Li, C;
Wang, S;
Serra, A;
Torheim, T;
Yan, J-L;
Boonzaier, NR;
Huang, Y;
... Price, SJ; + view all
(2019)
Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma.
European Radiology
10.1007/s00330-018-5984-z.
(In press).
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Boonzaier_Multi-parametric and multi-regional histogram analysis of MRI. Modality integration reveals imaging phenotypes of glioblastoma_VoR.pdf - Published Version Download (2MB) | Preview |
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.
| Type: | Article |
|---|---|
| Title: | Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma |
| Location: | Germany |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s00330-018-5984-z |
| Publisher version: | https://doi.org/10.1007/s00330-018-5984-z |
| Language: | English |
| Additional information: | © The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/). |
| Keywords: | Glioblastoma, Magnetic resonance imaging, Machine learning, urvival analysis, Prognosis, |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Developmental Neurosciences Dept |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10067493 |
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