Similar Items: Interpretable machine learning model using CT body composition combined with inflammatory and nutritional indicators to predict pathological complete response after neoadjuvant therapy in breast cancer: a retrospective study
- Machine learning-driven PET-CT and clinical pathology model for predicting mediastinal lymph node metastasis in non-small cell lung cancer: a retrospective cohort study
- Association between abdominal CT-based body composition parameters and early diabetic kidney disease in type 2 diabetes mellitus: a retrospective cross-sectional study
- Pathological features of BRCA-mutated breast cancer in Shenzhen, China: a single-center study
- Single-inspiratory quantitative CT nomogram for enhanced PRISm and COPD differentiation: a cross-sectional study with interpretable diagnostic boundaries
- Influence of posture during mastication on body composition and nutritional intake in individuals with Down syndrome
- A retrospective investigation of the platelet-to-lymphocyte ratio as a potential indicator in early rheumatoid arthritis