Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients.
Yuto SugaiNoriyuki KadoyaShohei TanakaShunpei TanabeMariko UmedaTakaya YamamotoKazuya TakedaSuguru DobashiHaruna OhashiKen TakedaKeiichi JinguPublished in: Radiation oncology (London, England) (2021)
Our results showed that feature selection methods moderately affected the survival prediction performance. In addition, prediction models based on specific subgroups may improve the prediction performance. These results may prove useful for determining the optimal radiomics-based predication model.
Keyphrases
- end stage renal disease
- contrast enhanced
- machine learning
- newly diagnosed
- deep learning
- ejection fraction
- lymph node metastasis
- chronic kidney disease
- computed tomography
- peritoneal dialysis
- prognostic factors
- squamous cell carcinoma
- randomized controlled trial
- magnetic resonance
- clinical trial
- patient reported outcomes
- positron emission tomography
- study protocol