Predicting recurrence risks in lung cancer patients using multimodal radiomics and random survival forests.
Jaryd R ChristieOmar DaherMohamed A AbdelrazekPerrin E RomineRichard A MalthanerMehdi QiabiRahul NayakSandy NapelViswam S NairSarah A MattonenPublished in: Journal of medical imaging (Bellingham, Wash.) (2022)
Our radiomic model, consisting of stage and tumor, peritumoral, and bone marrow features from CT and FDG PET-CT significantly stratified patients into low- and high-risk of recurrence/progression.
Keyphrases
- end stage renal disease
- bone marrow
- ejection fraction
- newly diagnosed
- chronic kidney disease
- mesenchymal stem cells
- peritoneal dialysis
- climate change
- squamous cell carcinoma
- patient reported outcomes
- magnetic resonance
- pain management
- chronic pain
- risk assessment
- lymph node metastasis
- positron emission tomography
- neural network