Reproducibility of CT radiomic features in lung neuroendocrine tumours (NETs) patients: analysis in a heterogeneous population.
Eleonora BicciDiletta CozziEdoardo CavigliRon RuzgaElena BertelliGinevra DantiSilvia BettariniPaolo TortoliLorenzo Nicola MazzoniSimone BusoniVittorio MielePublished in: La Radiologia medica (2023)
Some radiomic features can be used as a valid and reproducible tool for predicting Ki-67 class and hence the subtype of lung NET in baseline and post-contrast enhanced CT images. In particular, in baseline examination three features can establish both tumour class and aggressiveness.
Keyphrases
- contrast enhanced
- computed tomography
- magnetic resonance imaging
- diffusion weighted
- dual energy
- magnetic resonance
- end stage renal disease
- diffusion weighted imaging
- ejection fraction
- newly diagnosed
- chronic kidney disease
- image quality
- peritoneal dialysis
- positron emission tomography
- deep learning
- squamous cell carcinoma
- machine learning
- convolutional neural network
- radiation therapy
- patient reported outcomes