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Neurosurgery. 2018 Sep 17. doi: 10.1093/neuros/nyy388. [Epub ahead of print]

Intratumoral Heterogeneity of Glioblastoma Infiltration Revealed by Joint Histogram Analysis of Diffusion Tensor Imaging.

Author information

1
Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom.
2
Department of Neurosurgery, Shanghai General Hospital (originally named "Shanghai First People's Hospital"), Shanghai Jiao Tong University School of Medicine, China.
3
Department of Radiology, University of Cambridge, Cambridge, United Kingdom.
4
Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Taiwan.
5
Chang Gung University College of Medicine, Taoyuan, Taiwan.
6
Molecular Malignancy Laboratory, Hematology and Oncology Diagnostic Service, Addenbrooke's Hospital, Cambridge, United Kingdom.
7
Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom.
8
CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge, United Kingdom.
9
Statistical laboratory, Centre for Mathematical Sciences, University of Cambridge, United Kingdom.
10
Developmental Imaging and Biophysics Section, Institute of Child Health, University College London, London, United Kingdom.
11
Cancer Trials Unit Department of Oncology, Addenbrooke's Hospital, Cambridge, United Kingdom.
12
Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom.

Abstract

Background:

Glioblastoma is a heterogeneous disease characterized by its infiltrative growth, rendering complete resection impossible. Diffusion tensor imaging (DTI) shows potential in detecting tumor infiltration by reflecting microstructure disruption.

OBJECTIVE:

To explore the heterogeneity of glioblastoma infiltration using joint histogram analysis of DTI, to investigate the incremental prognostic value of infiltrative patterns over clinical factors, and to identify specific subregions for targeted therapy.

Methods:

A total of 115 primary glioblastoma patients were prospectively recruited for surgery and preoperative magnetic resonance imaging. The joint histograms of decomposed anisotropic and isotropic components of DTI were constructed in both contrast-enhancing and nonenhancing tumor regions. Patient survival was analyzed with joint histogram features and relevant clinical factors. The incremental prognostic values of histogram features were assessed using receiver operating characteristic curve analysis. The correlation between the proportion of diffusion patterns and tumor progression rate was tested using Pearson correlation.

Results:

We found that joint histogram features were associated with patient survival and improved survival model performance. Specifically, the proportion of nonenhancing tumor subregion with decreased isotropic diffusion and increased anisotropic diffusion was correlated with tumor progression rate (P = .010, r = 0.35), affected progression-free survival (hazard ratio = 1.08, P < .001), and overall survival (hazard ratio = 1.36, P < .001) in multivariate models.

Conclusion:

Joint histogram features of DTI showed incremental prognostic values over clinical factors for glioblastoma patients. The nonenhancing tumor subregion with decreased isotropic diffusion and increased anisotropic diffusion may indicate a more infiltrative habitat and potential treatment target.

PMID:
30239840
DOI:
10.1093/neuros/nyy388

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