Diffusion imaging has shown promise as a predictive and prognostic biomarker in glioma. We assessed the ability of graded functional diffusion maps (fDMs) and apparent diffusion coefficient (ADC) characteristics to predict overall survival (OS) in recurrent glioblastoma multiforme (GBM) patients treated with bevacizumab. Seventy-seven patients with recurrent GBMs were retrospectively examined. MRI scans were obtained before and approximately 6 weeks after treatment with bevacizumab. Graded fDMs were created by registering datasets to each patient's pretreatment scan and then performing voxel-wise subtraction between post- and pretreatment ADC maps. Voxels were categorized according to the degree of change in ADC within pretreatment fluid-attenuated inversion recovery (FLAIR) and contrast-enhancing regions of interest (ROIs). We found that the volume of tissue showing decreased ADC within both FLAIR and contrast-enhancing regions stratified OS (log-rank, P < .05). fDMs applied to contrast-enhancing ROIs more accurately predicted OS compared with fDMs applied to FLAIR ROIs. Graded fDMs (showing voxels with decreased ADC between 0.25 and 0.4 µm2/ms) were more predictive of OS than traditional (single threshold) fDMs, and the predictive ability of graded fDMs could be enhanced even further by adding the ADC characteristics from the fDM-classified voxels to the analysis (log-rank, P < .001). These results demonstrate that spatially resolved diffusion-based tumor metrics are a powerful imaging biomarker of survival in patients with recurrent GBM treated with bevacizumab.

Recently, we demonstrated that vessel geometry is a significant determinant of susceptibility-induced contrast in MRI. This is especially relevant for susceptibility-contrast enhanced MRI of tumors with their characteristically abnormal vessel morphology. In order to better understand the biophysics of this contrast mechanism, it is of interest to model how various factors, including microvessel morphology contribute to the measured MR signal, and was the primary motivation for developing a novel computer modeling approach called the Finite Perturber Method (FPM). The FPM circumvents the limitations of traditional fixed-geometry approaches, and enables us to study susceptibility-induced contrast arising from arbitrary microvascular morphologies in 3D, such as those typically observed with brain tumor angiogenesis. Here we describe this new modeling methodology and some of its applications. The excellent agreement of the FPM with theory and the extant susceptibility modeling data, coupled with its computational efficiency demonstrates its potential to transform our understanding of the factors that engender susceptibility contrast in MRI.

Purpose

To present comprehensive examinations of the assumptions made in functional diffusion map (fDM) analyses and provide a biological basis for fDM classification.

Materials and Methods

Sixty-nine patients with gliomas were enrolled in this study. To determine the sensitivity of ADC to cellularity, cell density from stereotactic biopsy specimens was correlated with pre-operative ADC maps. For definition of ADC thresholds used for fDMs, the 95% confidence intervals (C.I.) for changes in voxel-wise ADC measurements in normal appearing tissue was analyzed. The sensitivity and specificity to progressing disease was examined using both radiographic and neurological criteria.

Results

Results support the hypothesis that ADC is inversely proportional to cell density with a sensitivity of 1.01 × 10-7 [mm2/s]/[nuclei/mm2]. The 95% C.I. for white matter = 0.25×10-3mm2/s, gray matter = 0.31×10-3mm2/s, a mixture of white and gray matter = 0.40×10-3mm2/s, and a mixture of white matter, gray matter, and cerebrospinal fluid = 0.75×10-3mm2/s. Application of these measurements as ADC thresholds produce varying levels of sensitivity and specificity to disease progression, which were all significantly better than chance.

Conclusion

This study suggests fDMs are valid biomarkers for brain tumor cellularity.

Background

This study demonstrates that a dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI) perfusion parameter may indicate vascular abnormality in a brain tumor model and reflects an effect of dexamethasone treatment. In addition, X-ray computed tomography (CT) measurements of vascular tortuosity and tissue markers of vascular morphology were performed to investigate the underpinnings of tumor response to dexamethasone.

Methodology/Principal Findings

One cohort of Fisher 344 rats (N = 13), inoculated intracerebrally with 9L gliosarcoma cells, was treated with dexamethasone (i.p. 3 mg/kg/day) for five consecutive days, and another cohort (N = 11) was treated with equal volume of saline. Longitudinal DSC-MRI studies were performed at the first (baseline), third and fifth day of treatments. Relative cerebral blood volume (rCBV) was significantly reduced on the third day of dexamethasone treatment (0.65±.13) as compared to the fifth day during treatment (1.26±.19, p<0.05). In saline treated rats, relative CBV gradually increased during treatment (0.89±.13, 1.00±.21, 1.13±.23) with no significant difference on the third day of treatment (p>0.05). In separate serial studies, microfocal X-ray CT of ex vivo brain specimens (N = 9) and immunohistochemistry for endothelial cell marker anti-CD31 (N = 8) were performed. Vascular morphology of ex vivo rat brains from micro-CT analysis showed hypervascular characteristics in tumors, and both vessel density (41.32±2.34 branches/mm3, p<0.001) and vessel tortuosity (p<0.05) were significantly reduced in tumors of rats treated with dexamethasone compared to saline (74.29±3.51 branches/mm3). The vascular architecture of rat brain tissue was examined with anti-CD31 antibody, and dexamethasone treated tumor regions showed reduced vessel area (16.45±1.36 µm2) as compared to saline treated tumor regions (30.83±4.31 µm2, p<0.001) and non-tumor regions (22.80±1.11 µm2, p<0.01).

Conclusions/Significance

Increased vascular density and tortuosity are culprit to abnormal perfusion, which is transiently reduced during dexamethasone treatment.

Depending on dose, dexamethasone has been shown to inhibit or stimulate growth of rat 9L gliosarcoma and decrease the expression of vascular endothelial growth factor (VEGF), an important mediator of tumor-associated angiogenesis. We demonstrate, by constructing relative cerebral blood volume (rCBV) maps with MRI, that dexamethasone also decreases total blood volume while increasing microvascular blood volume in Fischer rats bearing intracranial 9L gliosarcoma. Animals were inoculated with 1 x 10(5) 9L gliosarcoma tumor cells. On days 10-14 after tumor cell inoculation, animals were intra-peritoneally injected with dexamethasone (3 mg/kg) over 5 days. MRI-derived gradient echo (GE) and spin-echo (SE) rCBV maps were created to demonstrate total vasculature (GE) and microvasculature (SE). After MRI studies were performed, the rat's vasculature was perfused with a latex compound. Total vessel volume and diameters were assessed by microscopy. Dexamethasone decreased the tumor-enhancing area of postcontrast T1-weighted images (P < 0.0001) and total tumor volume(P = 0.0085). In addition, there was a greater than 50% decrease in GE rCBV (total vasculature) (P = 0.007) as well as a significant decrease in total fractional blood volume, as validated by histology (P = 0.0007). Conversely, there was an increase in SE rCBV signal (microvasculature) in animals treated with dexamethasone (P = 0.05), which was consistent with microscopy (P < 0.0001). These data demonstrate that (1) dexamethasone selectively treats tumor vasculature, suggesting a vessel-size selective effect and (2) MRI-derived rCBV is a noninvasive technique that can be used to evaluate changes in blood volume and vascular morphology.

The purpose of the current study was to develop a voxel-wise analytical solution to a glioma growth model using serial diffusion MRI. These cell invasion, motility, and proliferation level estimates (CIMPLE maps) provide quantitative estimates of microscopic tumor growth dynamics. After an analytical solution was found, noise simulations were performed to predict the effects that perturbations in apparent diffusion coefficient (ADC) values and the time between ADC map acquisitions would have on the accuracy of CIMPLE maps. CIMPLE maps were then created for 53 patients with gliomas with WHO grades of II–IV. MR spectroscopy estimates of the Cho/NAA ratio were compared to cell proliferation estimates in CIMPLE maps using Pearson’s correlation analysis. Median differences in cell proliferation and diffusion rates between WHO grades were compared. A strong correlation (R2 = 0.9714) and good spatial correspondence were observed between MR spectroscopy measurements of the Cho/NAA ratio and CIMPLE map cell proliferation rate estimates. Estimates of cell proliferation and diffusion rates appear to be significantly different between low (WHO II) and high-grade (WHO III–IV) gliomas. Cell diffusion rate (motility) estimates are highly dependent on the time interval between ADC map acquisitions, whereas cell proliferation rate estimates are additionally influenced by the level of noise present in ADC maps.

Anti-angiogenic agents targeting brain tumor neovasculature may increase progression-free survival in patients with recurrent malignant gliomas. However, when these patients do recur it is not always apparent as an increase in enhancing tumor volume on MRI, which has been the standard of practice for following patients with brain tumors. Therefore alternative methods are needed to evaluate patients treated with these novel therapies. Furthermore, a method that can also provide useful information for the evaluation of conventional therapies would provide an important advantage for general applicability. Diffusion-weighted magnetic resonance imaging (DWI) has the potential to serve as a valuable biomarker for these purposes. In the current study, we explore the prognostic ability of functional diffusion maps (fDMs), which examine voxel-wise changes in the apparent diffusion coefficient (ADC) over time, applied to regions of fluid-attenuated inversion recovery (FLAIR) abnormalities in patients with malignant glioma, treated with either anti-angiogenic or cytotoxic therapies. Results indicate that the rate of change in fDMs is an early predictor of tumor progression, time to progression and overall survival for both treatments, suggesting the application of fDMs in FLAIR abnormal regions may be a significant advance in brain tumor biomarker technology.

Diffusion-weighted magnetic resonance imaging (DWI) is a sensitive imaging biomarker for tumor cellularity. Functional diffusion maps (fDMs), which examine voxel-by-voxel changes in the apparent diffusion coefficient (ADC) calculated from serial DWIs, have previously been applied to regions of contrast-enhancement; however, application of fDMs to non-enhancing brain tumors has not been pursued. In this case study we demonstrate the utility of applying fDMs to regions of abnormal FLAIR signal intensity in a patient diagnosed with gliomatosis cerebri: a relatively rare, infiltrative, non-enhancing brain tumor. The absolute volume of hyper-cellularity extracted from fDMs was useful in tracking tumor growth, which correlated in time with a progressive decline in neurological status despite no change in traditional magnetic resonance images. Results of this study demonstrate the value of fDMs, applied to regions of FLAIR abnormal signal intensity, for localizing regions of hypercellularity and for monitoring overall tumor status.