quality. Materials and methods: The cross-sectional study was conducted at the Central Hospital of the Mongolian National University of Medical Sciences, including 183 diabetic patients aged 18-65 years. Participants were managed by endocrinologists and participated in a survey consisting of 44 questions alongside physical measurements. Data were 78 analyzed using SPSS version 25, with a significance threshold of p<0.05. Results: The study population had a mean age of 56.7 ± 11.0 years, with 55.2% (n=101) males. The mean duration of diagnosed DM was 12.6 ± 6.33 years, and the mean duration since the initiation of insulin therapy was 6.77 (1-24) years. The mean glycated hemoglobin (HbA1c) was 10.5%, with 87.4% of the participants categorized under poor glycemic control and only 5.5% maintaining optimal levels. Insulin usage was predominantly recombinant (60.7%), with analogue at 32.2%, and mixed regimens at 7.1%. Usage of insulin pens was reported by 43.2% of the participants. The daily insulin dosage ranged from 10 to 71 units, averaging 37.2 ± 13.7 units. Multivariate logistic regression analysis confirmed that inadequate knowledge about insulin therapy is an independent risk factor (OR; 95% CI = 1.48; 1.21 10.32). When adjusting for confounding factors, the average glycated hemoglobin (HbA1c) levels in the group with inadequate knowledge was significantly higher (11.8%) compared to the group with adequate knowledge, where it was significantly lower (10.2%). Conclusion The use of analog insulin among patients with type 2 diabetes undergoing insulin therapy is relatively low (32%), and glycemic control is poor (87%). Therefore, it is necessary to increase the use of analog insulin and improve glycemic control. Inadequate knowledge about insulin therapy negatively affects glycemic control in patients with type 2 diabetes (OR; 95% CI = 1.48; 1.21-10.32). Therefore, it is essential to enhance patients’ knowledge.