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European Radiology
Published in: European Radiology 9/2019

Open Access 01-09-2019 | Glioblastoma | Oncology

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

Authors: Chao Li, Shuo Wang, Angela Serra, Turid Torheim, Jiun-Lin Yan, Natalie R. Boonzaier, Yuan Huang, Tomasz Matys, Mary A. McLean, Florian Markowetz, Stephen J. Price

Published in: European Radiology | Issue 9/2019

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Abstract

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.
Appendix
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Metadata
Title
Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma
Authors
Chao Li
Shuo Wang
Angela Serra
Turid Torheim
Jiun-Lin Yan
Natalie R. Boonzaier
Yuan Huang
Tomasz Matys
Mary A. McLean
Florian Markowetz
Stephen J. Price
Publication date
01-09-2019
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 9/2019
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-018-5984-z

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