Development of a nomograph integrating radiomics and deep features based on MRI to predict the prognosis of high grade Gliomas

Abstract

The purpose of this study was to assess the overall survival of patients with HGG using a nomogram which combines the optimized radiomics with deep signatures extracted from 3D Magnetic Resonance Images (MRI) as well as clinical predictors. One training cohort of 168 HGG patients and one validation cohort of 42 HGG patients were enrolled in this study. From each patient's 3D MRI, 1284 radiomics features were extracted, and 8192 deep features were extracted via transfer learning. By using Least Absolute Shrinkage and Selection Operator (LASSO) regression to select features, the radiomics signatures and deep signatures were generated. The radiomics and deep features were then analyzed synthetically to generate a combined signature. Finally, the nomogram was developed by integrating the combined signature and clinical predictors. The radiomics and deep signatures were significantly associated with HGG patients' survival time. The signature derived from the synthesized radiomics and deep features showed a better prognostic performance than those from radiomics or deep features alone. The nomogram we developed takes the advantages of both radiomics and deep signatures, and also integrates the predictive ability of clinical indicators. The calibration curve shows our predicted survival time by the nomogram was very close to the actual time.

Keywords

• high grade gliomas
• radiomics
• transfer learning
• magnetic resonance imaging
• nomogram