Machine learning-based analysis of CT radiomics model for prediction of colorectal metachronous liver metastases.
Marjaneh TaghaviStefano TrebeschiRita SimõesDavid B MeekRianne C J BeckersDoenja M J LambregtsCornelis VerhoefJanneke B HouwersUulke A van der HeideRegina G H Beets-TanMonique MaasPublished in: Abdominal radiology (New York) (2021)
A machine learning-based radiomics analysis of routine clinical CT imaging at primary staging can provide valuable biomarkers to identify patients at high risk for developing colorectal liver metastases.
Keyphrases
- liver metastases
- machine learning
- contrast enhanced
- end stage renal disease
- computed tomography
- image quality
- chronic kidney disease
- ejection fraction
- dual energy
- newly diagnosed
- lymph node metastasis
- magnetic resonance imaging
- artificial intelligence
- high resolution
- big data
- lymph node
- squamous cell carcinoma
- magnetic resonance
- positron emission tomography
- clinical practice
- patient reported outcomes
- mass spectrometry
- patient reported
- photodynamic therapy