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Eur Radiol. 2019 Feb 1. doi: 10.1007/s00330-018-5984-z. [Epub ahead of print]

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

Li C1,2,3, Wang S4,5, Serra A6,7,8, Torheim T9,10, Yan JL11,12,13, Boonzaier NR11,14, Huang Y4, Matys T5, McLean MA5,9, Markowetz F9,10, Price SJ11,15.

Author information

1
Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Box 167 Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK. cl109@outlook.com.
2
Department of Neurosurgery, Shanghai General Hospital (originally named "Shanghai First People's Hospital"), Shanghai Jiao Tong University School of Medicine, Shanghai, China. cl109@outlook.com.
3
The Centre for Mathematical Imaging in Healthcare, Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, UK. cl109@outlook.com.
4
The Centre for Mathematical Imaging in Healthcare, Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, UK.
5
Department of Radiology, University of Cambridge, Cambridge, UK.
6
Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
7
Institute of Biosciences and Medical Technologies (BioMediTech), Tampere, Finland.
8
NeuRoNe Lab, DISA-MIS, University of Salerno, Fisciano, SA, Italy.
9
Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
10
CRUK & EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge, UK.
11
Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Box 167 Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
12
Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Taiwan.
13
Chang Gung University College of Medicine, Taoyuan, Taiwan.
14
Developmental Imaging and Biophysics Section, Great Ormond Street Institute of Child Health, University College London, London, UK.
15
Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.

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.

KEYWORDS:

Glioblastoma; Machine learning; Magnetic resonance imaging; Prognosis; Survival analysis

PMID:
30707277
DOI:
10.1007/s00330-018-5984-z

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