3D Texture Feature Learning for Noninvasive Estimation of Gliomas Pathological Subtype

  • Guoqing Wu
  • Yuanyuan WangEmail author
  • Jinhua YuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11383)


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.


Gliomas Pathological subtype Radiomics Sparse representation 


  1. 1.
    Young, R.J., Knopp, E.A.: Brain MRI: tumor evaluation. J. Magn. Reson. Imaging 24(4), 709–724 (2006)CrossRefGoogle Scholar
  2. 2.
    Gao, Y., et al.: Histological grade and type classification of glioma using magnetic resonance imaging. In: CISP-BMEI, pp. 1808–1813 (2017)Google Scholar
  3. 3.
    Huang, Y., et al.: Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (i or ii) non-small cell lung cancer. Radiol. 281(3), 947–957 (2016)CrossRefGoogle Scholar
  4. 4.
    Huang, Y., et al.: Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis incolorectal cancer. J. Clin. Oncol. 34(8), 2157–2164 (2016)CrossRefGoogle Scholar
  5. 5.
    Liu, L., Zhang, H., Rekik, I., Chen, X., Wang, Q., Shen, D.: Outcome prediction for patient with high-grade gliomas from brain functional and structural networks. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 26–34. Springer, Cham (2016). Scholar
  6. 6.
    Kickingereder, P., et al.: Large-scale radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying antiangiogenic treatment response. Clin. Cancer Res. 22(23), 5765–5771 (2016)CrossRefGoogle Scholar
  7. 7.
    Kickingereder, P., et al.: Radiogenomics of glioblastoma: machine learning-based classification of molecular characteristics by using multiparametric and multiregional MR imaging features. Radiol. 281(3), 907–918 (2016)CrossRefGoogle Scholar
  8. 8.
    Wu, G., et al.: Sparse representation-based radiomics for the diagnosis of brain tumors. IEEE Trans. Med. Imaging 37(4), 893–905 (2018)CrossRefGoogle Scholar
  9. 9.
    Wu, G., Wang, Y., Yu, J.: Overall survival time prediction for high grade gliomas based on sparse representation framework. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 77–87. Springer, Cham (2018). Scholar
  10. 10.
    Tzourio-Mazoyer, N., et al.: Automated anatomical labeling of activations in SPM using a macro-scopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15(1), 273–289 (2002)CrossRefGoogle Scholar
  11. 11.
    Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Wright, J., et al.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Electronic EngineeringFudan UniversityShanghaiChina
  2. 2.The Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of ShanghaiShanghaiChina

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