Diffusion tensor MRI in rat models of invasive and well-demarcated brain tumors

Diffusion kurtosis imaging for characterizing tumor heterogeneity in an intracranial rat glioblastoma model

The utility of diffusion kurtosis imaging (DKI) for assessing intra-tumor heterogeneity was evaluated in a rat model of glioblastoma multiforme. Longitudinal MRI including T₂ -weighted and diffusion-weighted MRI (DWI) was performed on six female Fischer rats 8, 11 and 14 days after intracranial transplantation of F98 cells. T₂ -weighted images were used to measure the tumor volumes and DWI images were used to compute diffusion tensor imaging (DTI) and DWI based parametric maps including mean diffusivity (MD), mean kurtosis (MK), axial diffusivity (AD), axial kurtosis, radial diffusivity, radial kurtosis, fractional anisotropy (FA) and kurtosis fractional anisotropy (KFA). Median values from the segmented normal contralateral cortex, tumor and edema from the diffusion parameters were compared at the three imaging time points to assess any changes in tumor heterogeneity over time. ex vivo DKI was also performed in a representative sample and compared with histology. Significant differences were observed between normal cortex, tumor and edema in both the DTI and DKI parameters. Notably, at the earliest time point MK and KFA were significantly different between normal cortex and tumor in comparison with MD or FA. Although a decreasing trend in MD, AD and FA values of the tumor were observed as the tumor grew, no significant changes in any of the DTI or DKI parameters were observed longitudinally. While DKI was equally sensitive to DTI in differentiating tumor from edema and normal brain, it was unable to detect longitudinal increases in intra-tumoral heterogeneity in the F98 model of glioblastoma multiforme.

1H magnetic resonance spectroscopy of 2H-to-1H exchange quantifies the dynamics of cellular metabolism in vivo

The quantitative mapping of the in vivo dynamics of cellular metabolism via non-invasive imaging contributes to the understanding of the initiation and progression of diseases associated with dysregulated metabolic processes. Current methods for imaging cellular metabolism are limited by low sensitivities, by costs, or by the use of specialized hardware. Here, we introduce a method that captures the turnover of cellular metabolites by quantifying signal reductions in proton magnetic resonance spectroscopy (MRS) resulting from the replacement of ¹H with ²H. The method, which we termed quantitative exchanged-label turnover MRS, only requires deuterium-labelled glucose and standard MRI scanners, and with a single acquisition provides steady-state information and metabolic rates for several metabolites. We used the method to monitor glutamate, glutamine, γ-aminobutyric acid and lactate in the brains of normal and glioma-bearing rats following the administration of ²H₂-labelled glucose and ²H₃-labelled acetate. Quantitative exchanged-label turnover MRS should broaden the applications of routine ¹H MRS.

Experimental sepsis-associated encephalopathy is accompanied by altered cerebral blood perfusion and water diffusion and related to changes in cyclooxygenase-2 expression and glial cell morphology but not to blood-brain barrier breakdown

Sepsis-associated encephalopathy (SAE) refers to brain dysfunction, including delirium, occurs during severe infection and is associated with development of post-traumatic stress disorder. SAE has been proposed to be related to reduced cerebral blood flow (CBF), blood-brain barrier breakdown (BBB), white matter edema and disruption and glia cell activation, but their exact relationships remain to be determined. In the present work, we set out to study CBF using Arterial Spin Labeling (ASL) and grey and white matter structure with T2- and diffusion magnetic resonance imaging (dMRI) in rats with cecal ligation and puncture (CLP)-induced encephalopathy. Using immunohistochemistry, the distribution of the vasoactive prostaglandin-synthesizing enzyme cyclooxygenase-2 (COX-2), perivascular immunoglobulins G (IgG), aquaporin-4 (AQP4) and the morphology of glial cell were subsequently assessed in brains of the same animals. CLP induced deficits in the righting reflex and resulted in higher T2-weighted contrast intensities in the cortex, striatum and at the base of the brain, decreased blood perfusion distribution to the cortex and increased water diffusion parallel to the fibers of the corpus callosum compared to sham surgery. In addition, CLP reduced staining for microglia- and astrocytic-specific proteins in the corpus callosum, decreased neuronal COX-2 and AQP4 expression in the cortex while inducing perivascular COX-2 expression, but did not induce widespread perivascular IgG diffusion. In conclusion, our findings indicate that experimental SAE can occur in the absence of BBB breakdown and is accompanied by increased water diffusion anisotropy and altered glia cell morphology in brain white matter.

Sugar alcohol provides imaging contrast in cancer detection

Clinical imaging is widely used to detect, characterize and stage cancers in addition to monitoring the therapeutic progress. Magnetic resonance imaging (MRI) aided by contrast agents utilizes the differential relaxivity property of water to distinguish between tumorous and normal tissue. Here, we describe an MRI contrast method for the detection of cancer using a sugar alcohol, maltitol, a common low caloric sugar substitute that exploits the chemical exchange saturation transfer (CEST) property of the labile hydroxyl group protons on maltitol (malCEST). In vitro studies pointed toward concentration and pH-dependent CEST effect peaking at 1 ppm downfield to the water resonance. Studies with control rats showed that intravenously injected maltitol does not cross the intact blood-brain barrier (BBB). In glioma carrying rats, administration of maltitol resulted in the elevation of CEST contrast in the tumor region only owing to permeable BBB. These preliminary results show that this method may lead to the development of maltitol and other sugar alcohol derivatives as MRI contrast agents for a variety of preclinical imaging applications.

Texture analysis on diffusion tensor imaging: discriminating glioblastoma from single brain metastasis

BACKGROUND

Texture analysis has been done on several radiological modalities to stage, differentiate, and predict prognosis in many oncologic tumors.

PURPOSE

To determine the diagnostic accuracy of discriminating glioblastoma (GBM) from single brain metastasis (MET) by assessing the heterogeneity of both the solid tumor and the peritumoral edema with magnetic resonance imaging (MRI) texture analysis (MRTA).

MATERIAL AND METHODS

Preoperative MRI examinations done on a 3-T scanner of 43 patients were included: 22 GBM and 21 MET. MRTA was performed on diffusion tensor imaging (DTI) in a representative region of interest (ROI). The MRTA was assessed using a commercially available research software program (TexRAD) which applies a filtration histogram technique for characterizing tumor and peritumoral heterogeneity. The filtration step selectively filters and extracts texture features at different anatomical scales varying from 2 mm (fine) to 6 mm (coarse). Heterogeneity quantification was obtained by the statistical parameter entropy. A threshold value to differentiate GBM from MET with sensitivity and specificity was calculated by receiver operating characteristic (ROC) analysis.

RESULTS

Quantifying the heterogeneity of the solid part of the tumor showed no significant difference between GBM and MET. However, the heterogeneity of the GBMs peritumoral edema was significantly higher than the edema surrounding MET, differentiating them with a sensitivity of 80% and specificity of 90%.

CONCLUSION

Assessing the peritumoral heterogeneity can increase the radiological diagnostic accuracy when discriminating GBM and MET. This will facilitate the medical staging and optimize the planning for surgical resection of the tumor and postoperative management.

Characterization of active and infiltrative tumorous subregions from normal tissue in brain gliomas using multiparametric MRI

BACKGROUND

Targeted localized biopsies and treatments for diffuse gliomas rely on accurate identification of tissue subregions, for which current MRI techniques lack specificity.

PURPOSE

To explore the complementary and competitive roles of a variety of conventional and quantitative MRI methods for distinguishing subregions of brain gliomas.

STUDY TYPE

Prospective.

POPULATION

Fifty-one tissue specimens were collected using image-guided localized biopsy surgery from 10 patients with newly diagnosed gliomas.

FIELD STRENGTH/SEQUENCE

Conventional and quantitative MR images consisting of pre- and postcontrast T1 w, T2 w, T2 -FLAIR, T2 -relaxometry, DWI, DTI, IVIM, and DSC-MRI were acquired preoperatively at 3T.

ASSESSMENT

Biopsy specimens were histopathologically attributed to glioma tissue subregion categories of active tumor (AT), infiltrative edema (IE), and normal tissue (NT) subregions. For each tissue sample, a feature vector comprising 15 MRI-based parameters was derived from preoperative images and assessed by a machine learning algorithm to determine the best multiparametric feature combination for characterizing the tissue subregions.

STATISTICAL TESTS

For discrimination of AT, IE, and NT subregions, a one-way analysis of variance (ANOVA) test and for pairwise tissue subregion differentiation, Tukey honest significant difference, and Games-Howell tests were applied (P < 0.05). Cross-validated feature selection and classification methods were implemented for identification of accurate multiparametric MRI parameter combination.

RESULTS

After exclusion of 17 tissue specimens, 34 samples (AT = 6, IE = 20, and NT = 8) were considered for analysis. Highest accuracies and statistically significant differences for discrimination of IE from NT and AT from NT were observed for diffusion-based parameters (AUCs >90%), and the perfusion-derived parameter as the most accurate feature in distinguishing IE from AT. A combination of "CBV, MD, T2 _ISO, FLAIR" parameters showed high diagnostic performance for identification of the three subregions (AUC ∼90%).

DATA CONCLUSION

Integration of a few quantitative along with conventional MRI parameters may provide a potential multiparametric imaging biomarker for predicting the histopathologically proven glioma tissue subregions.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;48:938-950.

A combined diffusion tensor imaging and Ki-67 labeling index study for evaluating the extent of tumor infiltration using the F98 rat glioma model

Diffusion tensor imaging (DTI) has been proven to be a sophisticated and useful tool for the delineation of tumors. In the present study, we investigated the predictive role of DTI compared to other magnetic resonance imaging (MRI) techniques in combination with Ki-67 labeling index in defining tumor cell infiltration in the peritumoral regions of F98 glioma-bearing rats. A total of 29 tumor-bearing Fischer rats underwent T2-weighted imaging, contrast-enhanced T1-weighted imaging, and DTI of their brain using a 7.0-T MRI scanner. The fractional anisotropy (FA) ratios were correlated to the Ki-67 labeling index using the Spearman correlation analysis. A receiver operating characteristic curve (ROC) analysis was established to evaluate parameters with sensitivity and specificity in order to identify the threshold values for predicting tumor infiltration. Significant correlations were observed between the FA ratios and Ki-67 labeling index (r = - 0.865, p < 0.001). The ROC analysis demonstrated that the apparent diffusion coefficient (ADC) and FA ratios could predict 50% of the proliferating cells in the regions of interest (ROI), with a sensitivity of 88.1 and 81.3%, and a specificity of 86.2 and 90.2%, respectively (p < 0.001). Meanwhile, the two ratios could also predict 10% of the proliferating cells in the ROI, with a sensitivity of 82.5 and 94.9%, and a specificity of 100 and 88.9%, respectively (p < 0.001). The present study demonstrated that the FA ratios are closely correlated with the Ki-67 labeling index. Furthermore, both ADC and FA ratios, derived from DTI, were useful for quantitatively predicting the Ki-67 labeling of glioma cells.

Analysis of DTI-Derived Tensor Metrics in Differential Diagnosis between Low-grade and High-grade Gliomas

Purpose: It is critical and difficult to accurately discriminate between high- and low-grade gliomas preoperatively. This study aimed to ascertain the role of several scalar measures in distinguishing high-grade from low-grade gliomas, especially the axial diffusivity (AD), radial diffusivity (RD), planar tensor (Cp), spherical tensor (Cs), and linear tensor (Cl) derived from diffusion tensor imaging (DTI). Materials and Methods: Fifty-three patients with pathologically confirmed brain gliomas (21 low-grade and 32 high-grade) were included. Contrast-enhanced T1-weighted images and DTI were performed in all patients. The AD, RD, Cp, Cs, and Cl values in the tumor zone, peritumoral edema zone, white matter (WM) adjacent to edema and contralateral normal-appearing white matter (NAWM) were calculated. The DTI parameters and tumor grades were statistically analyzed, and receiver operating characteristic (ROC) curve analysis was also performed. Results: The DTI metrics in the affected hemisphere showed significant differences from those in the NAWM, except for the AD values in the tumor zone and the RD values in WM adjacent to edema in the low-grade groups, as well as the Cp values in WM adjacent to edema in the high-grade groups. AD in the tumor zone as well as Cs and Cl in WM adjacent to edema revealed significant differences between the low- and high-grade gliomas. The areas under the curve (Az) of all three metrics were greater than 0.5 in distinguishing low-grade from high-grade gliomas by ROC curve analysis, and the best DTI metric was Cs in WM adjacent to edema (Az: 0.692). Conclusion: AD in the tumor zone as well as Cs and Cl in WM adjacent to edema will provide additional information to better classify gliomas and can be used as non-invasive reliable biomarkers in glioma grading.

Non-caloric sweetener provides magnetic resonance imaging contrast for cancer detection

BACKGROUND

Image contrast enhanced by exogenous contrast agents plays a crucial role in the early detection, characterization, and determination of the precise location of cancers. Here, we investigate the feasibility of using a non-nutritive sweetener, sucralose (commercial name, Splenda), as magnetic resonance imaging (MRI) contrast agent for cancer studies.

METHODS

High-resolution nuclear-magnetic-resonance spectroscopy and MR studies on sucralose solution phantom were performed to detect the chemical exchange saturation transfer (CEST) property of sucralose hydroxyl protons with bulk water (sucCEST). For the animal experiments, female Fisher rats (F344/NCR) were used to generate 9L-gliosarcoma model. MRI with CEST experiments were performed on anesthetized rats at 9.4 T MR scanner. Following the baseline CEST scans, sucralose solution was intravenously administered in control and tumor bearing rats. CEST acquisitions were continued during and following the administration of sucralose. Following the sucCEST, Gadolinium-diethylenetriamine pentaacetic acid was injected to perform Gd-enhanced imaging for visualizing the tumor.

RESULTS

The sucCEST contrast in vitro was found to correlate positively with the sucralose concentration and negatively with the pH, indicating the potential of this technique in cancer imaging. In a control animal, the CEST contrast from the brain was found to be unaffected following the administration of sucralose, demonstrating its blood-brain barrier impermeability. In a 9L glioma model, enhanced localized sucCEST contrast in the tumor region was detected while the unaffected brain region showed unaltered CEST effect implying the specificity of sucralose toward the tumorous tissue. The CEST asymmetry plots acquired from the tumor region before and after the sucralose infusion showed elevation of asymmetry at 1 ppm, pointing towards the role of sucralose in increased contrast.

CONCLUSIONS

We show the feasibility of using sucralose and sucCEST in study of preclinical models of cancer. This study paves the way for the potential development of sucralose and other sucrose derivatives as contrast agents for clinical MRI applications.

Creatine CEST MRI for Differentiating Gliomas with Different Degrees of Aggressiveness

PURPOSE

Creatine (Cr) is a major metabolite in the bioenergetic system. Measurement of Cr using conventional MR spectroscopy (MRS) suffers from low spatial resolution and relatively long acquisition times. Creatine chemical exchange saturation transfer (CrCEST) magnetic resonance imaging (MRI) is an emerging molecular imaging method for tissue Cr measurements. Our previous study showed that the CrCEST contrast, obtained through multicomponent Z-spectral fitting, was lower in tumors compared to normal brain, which further reduced with tumor progression. The current study was aimed to investigate if CrCEST MRI can also be useful for differentiating gliomas with different degrees of aggressiveness.

PROCEDURES

Intracranial 9L gliosarcoma and F98 glioma bearing rats with matched tumor size were scanned with a 9.4 T MRI scanner at two time points. CEST Z-spectra were collected using a customized sequence with a frequency-selective rectangular saturation pulse (B1 = 50 Hz, duration = 3 s) followed by a single-shot readout. Z spectral data were fitted pixel-wise with five Lorentzian functions, and maps of CrCEST peak amplitude, linewidth, and integral were produced. For comparison, single-voxel proton MR spectroscopy (1H-MRS) was performed to quantify and compare the total Cr concentration in the tumor.

RESULTS

CrCEST contrasts decreased with tumor progression from weeks 3 to 4 in both 9L and F98 phenotypes. More importantly, F98 tumors had significantly lower CrCEST integral compared to 9L tumors. On the other hand, integrals of other Z-spectral components were unable to differentiate both tumor progression and phenotype with limited sample size.

CONCLUSIONS

Given that F98 is a more aggressive tumor than 9L, this study suggests that CrCEST MRI may help differentiate gliomas with different aggressiveness.

Magnetic resonance imaging based functional imaging in paediatric oncology

Imaging is central to management of solid tumours in children. Conventional magnetic resonance imaging (MRI) is the standard imaging modality for tumours of the central nervous system (CNS) and limbs and is increasingly used in the abdomen. It provides excellent structural detail, but imparts limited information about tumour type, aggressiveness, metastatic potential or early treatment response. MRI based functional imaging techniques, such as magnetic resonance spectroscopy, diffusion and perfusion weighted imaging, probe tissue properties to provide clinically important information about metabolites, structure and blood flow. This review describes the role of and evidence behind these functional imaging techniques in paediatric oncology and implications for integrating them into routine clinical practice.

Improved Treatment of Pancreatic Cancer With Drug Delivery Nanoparticles Loaded With a Novel AKT/PDK1 Inhibitor

OBJECTIVES

This research study sought to improve the treatment of pancreatic cancer by improving the drug delivery of a promising AKT/PDK1 inhibitor, PHT-427, in poly(lactic-co-glycolic) acid (PLGA) nanoparticles.

METHODS

PHT-427 was encapsulated in single-emulsion and double-emulsion PLGA nanoparticles (SE-PLGA-427 and DE-PLGA-427). The drug release rate was evaluated to assess the effect of the second PLGA layer of DE-PLGA-427. Ex vivo cryo-imaging and drug extraction from ex vivo organs was used to assess the whole-body biodistribution in an orthotopic model of MIA PaCa-2 pancreatic cancer. Anatomical magnetic resonance imaging (MRI) was used to noninvasively assess the effects of 4 weeks of nanoparticle drug treatment on tumor size, and diffusion-weighted MRI longitudinally assessed changes in tumor cellularity.

RESULTS

DE-PLGA-427 showed delayed drug release and longer drug retention in the pancreas relative to SE-PLGA-427. Diffusion-weighted MRI indicated a consistent decrease in cellularity during drug treatment with both types of drug-loaded nanoparticles. Both SE- and DE-PLGA-427 showed a 6-fold and 4-fold reduction in tumor volume relative to untreated tumors and an elimination of primary pancreatic tumor in 68% of the mice.

CONCLUSIONS

These results indicated that the PLGA nanoparticles improved drug delivery of PHT-427 to pancreatic tumors, which improved the treatment of MIA PaCa-2 pancreatic cancer.

Fractional Anisotropy Correlates with Overall Survival in Glioblastoma

OBJECTIVE

Glioblastoma (GB) is an infiltrative disease that results in microstructural damage on a cellular level. Fractional anisotropy (FA) is an important estimate of diffusion tensor imaging (DTI) that can be used to assess microstructural integrity. The aim of this study was to examine the correlation between FA values and overall survival (OS) in patients with GB.

METHODS

This retrospective single-center study included 122 consecutive patients with GB (50 women; median age, 63 years) with preoperative MRI including fluid attenuated inversion recovery (FLAIR), contrast-enhanced T1-weighted sequences, and DTI. FA and apparent diffusion coefficient (ADC) values in contrast-enhancing lesions (FA-CEL, FA-ADC), nonenhancing lesions, and central tumor regions were correlated to histopathologic and clinical parameters. Univariate and multivariate survival analyses were performed.

RESULTS

Patients with low FA-CEL (median <0.31) showed significantly improved OS in univariate analysis (P = 0.028). FA-CEL also showed a positive correlation with Ki-67 proliferation index (P = 0.003). However, in a multivariate survival model, FA values could not be identified as independent prognostic parameters beside established factors such as age and Karnofsky performance scale score. FA values in nonenhancing lesions and central tumor regions and mean ADC values had no distinct influence on OS.

CONCLUSIONS

FA values can provide prognostic information regarding OS in patients with GB. There is a correlation between FA-CEL values and Ki-67 proliferation index, a marker for malignancy. Noninvasive identification of more aggressive GB growth patterns might be beneficial for preoperative risk evaluation and estimation of prognosis.

Recent Developments in Diffusion Tensor Imaging of Brain

Magnetic resonance imaging (MRI) has come to be known as a unique radiological imaging modality because of its ability to perform tomographic imaging of body without the use of any harmful ionizing radiation. The radiologists use MRI to gain insight into the anatomy of organs, including the brain, while biomedical researchers explore the modality to gain better understanding of the brain structure and function. However, due to limited resolution and contrast, the conventional MRI fails to show the brain microstructure. Diffusion tensor imaging (DTI) harnesses the power of conventional MRI to deduce the diffusion dynamics of water molecules within the tissue and indirectly create a three-dimensional sketch of the brain anatomy. DTI enables visualization of brain tissue microstructure, which is extremely helpful in understanding various neuropathologies and neurodegenerative disorders. In this review, we briefly discuss the background and operating principles of DTI, followed by current trends in DTI applications for biomedical and clinical investigation of various brain diseases and disorders.

CEST signal at 2ppm (CEST@2ppm) from Z-spectral fitting correlates with creatine distribution in brain tumor

In general, multiple components such as water direct saturation, magnetization transfer (MT), chemical exchange saturation transfer (CEST) and aliphatic nuclear Overhauser effect (NOE) contribute to the Z-spectrum. The conventional CEST quantification method based on asymmetrical analysis may lead to quantification errors due to the semi-solid MT asymmetry and the aliphatic NOE located on a single side of the Z-spectrum. Fitting individual contributors to the Z-spectrum may improve the quantification of each component. In this study, we aim to characterize the multiple exchangeable components from an intracranial tumor model using a simplified Z-spectral fitting method. In this method, the Z-spectrum acquired at low saturation RF amplitude (50 Hz) was modeled as the summation of five Lorentzian functions that correspond to NOE, MT effect, bulk water, amide proton transfer (APT) effect and a CEST peak located at +2 ppm, called CEST@2ppm. With the pixel-wise fitting, the regional variations of these five components in the brain tumor and the normal brain tissue were quantified and summarized. Increased APT effect, decreased NOE and reduced CEST@2ppm were observed in the brain tumor compared with the normal brain tissue. Additionally, CEST@2ppm decreased with tumor progression. CEST@2ppm was found to correlate with the creatine concentration quantified with proton MRS. Based on the correlation curve, the creatine contribution to CEST@2ppm was quantified. The CEST@2ppm signal could be a novel imaging surrogate for in vivo creatine, the important bioenergetics marker. Given its noninvasive nature, this CEST MRI method may have broad applications in cancer bioenergetics.

In vivo magnetic resonance imaging of tumor protease activity

Increased expression of cathepsins has diagnostic as well as prognostic value in several types of cancer. Here, we demonstrate a novel magnetic resonance imaging (MRI) method, which uses poly-L-glutamate (PLG) as an MRI probe to map cathepsin expression in vivo, in a rat brain tumor model. This noninvasive, high-resolution and non-radioactive method exploits the differences in the CEST signals of PLG in the native form and cathepsin mediated cleaved form. The method was validated in phantoms with known physiological concentrations, in tumor cells and in an animal model of brain tumor along with immunohistochemical analysis. Potential applications in tumor diagnosis and evaluation of therapeutic response are outlined.

Diagnostic utility of diffusion tensor imaging in differentiating glioblastomas from brain metastases

BACKGROUND AND PURPOSE

Differentiation of glioblastomas and solitary brain metastases is an important clinical problem because the treatment strategy can differ significantly. The purpose of this study was to investigate the potential added value of DTI metrics in differentiating glioblastomas from brain metastases.

MATERIALS AND METHODS

One hundred twenty-eight patients with glioblastomas and 93 with brain metastases were retrospectively identified. Fractional anisotropy and mean diffusivity values were measured from the enhancing and peritumoral regions of the tumor. Two experienced neuroradiologists independently rated all cases by using conventional MR imaging and DTI. The diagnostic performances of the 2 raters and a DTI-based model were assessed individually and combined.

RESULTS

The fractional anisotropy values from the enhancing region of glioblastomas were significantly higher than those of brain metastases (P < .01). There was no difference in mean diffusivity between the 2 tumor types. A classification model based on fractional anisotropy and mean diffusivity from the enhancing regions differentiated glioblastomas from brain metastases with an area under the receiver operating characteristic curve of 0.86, close to those obtained by 2 neuroradiologists using routine clinical images and DTI parameter maps (area under the curve = 0.90 and 0.85). The areas under the curve of the 2 radiologists were further improved to 0.96 and 0.93 by the addition of the DTI classification model.

CONCLUSIONS

Classification models based on fractional anisotropy and mean diffusivity from the enhancing regions of the tumor can improve diagnostic performance in differentiating glioblastomas from brain metastases.

Utility of diffusion tensor imaging in evaluation of the peritumoral region in patients with primary and metastatic brain tumors

In the brain, diffusion tensor imaging is a useful tool for defining white matter anatomy, planning a surgical approach to space-occupying lesions, and characterizing tumors, including distinguishing primary tumors from metastases. Recent studies have attempted, with varying success, to use DTI to define the extent of tumor microinfiltration beyond the apparent borders on T2-weighted imaging. In the present review, we discuss the current state of research on the utility of DTI for evaluating the peritumoral region of brain tumors.

Local diffusion homogeneity (LDH): an inter-voxel diffusion MRI metric for assessing inter-subject white matter variability

Many diffusion parameters and indices (e.g., fractional anisotropy [FA] and mean diffusivity [MD]) have been derived from diffusion magnetic resonance imaging (MRI) data. These parameters have been extensively applied as imaging markers for localizing white matter (WM) changes under various conditions (e.g., development, degeneration and disease). However, the vast majority of the existing parameters is derived from intra-voxel analyses and represents the diffusion properties solely within the voxel unit. Other types of parameters that characterize inter-voxel relationships have been largely overlooked. In the present study, we propose a novel inter-voxel metric referred to as the local diffusion homogeneity (LDH). This metric quantifies the local coherence of water molecule diffusion in a model-free manner. It can serve as an additional marker for evaluating the WM microstructural properties of the brain. To assess the distinguishing features between LDH and FA/MD, the metrics were systematically compared across space and subjects. As an example, both the LDH and FA/MD metrics were applied to measure age-related WM changes. The results indicate that LDH reveals unique inter-subject variability in specific WM regions (e.g., cerebral peduncle, internal capsule and splenium). Furthermore, there are regions in which measurements of age-related WM alterations with the LDH and FA/MD metrics yield discrepant results. These findings suggest that LDH and FA/MD have different sensitivities to specific WM microstructural properties. Taken together, the present study shows that LDH is complementary to the conventional diffusion-MRI markers and may provide additional insights into inter-subject WM variability. Further studies, however, are needed to uncover the neuronal mechanisms underlying the LDH.

Diffusion tensor magnetic resonance imaging of rat glioma models: a correlation study of MR imaging and histology

INTRODUCTION

Diffusion tensor magnetic resonance (MR) imaging (DTI) can be used to characterize the microstructures of ordered biological tissues. This study was designed to assess histological features of gliomas and surrounding brain tissues in rats using DTI.

METHODS

Three types of tumors, a 9L gliosarcoma (n = 8), a F98 glioma (n = 5), and a human glioblastoma xenograft (GBM22; n = 8) were incubated in rat brains and underwent conventional MRI and DTI scanning using a 4.7-T animal MRI system. Fractional anisotropy (FA), isotropic apparent diffusion coefficient, parallel diffusivity (λ//), and perpendicular diffusivity (λ⊥), as well as histological features within several regions of interest were analyzed.

RESULTS

All tumor masses consisted of low-FA central zones (tumor center) and high-FA peripheral regions (tumor rim). Histological examination revealed the existence of highly coherent tumor organizations (circular for 9L and F98 or radial for GBM22) in the tumor rims. There were higher apparent diffusion coefficient, λ⊥, and λ// in the peritumoral edema compared to the contralateral gray matter. There were significantly lower FA and higher λ⊥ in the ipsilateral white matter than in the contralateral white matter for the GBM22 tumor, whereas there were no differences for the 9L and F98 tumors. Histologic examination showed GBM22 tumor infiltration into the ipsilateral damaged white matter.

CONCLUSIONS

Quantitative analysis of DTI indices provides useful information for assessing tumor microstructure and tumor cell invasion into the adjacent gray matter and white matter.

The physical and biological basis of quantitative parameters derived from diffusion MRI

Diffusion magnetic resonance imaging is a quantitative imaging technique that measures the underlying molecular diffusion of protons. Diffusion-weighted imaging (DWI) quantifies the apparent diffusion coefficient (ADC) which was first used to detect early ischemic stroke. However this does not take account of the directional dependence of diffusion seen in biological systems (anisotropy).Diffusion tensor imaging (DTI) provides a mathematical model of diffusion anisotropy and is widely used. Parameters, including fractional anisotropy (FA), mean diffusivity (MD), parallel and perpendicular diffusivity can be derived to provide sensitive, but non-specific, measures of altered tissue structure. They are typically assessed in clinical studies by voxel-based or region-of-interest based analyses.The increasing recognition of the limitations of the diffusion tensor model has led to more complex multi-compartment models such as CHARMED, AxCaliber or NODDI being developed to estimate microstructural parameters including axonal diameter, axonal density and fiber orientations. However these are not yet in routine clinical use due to lengthy acquisition times.In this review, I discuss how molecular diffusion may be measured using diffusion MRI, the biological and physical bases for the parameters derived from DWI and DTI, how these are used in clinical studies and the prospect of more complex tissue models providing helpful micro-structural information.

Blood oxygen level dependent magnetization transfer (BOLDMT) effect

A few studies have reported that magnetization transfer (MT) -preparation interacts with blood oxygen level dependent (BOLD) contrast used for functional magnetic resonance imaging (MRI). However, the mechanism is still not well established. This study shows that blood oxygenation level itself affects MT contrast. MT ratio (MTR) decreases with increased blood oxygenation, which is demonstrated by ex vivo and in vivo experiments. Oxygenated blood shows less MTR contrast compared to deoxygenated blood sample; and higher levels of oxygen inhalation decrease tissue MTR in vivo especially in brain tumor region. The percentage reduction of MTR due to hyperoxia inhalation, referred to as the blood oxygen dependent magnetization transfer (BOLDMT) effect, correlates well with tissue oxygen extraction, which is highest in well-vascularized tumor rim, followed by inner tumor, gray matter (GM), and white matter (WM) normal tissue. Simulations and experiments demonstrate that BOLDMT effect induced with hyperoxia inhalation may be generated by decreased tissue T (1) due to increased O(2) dissolution and increased tissue T (2) due to reduced deoxyhemoglobin (dHb) concentration. Compared to regular T (2)* weighted BOLD contrast, BOLDMT has higher insensitivity to B (0) inhomogeneities. BOLDMT may potentially serve as a reliable and novel biomarker for tumor oxygen extraction.

Blood oxygen level dependent angiography (BOLDangio) and its potential applications in cancer research

Clinically, development of anti-angiogenic drugs for cancer therapy is pivotal. Longitudinal monitoring of tumour angiogenesis can help clinicians determine the effectiveness of anti-angiogenic therapy. Blood oxygen level dependent (BOLD) effect has been widely used for functional imaging and tumour oxygenation assessment. In this study, the BOLD effect is investigated under different levels of oxygen inhalation for the development of a novel angiographic MRI technique, blood oxygen level dependent angiography (BOLDangio). Under short-term (<10 min) generalized hypoxia induced by inhalation of 8% oxygen, we measure BOLD contrast as high as 25% from vessels at 9.4T using a simple gradient echo (GRE) pulse sequence. This produces high-resolution 2D and 3D maps of normal and tumour brain vasculature in less than 10 minutes. Additionally, this technique reliably detects metastatic tumours and tumour-induced intracranial hemorrhage. BOLDangio provides a sensitive research tool for MRI of vasculature under normal and pathological conditions. Thus, it may be applied as a simple monitoring technique for measuring the effectiveness of anti-angiogenic drugs in a preclinical environment.

Using magnetic resonance imaging and spectroscopy in cancer diagnostics and monitoring: preclinical and clinical approaches

Nuclear Magnetic Resonance (MR) based imaging has become an integrated domain in today's oncology research and clinical management of cancer patients. MR is a unique imaging modality among numerous other imaging modalities by providing access to anatomical, physiological, biochemical and molecular details of tumour with excellent spatial and temporal resolutions. In this review we will cover established and investigational MR imaging (MRI) and MR spectroscopy (MRS) techniques used for cancer imaging and demonstrate wealth of information on tumour biology and clinical applications MR techniques offer for oncology research both in preclinical and clinical settings. Emphasis is given not only to the variety of information which may be obtained but also the complementary nature of the techniques. This ability to determine tumour type, grade, invasiveness, degree of hypoxia, microvacular characteristics, and metabolite phenotype, has already profoundly transformed oncology research and patient management. It is evident from the data reviewed that MR techniques will play a key role in uncovering molecular fingerprints of cancer, developing targeted treatment strategies and assessing responsiveness to treatment for personalized patient management, thereby allowing rapid translation of imaging research conclusions into the benefit of clinical oncology.

Integrating Imaging Data into Predictive Biomathematical and Biophysical Models of Cancer

While there is a mature literature on biomathematical and biophysical modeling in cancer, many of the existing approaches are not of clinical utility, as they require input data that are extremely difficult to obtain in an intact organism, and/or require a large number of assumptions on the free parameters included in the models. Thus, there has only been very limited application of such models to solve problems of clinical import. More recently, however, there has been increased activity at the interface of quantitative, noninvasive imaging data, and tumor mathematical modeling. In addition to reporting on bulk tumor morphology and volume, emerging imaging techniques can quantitatively report on for example tumor vascularity, glucose metabolism, cell density and proliferation, and hypoxia. In this paper, we first motivate the problem of predicting therapy response by highlighting some (acknowledged) shortcomings in existing methods. We then provide introductions to a number of representative quantitative imaging methods and describe how they are currently (and potentially can be) used to initialize and constrain patient specific mathematical and biophysical models of tumor growth and treatment response, thereby increasing the clinical utility of such approaches. We conclude by highlighting some of the exciting research directions when one integrates quantitative imaging and tumor modeling.

Association between sociability and diffusion tensor imaging in BALB/cJ mice

The purpose of this study was to use high-resolution diffusion tensor imaging (DTI) to investigate the association between DTI metrics and sociability in BALB/c inbred mice. The sociability of prepubescent (30-day-old) BALB/cJ mice was operationally defined as the time that the mice spent sniffing a stimulus mouse in a social choice test. High-resolution ex vivo DTI data on 12 BALB/cJ mouse brains were acquired using a 9.4-T vertical-bore magnet. Regression analysis was conducted to investigate the association between DTI metrics and sociability. Significant positive regression (p < 0.001) between social sniffing time and fractional anisotropy was found in 10 regions located in the thalamic nuclei, zona incerta/substantia nigra, visual/orbital/somatosensory cortices and entorhinal cortex. In addition, significant negative regression (p < 0.001) between social sniffing time and mean diffusivity was found in five areas located in the sensory cortex, motor cortex, external capsule and amygdaloid region. In all regions showing significant regression with either the mean diffusivity or fractional anisotropy, the tertiary eigenvalue correlated negatively with the social sniffing time. This study demonstrates the feasibility of using DTI to detect brain regions associated with sociability in a mouse model system.

Evaluation of radiation necrosis and malignant glioma in rat models using diffusion tensor MR imaging

Standard MRI cannot distinguish between radiation necrosis and tumor progression; however, this distinction is critical in the assessment of tumor response to therapy. In this study, one delayed radiation necrosis model (dose, 40 Gy; radiation field, 10 × 10 mm(2); n = 13) and two orthotopic glioma models in rats (9L gliosarcoma, n =8; human glioma xenografts, n = 5) were compared using multiple diffusion tensor imaging (DTI) indices. A visible isotropic apparent diffusion coefficient (ADC) pattern was observed in the lesion due to radiation necrosis, which consisted of a hypointense central zone and a hyperintense peripheral zone. There were significantly lower ADC, parallel diffusivity, and perpendicular diffusivity in the necrotic central zone than in the peripheral zone (all P < 0.001). When radiation-induced necrosis was compared with viable tumor, radiation necrosis had significantly lower ADC than 9L gliosarcoma and human glioma xenografts (both P < 0.01) in the central zone, and significantly lower fractional anisotropy than 9L gliosarcoma (P = 0.005) and human glioma xenografts (P = 0.012) in the peripheral zone. Histological analysis revealed parenchymal coagulative necrosis in the central zone, and damaged vessels and reactive astrogliosis in the peripheral zone. These data suggest that qualitative and quantitative analysis of the DTI maps can provide useful information by which to distinguish between radiation necrosis and viable glioma.

Vascular phenotyping of brain tumors using magnetic resonance microscopy (μMRI)

Abnormal vascular phenotypes have been implicated in neuropathologies ranging from Alzheimer's disease to brain tumors. The development of transgenic mouse models of such diseases has created a crucial need for characterizing the murine neurovasculature. Although histologic techniques are excellent for imaging the microvasculature at submicron resolutions, they offer only limited coverage. It is also challenging to reconstruct the three-dimensional (3D) vasculature and other structures, such as white matter tracts, after tissue sectioning. Here, we describe a novel method for 3D whole-brain mapping of the murine vasculature using magnetic resonance microscopy (μMRI), and its application to a preclinical brain tumor model. The 3D vascular architecture was characterized by six morphologic parameters: vessel length, vessel radius, microvessel density, length per unit volume, fractional blood volume, and tortuosity. Region-of-interest analysis showed significant differences in the vascular phenotype between the tumor and the contralateral brain, as well as between postinoculation day 12 and day 17 tumors. These results unequivocally show the feasibility of using μMRI to characterize the vascular phenotype of brain tumors. Finally, we show that combining these vascular data with coregistered images acquired with diffusion-weighted MRI provides a new tool for investigating the relationship between angiogenesis and concomitant changes in the brain tumor microenvironment.

Differentiation between glioblastomas, solitary brain metastases, and primary cerebral lymphomas using diffusion tensor and dynamic susceptibility contrast-enhanced MR imaging

BACKGROUND AND PURPOSE

Glioblastomas, brain metastases, and PCLs may have similar enhancement patterns on MR imaging, making the differential diagnosis difficult or even impossible. The purpose of this study was to determine whether a combination of DTI and DSC can assist in the differentiation of glioblastomas, solitary brain metastases, and PCLs.

MATERIALS AND METHODS

Twenty-six glioblastomas, 25 brain metastases, and 16 PCLs were retrospectively identified. DTI metrics, including FA, ADC, CL, CP, CS, and rCBV were measured from the enhancing, immediate peritumoral and distant peritumoral regions. A 2-level decision tree was designed, and a multivariate logistic regression analysis was used at each level to determine the best model for classification.

RESULTS

From the enhancing region, significantly elevated FA, CL, and CP and decreased CS values were observed in glioblastomas compared with brain metastases and PCLs (P < .001), whereas ADC, rCBV, and rCBV(max) values of glioblastomas were significantly higher than those of PCLs (P < .01). The best model to distinguish glioblastomas from nonglioblastomas consisted of ADC, CS (or FA) from the enhancing region, and rCBV from the immediate peritumoral region, resulting in AUC = 0.938. The best predictor to differentiate PCLs from brain metastases comprised ADC from the enhancing region and CP from the immediate peritumoral region with AUC = 0.909.

CONCLUSIONS

The combination of DTI metrics and rCBV measurement can help in the differentiation of glioblastomas from brain metastases and PCLs.

Intravoxel incoherent motion imaging of tumor microenvironment in locally advanced breast cancer

Diffusion-weighted imaging plays important roles in cancer diagnosis, monitoring, and treatment. Although most applications measure restricted diffusion by tumor cellularity, diffusion-weighted imaging is also sensitive to vascularity through the intravoxel incoherent motion effect. Hypervascularity can confound apparent diffusion coefficient measurements in breast cancer. We acquired multiple b-value diffusion-weighted imaging at 3 T in a cohort of breast cancer patients and performed biexponential intravoxel incoherent motion analysis to extract tissue diffusivity (D(t)), perfusion fraction (f(p)), and pseudodiffusivity (D(p)). Results indicated significant differences between normal fibroglandular tissue and malignant lesions in apparent diffusion coefficient mean (±standard deviation) values (2.44 ± 0.30 vs. 1.34 ± 0.39 μm(2)/msec, P < 0.01) and D(t) (2.36 ± 0.38 vs. 1.15 ± 0.35 μm(2)/msec, P < 0.01). Lesion diffusion-weighted imaging signals demonstrated biexponential character in comparison to monoexponential normal tissue. There is some differentiation of lesion subtypes (invasive ductal carcinoma vs. other malignant lesions) with f(p) (10.5 ± 5.0% vs. 6.9 ± 2.9%, P = 0.06), but less so with D(t) (1.14 ± 0.32 μm(2)/msec vs. 1.18 ± 0.52 μm(2)/msec, P = 0.88) and D(p) (14.9 ± 11.4 μm(2)/msec vs. 16.1 ± 5.7 μm(2)/msec, P = 0.75). Comparison of intravoxel incoherent motion biomarkers with contrast enhancement suggests moderate correlations. These results suggest the potential of intravoxel incoherent motion vascular and cellular biomarkers for initial grading, progression monitoring, or treatment assessment of breast tumors.

Effects of cardiac pulsation in diffusion tensor imaging of the rat brain

The purpose of this study was to investigate the effects of cardiac pulsation in diffusion tensor imaging (DTI) of the rat brain. DTI data were acquired either with or without different cardiac gating delays. For each case, two sets of identical DTI data were acquired for a bootstrap analysis to measure the uncertainty in estimating mean diffusivity (MD), fractional anisotropy (FA) and the primary eigenvector direction. The 95% confidence interval of the primary eigenvectors was substantially reduced (21-25%) when cardiac gating with triggering delay of 70 ms (∼half of R-R peak duration) was used in comparison to studies without gating or when gating with a triggering delay of 0 ms was used. Standard deviations of MD and FA estimates were also reduced by 12-26% and 13-24%, respectively. For voxels with mean FA values larger than 0.15 and smaller than 0.95, the decrease in CI and standard deviations of MD and FA by cardiac gating with triggering delay of 70 ms were significant (p < 0.05). These results demonstrate the importance of cardiac gating in acquisition of in vivo high resolution DTI data.

Advanced MRI: translation from animal to human in brain tumor research

Advanced magnetic resonance imaging (MRI) techniques, such as magnetic resonance spectroscopy, diffusion MRI, and perfusion MRI, allow for a diverse range of multidimensional information regarding brain tumor physiology to be obtained in addition to the traditional anatomic images. Although it is well documented that MRI of rodent brain tumor models plays an important role in the basic research and drug discovery process of new brain tumor therapies, the role that animal models have played in translating these methodologies is rarely discussed in such articles. Even in consensus reports outlining the pathway to validation of these techniques, the use of animal models is given scant regard. This is despite that the use of rodent cancer models to test advanced MRI techniques predates and was integral to the development of clinical MRI. This article highlights just how integral preclinical imaging is to the discovery, development, and validation of advanced MRI techniques for imaging brain neoplasms.

Differentiation between glioblastomas and solitary brain metastases using diffusion tensor imaging

The purpose of this study is to determine whether diffusion tensor imaging (DTI) metrics including tensor shape measures such as linear and planar anisotropy coefficients (CL and CP) can help differentiate glioblastomas from solitary brain metastases. Sixty-three patients with histopathologic diagnosis of glioblastomas (22 men, 16 women, mean age 58.4 years) and brain metastases (13 men, 12 women, mean age 56.3 years) were included in this study. Contrast-enhanced T1-weighted, fluid-attenuated inversion recovery (FLAIR) images, fractional anisotropy (FA), apparent diffusion coefficient (ADC), CL and CP maps were co-registered and each lesion was semi-automatically subdivided into four regions: central, enhancing, immediate peritumoral and distant peritumoral. DTI metrics as well as the normalized signal intensity from the contrast-enhanced T1-weighted images were measured from each region. Univariate and multivariate logistic regression analyses were employed to determine the best model for classification. The results demonstrated that FA, CL and CP from glioblastomas were significantly higher than those of brain metastases from all segmented regions (p<0.05), and the differences from the enhancing regions were most significant (p<0.001). FA and CL from the enhancing region had the highest prediction accuracy when used alone with an area under the curve of 0.90. The best logistic regression model included three parameters (ADC, FA and CP) from the enhancing part, resulting in 92% sensitivity, 100% specificity and area under the curve of 0.98. We conclude that DTI metrics, used individually or combined, have a potential as a non-invasive measure to differentiate glioblastomas from metastases.