3D texture feature learning for noninvasive estimation of gliomas pathological subtype

Abstract

Pathological subtype saved as an important marker in gliomas has considerable diagnostic and prognostic values. However, previous identification of pathological subtype relies on tumor samples, which is invasive. In this paper, we proposed a 3D texture feature learning method which is based on sparse representation (SR) theory to noninvasively estimate the pathological subtype for gliomas. Firstly, we developed a 3D patch-based SR model to extract 3D tumor texture features form magnetic resonance (MR) images. Then, by considering the physical meaning and characteristics of the extracted features, instead of performing feature selection directly, we further extract some deep features describing the statistical difference of the texture features of different tumors for subtype estimation. 213 subjects are divide into cross validation cohort and independent testing cohort to validate the proposed method. The proposed method achieves encouraging performance, with the accuracy of 91.43% and 88.57% by using T1 contrast-enhanced and T2-Flair MR images, respectively.