Less Invasive Phenotype Found in Isocitrate Dehydrogenase-mutated Glioblastomas than in Isocitrate Dehydrogenase Wild-Type Glioblastomas: A Diffusion-Tensor Imaging Study

PURPOSE: To explore the diffusion-tensor (DT) imaging-defined invasive phenotypes of both isocitrate dehydrogenase (IDH-1)-mutated and IDH-1 wild-type glioblastomas. MATERIALS AND METHODS: Seventy patients with glioblastoma were prospectively recruited and imaged preoperatively. All patients provided signed consent, and the local research ethics committee approved the study. Patients underwent surgical resection, and tumor samples underwent immunohistochemistry for IDH-1 R132H mutations. DT imaging data were coregistered to the anatomic magnetic resonance study and reconstructed to provide the anisotropic and isotropic components of the DT. The invasive phenotype was determined by using previously published criteria and correlated with IDH-1 mutation status by using the Freeman-Halton extension of the Fisher exact probability test. RESULTS: Nine patients had an IDH-1 mutation and 61 had IDH-1 wild type. All of the patients with IDH-1 mutation had a minimally invasive DT imaging phenotype. Among the IDH-1 wild-type tumors, 42 of 61 (69%) were diffusively invasive glioblastomas, 14 of 61 (23%) were locally invasive, and five of 61 (8%) were minimally invasive (P < .001). CONCLUSION: IDH-mutated glioblastomas have a less invasive phenotype compared with IDH wild type. This finding may have implications for individualizing the extent of surgical resection and radiation therapy volumes.NIHR Clinician Scientist Fellowship (NIHR/CS/009/011); Chang Gung Medical Foundation; Chang Gung Memorial Hospital; Commonwealth Scholarship Commission; Cambridge Commonwealth Overseas Trust; NIHR Cambridge Biomedical Research Centr

Multiparametric MR Imaging of Diffusion and Perfusion in Contrast-enhancing and Nonenhancing Components in Patients with Glioblastoma

Purpose To determine whether regions of low apparent diffusion coefficient (ADC) with high relative cerebral blood volume (rCBV) represented elevated choline (Cho)-to-N-acetylaspartate (NAA) ratio (hereafter, Cho/NAA ratio) and whether their volumes correlated with progression-free survival (PFS) and overall survival (OS) in patients with glioblastoma (GBM). Materials and Methods This retrospective analysis was approved by the local research ethics committee. Volumetric analysis of imaging data from 43 patients with histologically confirmed GBM was performed. Patients underwent preoperative 3-T magnetic resonance imaging with conventional, diffusion-weighted, perfusion-weighted, and spectroscopic sequences. Patients underwent subsequent surgery with adjuvant chemotherapy and radiation therapy. Overlapping low-ADC and high-rCBV regions of interest (ROIs) (hereafter, ADC-rCBV ROIs) were generated in contrast-enhancing and nonenhancing regions. Cho/NAA ratio in ADC-rCBV ROIs was compared with that in control regions by using analysis of variance. All resulting ROI volumes were correlated with patient survival by using multivariate Cox regression. Results ADC-rCBV ROIs within contrast-enhancing and nonenhancing regions showed elevated Cho/NAA ratios, which were significantly higher than those in other abnormal tumor regions (P < .001 and P = .008 for contrast-enhancing and nonenhancing regions, respectively) and in normal-appearing white matter (P < .001 for both contrast-enhancing and nonenhancing regions). After Cox regression analysis controlling for age, tumor size, resection extent, O-6-methylguanine-DNA methyltransferase-methylation, and isocitrate dehydrogenase mutation status, the proportional volume of ADC-rCBV ROIs in nonenhancing regions significantly contributed to multivariate models of OS (hazard ratio, 1.132; P = .026) and PFS (hazard ratio, 1.454; P = .017). Conclusion Volumetric analysis of ADC-rCBV ROIs in nonenhancing regions of GBM can be used to identify patients with poor survival trends after accounting for known confounders of GBM patient outcome.Supported by a Clinician Scientist Award from the National Institute for Health Research (NIHR/CS/009/011) and by the NIHR Cambridge Biomedical Research Center and Commonwealth Scholarship Commission

Decoding the Interdependence of Multiparametric Magnetic Resonance Imaging to Reveal Patient Subgroups Correlated with Survivals

Glioblastoma is highly heterogeneous in microstructure and vasculature, creating various tumor microenvironments among patients, which may lead to different phenotypes. The purpose was to interrogate the interdependence of microstructure and vasculature using perfusion and diffusion imaging and to investigate the utility of this approach in tumor invasiveness assessment. A total of 115 primary glioblastoma patients were prospectively recruited for preoperative magnetic resonance imaging (MRI) and surgery. Apparent diffusion coefficient (ADC) was calculated from diffusion imaging, and relative cerebral blood volume (rCBV) was calculated from perfusion imaging. The empirical copula transform was applied to ADC and rCBV voxels in the contrast-enhancing tumor region to obtain their joint distribution, which was discretized to extract second-order features for an unsupervised hierarchical clustering. The lactate levels of patient subgroups, measured by MR spectroscopy, were compared. Survivals were analyzed using Kaplan-Meier and multivariate Cox regression analyses. The results showed that three patient subgroups were identified by the unsupervised clustering. These subtypes showed no significant differences in clinical characteristics but were significantly different in lactate level and patient survivals. Specifically, the subtype demonstrating high interdependence of ADC and rCBV displayed a higher lactate level than the other two subtypes (P = .016 and P = .044, respectively). Both subtypes of low and high interdependence showed worse progression-free survival than the intermediate (P = .046 and P = .009 respectively). Our results suggest that the interdependence between perfusion and diffusion imaging may be useful in stratifying patients and evaluating tumor invasiveness, providing overall measure of tumor microenvironment using multiparametric MRI

Multimodal MRI characteristics of the glioblastoma infiltration beyond contrast enhancement

Our inability to identify the invasive margin of glioblastomas hampers attempts to achieve local control. Diffusion tensor imaging (DTI) has been implemented clinically to delineate the margin of the tumor infiltration, its derived anisotropic (q) values can extend beyond the contrast-enhanced area and correlates closely with the tumor. However, its correlation with tumor infiltration shown on multivoxel proton magnetic resonance spectroscopy1 (MRS) and perfusion magnetic resonance imaging (MRI) should be investigated. In this study, we aimed to show tissue characteristics of the q-defined peritumoral invasion on MRS and perfusion MRI. Patients with a primary glioblastoma were included (n = 51). Four regions of interest were analyzed; the contrast-enhanced lesion, peritumoral abnormal q region, peritumoral normal q region, and contralateral normal-appearing white matter. MRS, including choline (Cho)/creatinine (Cr), Cho/N-acetyl-aspartate (NAA) and NAA/Cr ratios, and the relative cerebral blood volume (rCBV) were analyzed. Our results showed an increase in the Cho/NAA (p = 0.0346) and Cho/Cr (p = 0.0219) ratios in the peritumoral abnormal q region, suggestive of tumor invasion. The rCBV was marginally elevated (p = 0.0798). Furthermore, the size of the abnormal q regions was correlated with survival; patients with larger abnormal q regions showed better progression-free survival (median 287 versus 53 days, p = 0.001) and overall survival (median 464 versus 274 days, p = 0.006) than those with smaller peritumoral abnormal q regions of interest. These results support how the DTI q abnormal area identifies tumor activity beyond the contrast-enhanced area, especially correlating with MRS

Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma

OBJECTIVES: Integrating 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. METHODS: Eighty 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. RESULTS: Two 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). CONCLUSIONS: The multi-view clustering method can provide an effective integration of multi-parametric MRI. The histogram features selected may be used as potential prognostic markers

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

PURPOSE: To use perfusion and magnetic resonance (MR) spectroscopy to compare the diffusion tensor imaging (DTI)-defined invasive and noninvasive regions. Invasion of normal brain is a cardinal feature of glioblastomas (GBM) and a major cause of treatment failure. DTI can identify invasive regions. MATERIALS AND METHODS: In all, 50 GBM patients were imaged preoperatively at 3T with anatomic sequences, DTI, dynamic susceptibility perfusion MR (DSCI), and multivoxel spectroscopy. The DTI and DSCI data were coregistered to the spectroscopy data and regions of interest (ROIs) were made in the invasive (determined by DTI), noninvasive regions, and normal brain. Values of relative cerebral blood volume (rCBV), N-acetyl aspartate (NAA), myoinositol (mI), total choline (Cho), and glutamate + glutamine (Glx) normalized to creatine (Cr) and Cho/NAA were measured at each ROI. RESULTS: Invasive regions showed significant increases in rCBV, suggesting angiogenesis (invasive rCBV 1.64 [95% confidence interval, CI: 1.5-1.76] vs. noninvasive 1.14 [1.09-1.18]; P < 0.001), Cho/Cr (invasive 0.42 [0.38-0.46] vs. noninvasive 0.35 [0.31-0.38]; P = 0.02) and Cho/NAA (invasive 0.54 [0.41-0.68] vs. noninvasive 0.37 [0.29-0.45]; P = < 0.03), suggesting proliferation, and Glx/Cr (invasive 1.54 [1.27-1.82] vs. noninvasive 1.3 [1.13-1.47]; P = 0.028), suggesting glutamate release; and a significantly reduced NAA/Cr (invasive 0.95 [0.85-1.05] vs. noninvasive 1.19 [1.06-1.31]; P = 0.008). The mI/Cr was not different between the three ROIs (invasive 1.2 [0.99-1.41] vs. noninvasive 1.3 [1.14-1.46]; P = 0.68). In the noninvasive regions, the values were not different from normal brain. CONCLUSION: Combining DTI to identify the invasive region with perfusion and spectroscopy, we can identify changes in invasive regions not seen in noninvasive regions

Characterizing tumor invasiveness of glioblastoma using multiparametric magnetic resonance imaging

OBJECTIVE: The objective of this study was to characterize the abnormalities revealed by diffusion tensor imaging (DTI) using MR spectroscopy (MRS) and perfusion imaging, and to evaluate the prognostic value of a proposed quantitative measure of tumor invasiveness by combining contrast-enhancing (CE) and DTI abnormalities in patients with glioblastoma. METHODS: Eighty-four patients with glioblastoma were recruited preoperatively. DTI was decomposed into isotropic (p) and anisotropic (q) components. The relative cerebral blood volume (rCBV) was calculated from the dynamic susceptibility contrast imaging. Values of N-acetylaspartate, myoinositol, choline (Cho), lactate (Lac), and glutamate + glutamine (Glx) were measured from multivoxel MRS and normalized as ratios to creatine (Cr). Tumor regions of interest (ROIs) were manually segmented from the CE T1-weighted (CE-ROI) and DTI-q (q-ROI) maps. Perfusion and metabolic characteristics of these ROIs were measured and compared. The relative invasiveness coefficient (RIC) was calculated as a ratio of the characteristic radii of CE-ROI and q-ROI. The prognostic significance of RIC was tested using Kaplan-Meier and multivariate Cox regression analyses. RESULTS: The Cho/Cr, Lac/Cr, and Glx/Cr in q-ROI were significantly higher than CE-ROI (p = 0.004, p = 0.005, and p = 0.007, respectively). CE-ROI had significantly higher rCBV values than q-ROI (p < 0.001). A higher RIC was associated with worse survival in a multivariate overall survival (OS) model (hazard ratio [HR] 1.40, 95% confidence interval [CI] 1.06–1.85, p = 0.016) and progression-free survival (PFS) model (HR 1.55, 95% CI 1.16–2.07, p = 0.003). An RIC cutoff value of 0.89 significantly predicted shorter OS (median 384 vs 605 days, p = 0.002) and PFS (median 244 vs 406 days, p = 0.001). CONCLUSIONS: DTI-q abnormalities displayed higher tumor load and hypoxic signatures compared with CE abnormalities, whereas CE regions potentially represented the tumor proliferation edge. Integrating the extents of invasion visualized by DTI-q and CE images into clinical practice may lead to improved treatment efficacy

Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma

Objectives Integrating 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. Methods Eighty 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. Results Two 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). Conclusions The multi-view clustering method can provide an effective integration of multi-parametric MRI. The histogram features selected may be used as potential prognostic markers

Quantitative MRI demonstrates abnormalities of the third ventricle subventricular zone in neurofibromatosis type-1 and sporadic paediatric optic pathway glioma

BACKGROUND: The subventricular zone of the third ventricle (TVZ) is a germinal stem cell niche, identified as the possible location of optic pathway glioma (OPG) cell origin. Paediatric OPGs are predominantly diagnosed as low-grade astrocytomas, which are either sporadic or are associated with neurofibromatosis type-1 (NF1). These tumours often cause a significant impairment to visual acuity (VA). Infiltrative/invasive tumour activity is associated with increased apparent diffusion coefficient (ADC) and cerebral blood flow (CBF). This study aimed to determine whether TVZ imaging features differed between sporadic-OPG, NF1-OPG and controls, and whether the ADC and CBF profile at the germinal stem cell niche (the TVZ) correlated with the primary outcome of VA. METHODS: ADC and CBF MRI data were acquired from 30 paediatric OPG patients (median age 6 years; range 8 months-17 years), along with VA measurements, during clinical surveillance of their tumour. Values for mean ADC and maximum CBF were measured at the TVZ, and normalized to normal-appearing grey matter. These values were compared between the two OPG groups and the healthy control subjects, and multivariate linear regression was used to test the linear association between these values and patient's VA. RESULTS: In the TVZ, normalized mean ADC was higher in NF1-associated OPG patients (N = 15), compared to both sporadic OPG patients (N = 15; p = 0.010) and healthy controls (N = 14; p < 0.001). In the same region, normalized maximum CBF was higher in sporadic OPG patients compared to both NF1-OPG patients (p = 0.016) and healthy controls (p < 0.001). In sporadic OPG patients only, normalized mean ADC in the TVZ was significantly correlated with visual acuity (R2 = 0.41, p = 0.019). No significant correlations were found between TVZ CBF and ADC values and visual acuity in the NF1-associated OPG patients. CONCLUSION: Quantitative MRI detects TVZ abnormalities in both sporadic and NF1-OPG patients, and identifies TVZ features that differentiate the two. TVZ features may be useful MRI markers of interest in future predictive studies involving sporadic OPG

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

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