Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma

Eur Radiol. 2019 Sep;29(9):4718-4729. doi: 10.1007/s00330-018-5984-z. Epub 2019 Feb 1.


Objectives: Integrating multiple imaging modalities is crucial for MRI data interpretation. The purpose of this study is to determine whether a previously proposed multi-view approach can effectively integrate the histogram features from multi-parametric MRI and whether the selected features can offer incremental prognostic values over clinical variables.

Methods: Eighty newly-diagnosed glioblastoma patients underwent surgery and chemoradiotherapy. Histogram features of diffusion and perfusion imaging were extracted from contrast-enhancing (CE) and non-enhancing (NE) regions independently. An unsupervised patient clustering was performed by the multi-view approach. Kaplan-Meier and Cox proportional hazards regression analyses were performed to evaluate the relevance of patient clustering to survival. The metabolic signatures of patient clusters were compared using multi-voxel spectroscopy analysis. The prognostic values of histogram features were evaluated by survival and ROC curve analyses.

Results: Two patient clusters were generated, consisting of 53 and 27 patients respectively. Cluster 2 demonstrated better overall survival (OS) (p = 0.007) and progression-free survival (PFS) (p < 0.001) than Cluster 1. Cluster 2 displayed lower N-acetylaspartate/creatine ratio in NE region (p = 0.040). A higher mean value of anisotropic diffusion in NE region was associated with worse OS (hazard ratio [HR] = 1.40, p = 0.020) and PFS (HR = 1.36, p = 0.031). The seven features selected by this approach showed significantly incremental value in predicting 12-month OS (p = 0.020) and PFS (p = 0.022).

Conclusions: The multi-view clustering method can provide an effective integration of multi-parametric MRI. The histogram features selected may be used as potential prognostic markers.

Key points: • Multi-parametric magnetic resonance imaging captures multi-faceted tumor physiology. • Contrast-enhancing and non-enhancing tumor regions represent different tumor components with distinct clinical relevance. • Multi-view data analysis offers a method which can effectively select and integrate multi-parametric and multi-regional imaging features.

Keywords: Glioblastoma; Machine learning; Magnetic resonance imaging; Prognosis; Survival analysis.

MeSH terms

  • Brain / diagnostic imaging
  • Brain / pathology
  • Brain Neoplasms / diagnostic imaging*
  • Brain Neoplasms / pathology
  • Cluster Analysis
  • Contrast Media
  • Female
  • Glioblastoma / diagnostic imaging*
  • Glioblastoma / pathology
  • Humans
  • Image Enhancement / methods
  • Kaplan-Meier Estimate
  • Magnetic Resonance Imaging / methods*
  • Magnetic Resonance Spectroscopy / methods
  • Male
  • Middle Aged
  • Phenotype
  • Proportional Hazards Models
  • Reproducibility of Results
  • Retrospective Studies


  • Contrast Media