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


Journal

Neurosurgery
ISSN: 1524-4040
Titre abrégé: Neurosurgery
Pays: United States
ID NLM: 7802914

Informations de publication

Date de publication:
01 10 2019
Historique:
received: 26 01 2018
accepted: 07 08 2018
pubmed: 22 9 2018
medline: 26 3 2020
entrez: 22 9 2018
Statut: ppublish

Résumé

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. 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. 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. 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. 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.

Sections du résumé

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.

Identifiants

pubmed: 30239840
pii: 5099458
doi: 10.1093/neuros/nyy388
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

524-534

Subventions

Organisme : Department of Health
ID : NIHR/CS/009/011
Pays : United Kingdom

Informations de copyright

Copyright © 2018 by the Congress of Neurological Surgeons.

Auteurs

Chao Li (C)

Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom.
Department of Neurosurgery, Shanghai General Hospital (originally named "Shanghai First People's Hospital"), Shanghai Jiao Tong University School of Medicine, China.

Shuo Wang (S)

Department of Radiology, University of Cambridge, Cambridge, United Kingdom.

Jiun-Lin Yan (JL)

Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom.
Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Taiwan.
Chang Gung University College of Medicine, Taoyuan, Taiwan.

Rory J Piper (RJ)

Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom.

Hongxiang Liu (H)

Molecular Malignancy Laboratory, Hematology and Oncology Diagnostic Service, Addenbrooke's Hospital, Cambridge, United Kingdom.

Turid Torheim (T)

Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom.
CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge, United Kingdom.

Hyunjin Kim (H)

Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom.

Jingjing Zou (J)

Statistical laboratory, Centre for Mathematical Sciences, University of Cambridge, United Kingdom.

Natalie R Boonzaier (NR)

Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom.
Developmental Imaging and Biophysics Section, Institute of Child Health, University College London, London, United Kingdom.

Rohitashwa Sinha (R)

Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom.

Tomasz Matys (T)

Department of Radiology, University of Cambridge, Cambridge, United Kingdom.
Cancer Trials Unit Department of Oncology, Addenbrooke's Hospital, Cambridge, United Kingdom.

Florian Markowetz (F)

Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom.
CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge, United Kingdom.

Stephen J Price (SJ)

Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom.
Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom.

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