Unsupervised Machine Learning of MRI Radiomics Features Identifies Two Distinct Subgroups with Different Liver Function Reserve and Risks of Post-Hepatectomy Liver Failure in Patients with Hepatocellular Carcinoma.
Qiang WangChangfeng LiGeng ChenKai FengZhiyu ChenFeng XiaPing CaiLeida ZhangErnesto SparrelidTorkel B BrismarKuansheng MaPublished in: Cancers (2023)
Based on the radiomics features of gadoxetic-acid-enhanced MRI, unsupervised clustering analysis identified two distinct subgroups with different liver function reserves and risks of PHLF in HCC patients. Future studies are required to validate our findings.
Keyphrases
- machine learning
- contrast enhanced
- liver failure
- end stage renal disease
- magnetic resonance imaging
- diffusion weighted imaging
- ejection fraction
- hepatitis b virus
- chronic kidney disease
- lymph node metastasis
- artificial intelligence
- human health
- newly diagnosed
- peritoneal dialysis
- prognostic factors
- big data
- risk assessment
- gene expression
- single cell
- genome wide
- deep learning
- data analysis