An interpretable machine learning model based on contrast-enhanced CT parameters for predicting treatment response to conventional transarterial chemoembolization in patients with hepatocellular carcinoma.
Lu ZhangZhe JinChen LiZicong HeBin ZhangQiuying ChenJingjing YouXiao MaHui ShenFei WangLingeng WuCunwen MaShuixing ZhangPublished in: La Radiologia medica (2024)
The RF-combined model can serve as a robust and interpretable tool to identify the appropriate crowd for cTACE sessions, sparing patients from receiving ineffective and unnecessary treatments.
Keyphrases
- contrast enhanced
- magnetic resonance imaging
- computed tomography
- machine learning
- diffusion weighted
- end stage renal disease
- magnetic resonance
- ejection fraction
- chronic kidney disease
- dual energy
- newly diagnosed
- diffusion weighted imaging
- peritoneal dialysis
- radiofrequency ablation
- robot assisted
- patient reported outcomes
- positron emission tomography
- artificial intelligence
- image quality
- deep learning