Dataset Information

Real-time multi-modality imaging of glioblastoma tumor resection and recurrence.

ABSTRACT: The lack of relevant pre-clinical animal models incorporating the clinical scenario of Glioblastoma multiforme (GBM) resection and recurrence has contributed significantly to the inability to successfully treat GBM. A multi-modality imaging approach that allows real-time assessment of tumor resection during surgery and non-invasive detection of post-operative tumor volumes is urgently needed. In this study, we report the development and implementation of an optical imaging and magnetic resonance imaging (MRI) approach to guide GBM resection during surgery and track tumor recurrence at multiple resolutions in mice. Intra-operative fluorescence-guided surgery allowed real-time monitoring of intracranial tumor removal and led to greater than 90 % removal of established intracranial human GBM. The fluorescent signal clearly delineated tumor margins, residual tumor, and correlated closely with the clinically utilized fluorescence surgical marker 5-aminolevulinic acid/porphyrin. Post-operative non-invasive optical imaging and MRI confirmed near-complete tumor removal, which was further validated by immunohistochemistry (IHC). Longitudinal non-invasive imaging and IHC showed rapid recurrence of multi-focal tumors that exhibited a faster growth rate and altered blood-vessel density compared to non-resected tumors. Surgical tumor resection significantly extended long-term survival, however mice ultimately succumbed to the recurrent GBM. This multi-modality imaging approach to GBM resection and recurrence in mice should provide an important platform for investigating multiple aspects of GBM and ultimately evaluating novel therapeutics.

SUBMITTER: Hingtgen S

PROVIDER: S-EPMC3548430 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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Real-time multi-modality imaging of glioblastoma tumor resection and recurrence.

Hingtgen Shawn S Figueiredo Jose-Luiz JL Farrar Christian C Duebgen Matthias M Martinez-Quintanilla Jordi J Bhere Deepak D Shah Khalid K

Journal of neuro-oncology 20121216 2

The lack of relevant pre-clinical animal models incorporating the clinical scenario of Glioblastoma multiforme (GBM) resection and recurrence has contributed significantly to the inability to successfully treat GBM. A multi-modality imaging approach that allows real-time assessment of tumor resection during surgery and non-invasive detection of post-operative tumor volumes is urgently needed. In this study, we report the development and implementation of an optical imaging and magnetic resonance ...[more]

PMID: 23242736

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Project description:ObjectivesIntegrating 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.MethodsEighty 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.ResultsTwo 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).ConclusionsThe 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.

Dataset Information

Real-time multi-modality imaging of glioblastoma tumor resection and recurrence.

Publications

Real-time multi-modality imaging of glioblastoma tumor resection and recurrence.

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