A comprehensive texture feature analysis framework of renal cell carcinoma: pathological, prognostic, and genomic evaluation based on CT images.
Kai WuPeng WuKai YangZhe LiSijia KongLu YuEnpu ZhangHanlin LiuQing GuoSong WuPublished in: European radiology (2021)
• The analytical framework exhibits high-performance pathological classification of renal cell carcinoma and is on a par with human radiologists. • Quantitative decomposition of the predictive model shows that specific texture features contribute to histologic subtype and tumor stage classification. • Structural equation modeling shows the associations of genomic characteristics to CT texture features. Overall survival and molecular characteristics can be inferred by quantitative CT texture analysis in renal cell carcinoma.
Keyphrases
- renal cell carcinoma
- contrast enhanced
- deep learning
- machine learning
- computed tomography
- magnetic resonance imaging
- image quality
- dual energy
- magnetic resonance
- endothelial cells
- artificial intelligence
- positron emission tomography
- copy number
- convolutional neural network
- dna methylation
- optical coherence tomography
- mass spectrometry
- single molecule