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
- computed tomography
- magnetic resonance imaging
- machine learning
- diffusion weighted
- end stage renal disease
- magnetic resonance
- dual energy
- chronic kidney disease
- newly diagnosed
- ejection fraction
- diffusion weighted imaging
- radiofrequency ablation
- peritoneal dialysis
- positron emission tomography
- image quality
- pet ct
- big data
- liver metastases