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Restriction Spectrum Imaging Improves Risk Stratification in Patients with Glioblastoma.

ABSTRACT: ADC as a marker of tumor cellularity has been promising for evaluating the response to therapy in patients with glioblastoma but does not successfully stratify patients according to outcomes, especially in the upfront setting. Here we investigate whether restriction spectrum imaging, an advanced diffusion imaging model, performed after an operation but before radiation therapy, could improve risk stratification in patients with newly diagnosed glioblastoma relative to ADC.Pre-radiation therapy diffusion-weighted and structural imaging of 40 patients with glioblastoma were examined retrospectively. Restriction spectrum imaging and ADC-based hypercellularity volume fraction (restriction spectrum imaging-FLAIR volume fraction, restriction spectrum imaging-contrast-enhanced volume fraction, ADC-FLAIR volume fraction, ADC-contrast-enhanced volume fraction) and intensities (restriction spectrum imaging-FLAIR 90th percentile, restriction spectrum imaging-contrast-enhanced 90th percentile, ADC-FLAIR 10th percentile, ADC-contrast-enhanced 10th percentile) within the contrast-enhanced and FLAIR hyperintensity VOIs were calculated. The association of diffusion imaging metrics, contrast-enhanced volume, and FLAIR hyperintensity volume with progression-free survival and overall survival was evaluated by using Cox proportional hazards models.Among the diffusion metrics, restriction spectrum imaging-FLAIR volume fraction was the strongest prognostic metric of progression-free survival (P = .036) and overall survival (P = .007) in a multivariate Cox proportional hazards analysis, with higher values indicating earlier progression and shorter survival. Restriction spectrum imaging-FLAIR 90th percentile was also associated with overall survival (P = .043), with higher intensities, indicating shorter survival. None of the ADC metrics were associated with progression-free survival/overall survival. Contrast-enhanced volume exhibited a trend toward significance for overall survival (P = .063).Restriction spectrum imaging-derived cellularity in FLAIR hyperintensity regions may be a more robust prognostic marker than ADC and conventional imaging for early progression and poorer survival in patients with glioblastoma. However, future studies with larger samples are needed to explore its predictive ability.

SUBMITTER: Krishnan AP

PROVIDER: S-EPMC5507368 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Restriction Spectrum Imaging Improves Risk Stratification in Patients with Glioblastoma.

Krishnan A P AP Karunamuni R R Leyden K M KM Seibert T M TM Delfanti R L RL Kuperman J M JM Bartsch H H Elbe P P Srikant A A Dale A M AM Kesari S S Piccioni D E DE Hattangadi-Gluth J A JA Farid N N McDonald C R CR White N S NS

AJNR. American journal of neuroradiology 20170309 5

<h4>Background and purpose</h4>ADC as a marker of tumor cellularity has been promising for evaluating the response to therapy in patients with glioblastoma but does not successfully stratify patients according to outcomes, especially in the upfront setting. Here we investigate whether restriction spectrum imaging, an advanced diffusion imaging model, performed after an operation but before radiation therapy, could improve risk stratification in patients with newly diagnosed glioblastoma relative ...[more]

PMID: 28279985

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Project description:BackgroundAnti-angiogenic therapy with bevacizumab is the most widely used treatment option for recurrent glioblastoma, but therapeutic response varies substantially and effective biomarkers for patient selection are not available. To this end, we determine whether novel quantitative radiomic strategies on the basis of MRI have the potential to noninvasively stratify survival and progression in this patient population.MethodsIn an initial cohort of 126 patients, we identified a distinct set of features representative of the radiographic phenotype on baseline (pretreatment) MRI. These selected features were evaluated on a second cohort of 165 patients from the multicenter BRAIN trial with prospectively acquired clinical and imaging data. Features were evaluated in terms of prognostic value for overall survival (OS), progression-free survival (PFS), and progression within 3, 6, and 9 months using baseline imaging and first follow-up imaging at 6 weeks posttreatment initiation.ResultsMultivariable analysis of features derived at baseline imaging resulted in significant stratification of OS (hazard ratio [HR] = 2.5; log-rank P = 0.001) and PFS (HR = 4.5; log-rank P = 2.1 × 10-5) in validation data. These stratifications were stronger compared with clinical or volumetric covariates (permutation test false discovery rate [FDR] <0.05). Univariable analysis of a prognostic textural heterogeneity feature (information correlation) derived from postcontrast T1-weighted imaging revealed significantly higher scores for patients who progressed within 3 months (Wilcoxon test P = 8.8 × 10-8). Generally, features derived from postcontrast T1-weighted imaging yielded higher prognostic power compared with precontrast enhancing T2-weighted imaging.ConclusionRadiomics provides prognostic value for survival and progression in patients with recurrent glioblastoma receiving bevacizumab treatment. These results could lead to the development of quantitative pretreatment biomarkers to predict benefit from bevacizumab using standard of care imaging.

Project description:Background:The purpose of this study was to analyze the potential of radiomics for disease stratification beyond key molecular, clinical, and standard imaging features in patients with glioblastoma. Methods:Quantitative imaging features (n = 1043) were extracted from the multiparametric MRI of 181 patients with newly diagnosed glioblastoma prior to standard-of-care treatment (allocated to a discovery and a validation set, 2:1 ratio). A subset of 386/1043 features were identified as reproducible (in an independent MRI test-retest cohort) and selected for analysis. A penalized Cox model with 10-fold cross-validation (Coxnet) was fitted on the discovery set to construct a radiomic signature for predicting progression-free and overall survival (PFS and OS). The incremental value of a radiomic signature beyond molecular (O6-methylguanine-DNA methyltransferase [MGMT] promoter methylation, DNA methylation subgroups), clinical (patient's age, KPS, extent of resection, adjuvant treatment), and standard imaging parameters (tumor volumes) for stratifying PFS and OS was assessed with multivariate Cox models (performance quantified with prediction error curves). Results:The radiomic signature (constructed from 8/386 features identified through Coxnet) increased the prediction accuracy for PFS and OS (in both discovery and validation sets) beyond the assessed molecular, clinical, and standard imaging parameters (P ? 0.01). Prediction errors decreased by 36% for PFS and 37% for OS when adding the radiomic signature (compared with 29% and 27%, respectively, with molecular + clinical features alone). The radiomic signature was-along with MGMT status-the only parameter with independent significance on multivariate analysis (P ? 0.01). Conclusions:Our study stresses the role of integrating radiomics into a multilayer decision framework with key molecular and clinical features to improve disease stratification and to potentially advance personalized treatment of patients with glioblastoma.

Project description:Diffusion tensor imaging (DTI) has become a popular tool for delineating the location and extent of white matter injury in temporal lobe epilepsy (TLE). However, DTI yields nonspecific measures that are confounded by changes occurring within both the intracellular and extracellular environments. This study investigated whether an advanced diffusion method, restriction spectrum imaging (RSI) could provide a more robust measure of white matter injury in TLE relative to DTI due to RSI's ability to separate intraaxonal diffusion (i.e., neurite density; ND) from diffusion associated with extraaxonal factors (e.g., inflammation; crossing fibers).RSI and DTI scans were obtained on 21 patients with TLE and 11 age-matched controls. RSI-derived maps of ND, isotropic-hindered (IH) and isotropic-free (IF) water, and crossing fibers (CFs) were compared to DTI-derived fractional anisotropy (FA) maps. Voxelwise and tract-based analyses were performed comparing patients with TLE to controls on each diffusion metric.Reductions in FA were seen primarily in frontotemporal white matter in TLE, and they were most pronounced proximal to the seizure focus. Reductions in ND corresponded to those seen in the FA maps; however, ND reductions were greater in magnitude, more lateralized to the epileptogenic hemisphere, and showed a broader pattern. Increases in IF/IH and effects from CFs also contributed to reduced FA in the ipsilateral parahippocampal cingulum and fornix, with decreases in IH extending into extratemporal regions. Reduced ND of the uncinate fasciculus was associated with longer disease duration, whereas FA was not associated with any clinical variables.RSI may provide a more specific measure of white matter pathology in TLE, distinguishing regions primarily affected by axonal/myelin loss from those where CFs and increases in extracellular water also play a role. By providing a more specific measure of axonal/myelin loss, RSI-derived ND may better reflect overall white matter burden in epilepsy.

Dataset Information

Restriction Spectrum Imaging Improves Risk Stratification in Patients with Glioblastoma.

Publications

Restriction Spectrum Imaging Improves Risk Stratification in Patients with Glioblastoma.

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