A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme

Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall survival (OS) in patients with Glioblastoma Multiforme (GBM). This study comprised a discovery data set of 75 patients and an independent validation data set of 37 patients. A total of 1403 handcrafted features and 98304 deep features were extracted from preoperative multi-modality MR images. After feature selection, a six-deep-feature signature was constructed by using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics…

• NN
  National Natural Science Foundation of China

  Awards: 61371146, 61521062, 2015CB755500, 61571432, No. 61571432, 61422112
• NH
  National High-tech Research and Development Program
• NK
  National Key Research and Development Program of China

  Award: 2015CB755500