An automatic texture feature analysis framework of renal tumor: surgical, pathological, and molecular evaluation based on multi-phase abdominal CT.
Huancheng YangHanlin LiuJiashan LinHongwei XiaoYiqi GuoHangru MeiQiuxia DingYangguang YuanXiaohui LaiKai WuSong WuPublished in: European radiology (2023)
• The automatic texture feature analysis framework based on multi-phase abdominal CT can provide more accurate prediction of benign and malignant, histological subtype, pathological stage, nephrectomy risk, pathological grade, and Ki67 index in renal tumor. • The quantitative decomposition of the prediction model was conducted to explore the contribution of the extracted feature. • The study involving 1051 patients from 5 medical centers, along with a heterogeneous external data testing strategy, can be seamlessly transferred to various tasks involving new datasets.
Keyphrases
- deep learning
- machine learning
- contrast enhanced
- end stage renal disease
- computed tomography
- healthcare
- high resolution
- chronic kidney disease
- neural network
- magnetic resonance imaging
- image quality
- ejection fraction
- dual energy
- squamous cell carcinoma
- prognostic factors
- working memory
- peritoneal dialysis
- mass spectrometry
- electronic health record
- robot assisted