Multimodal MRI can identify perfusion and metabolic changes in the invasive margin of glioblastomas

Price, Stephen J.; Young, Adam M. H.; Scotton, William; Ching, Jared; Mohsen, Laila; Boonzaier, Natalie R.; Lupson, Victoria; Griffiths, John R.; McLean, Mary A.; Larkin, Timothy J.

doi:10.1002/jmri.24996

Cited by 48 publications

(54 citation statements)

References 51 publications

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“…All the concentrations of metabolite were calculated as a ratio to creatine (Cr). All relevant spectra from CSI voxels of interest were assessed for artefacts using previous criteria [24]. The values of the Cramer–Rao lower bounds were used to evaluate the quality and reliability of CSI data and values with standard deviation (SD) > 20% were discarded.…”

Section: Methodsmentioning

confidence: 99%

Multi-Parametric and Multi-Regional Histogram Analysis of MRI: Revealing Imaging Phenotypes of Glioblastoma Correlated with Patient Survival

Wang

Serra

et al. 2017

Preprint

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IntroductionGlioblastoma is characterized by its remarkable heterogeneity and dismal prognosis. Histogram analysis of quantitative magnetic resonance imaging (MRI) is an important in vivomethod to study intratumoral heterogeneity. With large amounts of histogram features generated, integrating these modalities effectively for clinical decision remains a challenge. MethodsA total of 80 patients with supratentorial primary glioblastoma were recruited. All patients received surgery and standard regimen of temozolomide chemoradiotherapy. Diagnosis was confirmed by pathology. Anatomical T2-weighted, T1-weighted post-contrast and FLAIR images, as well as dynamic susceptibility contrast (DSC), diffusion tensor imaging (DTI) and chemical shift imaging were acquired preoperatively using a 3T MRI scanner. DTI-p, DTI-q, relative cerebral blood volume (rCBV), mean transit time (MTT) and relative cerebral blood flow (rCBF) maps were generated. Contrast-enhancing (CE) and non-enhancing (NE) regions of interest were manually delineated. Voxel intensity histograms were constructed from the CE and NE regions independently. Patient clustering was performed by the Multi-View Biological Data Analysis (MVDA) approach. Kaplan-Meier and Cox proportional hazards regression analyses were performed to evaluate the relevance of the patient clustering to survival. The histogram features selected from MVDA approach were evaluated using receiver operator characteristics (ROC) curve analysis. The metabolic signatures of the patient clusters were analyzed by multivoxel MR spectroscopy (MRS).All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/235861 doi: bioRxiv preprint first posted online Dec. 18, 2017; ResultsThe MVDA approach yielded two final patient clusters, consisting of 53 and 27 patients respectively. The two patient subgroups showed significance for overall survival (p = 0.007, HR = 0.32) and progression-free survival (p < 0.001, HR = 0.33) in multivariate Cox regression analysis. Among the features selected by MVDA, higher mean value of DTI-q in the non-enhancing region contributed to a worse OS (HR = 1.40, p = 0.020) and worse PFS (HR = 1.36, p = 0.031). Multivoxel MRS showed N-acetylaspartate/creatine (NAA/Cr) ratio between the two clusters, both in the CE region (p < 0.001) and NE region (p = 0.013).Glutamate/Cr (Glu/Cr) ratio and glutamate + glutamine/Cr (Glx/Cr) of the cluster 1 was significantly lower than cluster 2 (p = 0.037, and 0.027 respectively) In the NE region. DiscussionThis study demonstrated that integrating multi-parametric and multi-regional MRI histogram features may help to stratify patients. The histogram features selected from the proposed approach may be used as potential imaging markers in personalized treatment strategy and response determination.Key words: Glioblastoma, heterogeneity, magnetic resonance i...

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Section: Methodsmentioning

confidence: 99%

Multi-Parametric and Multi-Regional Histogram Analysis of MRI: Revealing Imaging Phenotypes of Glioblastoma Correlated with Patient Survival

Wang

Serra

et al. 2017

Preprint

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“…All relevant spectra from MRS voxels of interest were assessed for artefacts using previously published criteria. 24 The values of the Cramer-Rao lower bounds were used to evaluate the quality and reliability of MRS data and values with standard deviation (SD) > 20% were discarded. 24…”

Section: Image Processingmentioning

confidence: 99%

Low Perfusion Compartments in Glioblastoma Quantified by Advanced Magnetic Resonance Imaging and Correlated with Patient Survival

Yan

Torheim

et al. 2017

Preprint

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BackgroundGlioblastoma exhibits profound intratumoral heterogeneity in blood perfusion, which may cause inconsistent therapy response. Particularly, low perfusion may create hypoxic microenvironment and induce resistant clones. Thus, developing validated imaging approaches that define low perfusion compartments is crucial for clinical management.MethodsA total of 112 newly-diagnosed supratentorial glioblastoma patients were prospectively recruited for maximal safe resection. Preoperative MRI included anatomical, dynamic susceptibility contrast (DSC), diffusion tensor imaging (DTI) and chemical shift imaging (CSI). The apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) were calculated from DTI and DSC respectively. Using thresholding methods, two low perfusion compartments (ADCH-rCBVL and ADCL-rCBVL) were identified. Volumetric analysis was performed. Lactate and macromolecule/lipid levels were determined from multivoxel spectroscopy. Progression-free survival (PFS) and overall survival (OS) were analysed using Kaplan-Meier and multivariate Cox regression analyses.ResultsTwo compartments displayed higher lactate and macromolecule/lipid levels than normal controls (each P < 0.001), suggesting hypoxic and pro-inflammatory microenvironment. The proportional volume of ADCL-rCBVL compartment was associated with a larger infiltration area (P < 0.001, rho = 0.42). Lower lactate in this compartment was associated with a less invasive phenotype visualized on DTI. Multivariate Cox regression showed higher lactate level in the ADCL-rCBVL compartment was associated with a worse survival (PFS: HR 2.995, P = 0.047; OS: HR 4.974, P = 0.005).ConclusionsThe ADCL-rCBVL compartment represent a treatment resistant sub-region associated with glioblastoma invasiveness. This approach was based on clinically available imaging modalities and could thus provide crucial pretreatment information for clinical decision making.

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“…These ROIs are usually delineated using the information provided by morphological MRI sequences such as T 1 weighted, T 2 weighted, FLAIR, and T 1 weighted post‐gadolinium ( T 1 c ) acquisitions. Recent approaches, however, focus on the definition of functional habitats based on tissue features at the voxel scale through the information provided by MRI sequences such as diffusion weighted imaging, perfusion weighted imaging (PWI), or MRS, among others …”

Section: Introductionmentioning

confidence: 99%

“…Recent approaches, however, focus on the definition of functional habitats based on tissue features at the voxel scale through the information provided by MRI sequences such as diffusion weighted imaging, perfusion weighted imaging (PWI), or MRS, among others. [15][16][17][18][19][20] These complex studies describing the heterogeneity of GBM at voxel level require the use of automated multiparametric image analysis methods. [21][22][23][24][25] In particular, the study of Juan-Albarracín et al 26 proposed a methodology to describe GBM vascular intrapatient heterogeneity based on a structured unsupervised multiparametric image analysis technology.…”

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confidence: 99%

Improving the estimation of prognosis for glioblastoma patients by MR based hemodynamic tissue signatures

et al. 2018

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Advanced MRI and molecular markers have been raised as crucial to improve prognostic models for patients having glioblastoma (GBM) lesions. In particular, different MR perfusion based markers describing vascular intrapatient heterogeneity have been correlated with tumor aggressiveness, and represent key information to understand tumor resistance against effective therapies of these neoplasms. Recently, hemodynamic tissue signature (HTS) markers based on MR perfusion images have been demonstrated to be useful for describing the heterogeneity of GBM at the voxel level, as well as demonstrating significant correlations with the patient's overall survival. In this work, we analyze the abilities of these markers to improve the conventional prognostic models based on clinical, morphological, and demographic features. Our results, in both the regression and classification tests, show that inclusion of the HTS markers improves the reliability of prognostic models. The HTS method is fully automatic and it is available for research use at http://www.oncohabitats.upv.es.

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Multimodal MRI can identify perfusion and metabolic changes in the invasive margin of glioblastomas

Cited by 48 publications

References 51 publications

Multi-Parametric and Multi-Regional Histogram Analysis of MRI: Revealing Imaging Phenotypes of Glioblastoma Correlated with Patient Survival

Multi-Parametric and Multi-Regional Histogram Analysis of MRI: Revealing Imaging Phenotypes of Glioblastoma Correlated with Patient Survival

Low Perfusion Compartments in Glioblastoma Quantified by Advanced Magnetic Resonance Imaging and Correlated with Patient Survival

Improving the estimation of prognosis for glioblastoma patients by MR based hemodynamic tissue signatures

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