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.

Acceleration of emissions reductions in household coal stoves and modest improvements in other sectors, however, have the potential to considerably lower outdoor pollution and reduce total exposures to about 70% of those today (Scenario 1). Reducing total exposures closer to these international benchmark levels will require moving away from coal and wood as household fuels and even more control on other sources (Scenario 2). The first package of moderate control measures (Scenario 1) considered in this assessment will result in a slow decline in impacts(Figure) and a cumulative health savings over trends in 2013, but leave annual per capita health impacts only about 25% lower than today after ten years. A more aggressive set of control measures (Scenario 2), however, will result in more health protection over the period and reduce annual impacts by approximately 60% from current levels in 2025 . In terms of impact per capita,this would represent nearly a 70% reduction over the period taking population growth into account.