Hierarchical non-negative matrix factorization to characterize brain tumor heterogeneity using multi-parametric MRI

Nicolas Sauwen*, Diana M. Sima, Sofie Van Cauter, Jelle Veraart, Alexander Leemans, Frederik Maes, Uwe Himmelreich, Sabine Van Huffel

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Tissue characterization in brain tumors and, in particular, in high-grade gliomas is challenging as a result of the co-existence of several intra-tumoral tissue types within the same region and the high spatial heterogeneity. This study presents a method for the detection of the relevant tumor substructures (i.e. viable tumor, necrosis and edema), which could be of added value for the diagnosis, treatment planning and follow-up of individual patients. Twenty-four patients with glioma [10 low-grade gliomas (LGGs), 14 high-grade gliomas (HGGs)] underwent a multi-parametric MRI (MP-MRI) scheme, including conventional MRI (cMRI), perfusion-weighted imaging (PWI), diffusion kurtosis imaging (DKI) and short-TE 1H MRSI. MP-MRI parameters were derived: T2, T1+contrast, fluid-attenuated inversion recovery (FLAIR), relative cerebral blood volume (rCBV), mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis (MK) and the principal metabolites lipids (Lip), lactate (Lac), N-acetyl-aspartate (NAA), total choline (Cho), etc. Hierarchical non-negative matrix factorization (hNMF) was applied to the MP-MRI parameters, providing tissue characterization on a patient-by-patient and voxel-by-voxel basis. Tissue-specific patterns were obtained and the spatial distribution of each tissue type was visualized by means of abundance maps. Dice scores were calculated by comparing tissue segmentation derived from hNMF with the manual segmentation by a radiologist. Correlation coefficients were calculated between each pathologic tissue source and the average feature vector within the corresponding tissue region. For the patients with HGG, mean Dice scores of 78%, 85% and 83% were obtained for viable tumor, the tumor core and the complete tumor region. The mean correlation coefficients were 0.91 for tumor, 0.97 for necrosis and 0.96 for edema. For the patients with LGG, a mean Dice score of 85% and mean correlation coefficient of 0.95 were found for the tumor region. hNMF was also applied to reduced MRI datasets, showing the added value of individual MRI modalities.

Original languageEnglish
Pages (from-to)1599-1624
Number of pages26
JournalNMR in Biomedicine
Issue number12
Publication statusPublished - 2015


  • Dynamic contrast methods
  • Head and neck cancer
  • High-order diffusion MR methods
  • Multimodality imaging
  • Non-negative matrix factorization
  • Spectroscopic imaging


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