Reproducibility of radiomic features in CT images of NSCLC patients: an integrative analysis on the impact of acquisition and reconstruction parameters.
Lisa RinaldiSimone P De AngelisSara RaimondiStefania RizzoCristiana FanciulloCristiano RampinelliManuel MarianiAlessandro LascialfariMarta CremonesiRoberto OrecchiaDaniela OriggiFrancesca BottaPublished in: European radiology experimental (2022)
Combining univariate and multivariable models is allowed to identify features for which corrections may be applied to reduce the trend with the algorithm and increase reproducibility. Subsequent clustering may be applied to eliminate residual redundancy.
Keyphrases
- end stage renal disease
- deep learning
- ejection fraction
- small cell lung cancer
- newly diagnosed
- chronic kidney disease
- machine learning
- computed tomography
- prognostic factors
- peritoneal dialysis
- convolutional neural network
- single cell
- magnetic resonance
- rna seq
- image quality
- advanced non small cell lung cancer
- patient reported outcomes
- positron emission tomography
- dual energy
- neural network