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
- newly diagnosed
- dual energy
- chronic kidney disease
- diffusion weighted imaging
- peritoneal dialysis
- prognostic factors
- artificial intelligence
- robot assisted
- liver metastases
- replacement therapy