ABSTRACT

Background: Gliomas are the most common primary neoplasms of the central nervous system. Relative cerebral blood volume (rCBV) could estimate high-grade Gliomas computed with dynamic susceptibility contrast MR imaging which it is artificially lowered by contrast extravasation through a disrupted blood-brain barrier.

Objectives: Our intent was to clarify the usefulness of diffusion-weighted magnetic resonance imaging (DWI) and perfusion weighted magnetic resonance imaging (PWI) in the grading of Gliomas.

Materials and Methods: Both PWI and DWI with a three-tesla scanner investigated nineteen consecutive patients with Gliomas. The means of rCBV and ADC values have been compared among the tumor groups with t-test and ROC curve analysis to determine threshold values of Gliomas grading.

Results: Mean maximum rCBV were 2.71±1.41 for low grades (I & II), and 8.14±2.58 for high grades (III & IV) Gliomas (p=0.001). Mean minimum ADC were 1.47±.46 ×10³ mm²/s for low grades (I & II), and .47±.38×10³ mm²/s for high grades (III & IV) Gliomas (p=0.001). We can get 0.94×10³ mm²/s for minimum ADC and 3.85 for maximum rCBV as a difference cutoff point between low and high-grade Gliomas.

Conclusion: Combination of both DWI and PWI techniques, with measurement of minimum ADC and maximum rCBV can be used to distinguish between high grade and low-grade Glioma tumors.

Keywords: Diffusion Magnetic Resonance Imaging; Neoplasm Grading

Introduction

Gliomas are the most common primary neoplasms of the central nervous system and are classified into four grades by the World Health Organization (WHO) (1,2). Tumor grading has critical implications for preoperative planning therapeutic approaches, subsequent follow-up care, and assessing the patient prognosis and response to therapy (3). However, conventional magnetic resonance imaging (MRI) alone is not always accurate in predicting Glioma grade. Glioma grading is based on the histopathlogical assessment (3). Advanced MR imaging techniques such as DSC (Dynamic Susceptibility Contrast) and Diffusion Weighted Imaging (DWI) provide physiologic information that complements the anatomic information obtained from conventional MRI (3,4). Diffusion is a term used to describe the movement of molecules due to random thermal motion restricted by boundaries such as ligaments, membranes and macromolecule. Sometimes restrictions in diffusion are directional, depending on the structure of the tissues. Diffusion of molecules also occurs across tissues, especially from areas of restricted diffusion to areas with free diffusion (3,5). The comprehensive displacement of molecules is called the apparent diffusion coefficient (ADC) and a sequence can be sensitized to this motion by applying two gradients on either side of the 180^o radiofrequency (RF) pulse. This works in the similar way to phase contrast Magnetic Resonance Angiography (MRA) in that stationary spins will acquire no net phase change after applying the gradients. Moving spins, however, will acquire this phase change and result in a signal loss. In diffusion imaging, normal tissue has lower signal intensity than abnormal tissue, as the molecules within it are free to move, while diffusion becomes restricted when there is a pathologic change. The signal change depends on the ADC (Apparent Diffusion Coefficient) of the tissue and the strength of the gradients. Their amplitude are controlled by the b factor/value which is similar to the VENC (Encoding Velocity) in phase contrast MRA (4,5). This is another type of weighting in diffusion imaging; an extrinsic contrast parameter (b factor) controls how much a tissue's ADC contributes towards image weighting. If the TE (echo time) and TR (repetition time) are long and b=zero image is T2 weighted. If we then increase the b factor, then the image weighting changes from T2 to diffusion weighting. By this, we mean that areas will have a high signal not because they have a long T2 time but because they have a low ADC. This is why this technique is called diffusion weighted imaging. It is, in fact, another type of weighting. 'b' is expressed in units of s/mm² (6). ADC maps are acquired via post-processing by calculating the ADC for each voxel of tissue and allocating signal intensity according to its value. Therefore, restricted tissue, which has a low ADC, is darker than free diffusing areas that have a high ADC (7). The contrast is therefore the mirror of the trace images. This is useful when T2 shine through is a problem. DWI quantifies cellularity based on the premise that water diffusion within the extracellular compartment is inversely related to the content and attenuation of the intracellular space (8). The higher the tumor cellularity and grade are lead to the lower the ADC and this fact is because of decreased water diffusion (4,9). However, other factors may be complicating this relationship: ADC increases with increased edema and increased edema is seen in high-grade tumors. Differences in the restriction of water movement also can result from difference in tumor cellularity (7). Clinical perfusion measurements can be made with radiotracers, but as MRI is a non-ionizing technique with high spatial and temporal resolution that can be co-registered with anatomic information, there is much interest in perfusion MRI studies. Perfusion is the regional blood flow in tissues and is defined as the volume of blood that flows into one gram of tissue. Perfusion is a measure of the quality of vascular supply to a tissue and, since vascular supply and metabolism are usually related, perfusion can be used to measure tissue activity (5,6). Perfusion is measured using MRI by tagging the water in arterial blood during image acquisition. Tagging can be achieved either by a bolus injection of exogenous contrast agent like gadolinium, or by saturating the protons in arterial blood with RF inversion or saturation pulses. As the difference between tagged and untagged images is so small, ultra-fast imaging methods are desirable for reducing artifact. In their simple forms, perfusion images can be acquired with fast-scanning acquisitions before, during and after a bolus injection of intravenous contrast (5,7,10). In one type of this technique uses a bolus injection of gadolinium is administered intravenously during ultra-fast T2 or T2^* acquisitions to evaluate perfusion. In this case, the contrast agent causes transient decreases in T2 and T2* decay in and around the microvasculature perfused with contrast. SS-GE-EPI (Single Shot-Gradient Echo-Echo Planar Imaging) sequences are usually used as they produce the required temporal resolution to measure such transient changes. In the brain, the first-pass extraction of contrast agent is zero when the blood–brain barrier (BBB) is reasonably intact, and the intravascular compartmentalization of contrast agent creates large, microscopic susceptibility gradients, and the dephasing of spins as they diffuse among them result in signal loss in T₂ and T₂^*-weighted images, as first described by Villringer (10). The microscopic susceptibility gradients caused by the contrast agent, cause signal loss in a manner that depends on the type and the timing of the applied pulse sequence. Gradient echo EPI, especially when used with echo shifting (where the TE is longer than the TR) maximizes the susceptibility effects. After data acquisition, a signal decay curve is used to ascertain blood volume, transient time and measurement of perfusion (8,11). The measured MR signal intensity change versus time curve is converted into a contrast agent tissue concentration (ΔR2 or ΔR2*) versus time curve. This relationship is the key link allowing the calculation of cerebral hemodynamics. Tumor blood volume is calculated from the area under the T2 or T2^* rate change (ΔR2 or ΔR2*) and ΔR2 (ΔR2*) can be calculated from signal intensity before and during the passage of contrast agent, as follows:

Where S (t) is MR signal at time (t), S (0) is the mean MR signal before the arrival of contrast agent, and TE is echo time. Tumor blood volume can be normalized by volume of the reference tissue (e.g. white matter) to generate an index of tumor vascularity (11). It is generally assumed that T2 and T2* depend upon contrast agent concentration according to the following relationship:

Where C is the concentration of the contrast agent in the blood, CBV is defined as the voxel volume occupied by blood vessels, and R or R* is the relaxivity of gadolinium-based contrast agent. Relaxivity depends on the molecular structure and kinetic of the complex, second sphere water exchange, and the rotational dynamics of the molecule. The T2 and T2* effects of a contrast agent are almost entirely determined by its magnetic susceptibility, i.e. its tendency to enhance magnetic fields, so the molecular chemistry of the agent is important only to the extern that it affects susceptibility. Unfortunately, the relaxation of the agent is not certain (11,12). A theory for the 12 effects of contrast in vessels could help us to estimate R*. According to it that R* is not strongly dependent on tissue type as long as the blood volume is predominantly large enough in vessels, usually larger than capillaries, that diffusion is not important, and they are randomly oriented. However, been a report demonstrating that R is significantly smaller in tissues with complex microvascular structure, such as kidney and spleen than in other tissues (12). Time intensity curves for multiple images acquired during and after injection are combined to generate a cerebral blood volume (CBV) map. CBV is measured in unit of ml/100g/min and can be calculated by the integration of the concentration versus time curve. CBV reflects any increase in the cerebral capillary and venular beds, such as vasodilation or in angiogenesis (7). The angiogenic process is complex and can be stimulated by any one of several mechanisms. Typically, growth of tissue, which has outstripped its local blood supply, results in regional hypoxia and hypoglycemia that stimulates the release of local chemical messengers from the cells of the tissue itself. The best known of these messengers is the cytokine, vascular endothelial growth factor (VEGF). VEGF is a common and potent angiogenic stimulator that is found in many pathological tissues. It is released in response to local hypoglycemia and/or hypoxia and has several effects, each of which will improve metabolic supply. In the short term, VEGF will act directly on local capillaries to increase endothelial permeability resulting in an immediate increase in the supply of nutrients (13). This increase in permeability is also believed to form an important part of the metastatic mechanism, allowing passage of tumor cells into the circulation (14). DSC provides noninvasive assessment of tumor vascularity and angiogenesis (4,8,15) through the examination of the degradation of signal intensity with time associated with the first pass of a bolus of paramagnetic contrast agent (16). Because higher vascularity corresponds to a higher tumor grade, as the grade of the astrocytoma increases, the maximum tumor CBV tends to increase (8,15). Recent developments in MRI have allowed for the assessment of cerebral blood volume (CBV) derived from perfusion-weighted MRI and perfusion abnormalities in brain tumors. An important factor in the malignancy of tumors is their ability to recruit and synthesize vascular networks for further growth and proliferation. Relative cerebral blood volume (rCBV) maps and measurements have been shown to correlate reliably with tumor grade and histological findings of increased tumor vascularity (17,18):

The aim of this study was to evaluate the diagnostic accuracy of combined ADC and CBV values in the preoperative differentiation of diffuse Gliomas. Our objectives were as follow. 1) To calculate CBV and ADC values for diffuse Gliomas included in the study. 2) To establish whether there is any difference in rCBV and ADC values in Gliomas classified by tumor grade and histology. 3) To estimate a cut-off CBV and ADC value for differentiation of high- and low-grade Gliomas. 4) To investigate whether combined CBV and ADC values improve the diagnostic accuracy of MRI.

Materials and Methods

Patient Population

We retrospectively reviewed data obtained during a 1-year period (September 2013 - September 2014) from 19 patients with a histopathologically proved diagnosis of Glioma. They underwent an MR imaging study at our hospital (Imam Khomeini Hospital, Tehran, Iran) after signing a written informed consent.

 Diagnosis was histologically confirmed by surgical resection or biopsy. The tumor specimens obtained were examined by neuropathologist and graded as low-grade (grades I and II) and high grade (grades III and IV) tumors according to the WHO Classification of Tumors of the Central Nervous System (2007) (19).

Conventional MRI

Imaging was performed with a three-tesla scanner (MAGNETOM TRIO A Tim SIEMENSE). We performed the following anatomic sequences: a 3-plane localizer sequence, T₂_tirm_tra_dark-fluid_5mm (TR_8910 ms, TE_93 ms, TI_2489 ms), T₂ _tse_tra_384_5mm (TR_3230 ms, TE_93 ms), T₂_tse_sag_320_ 4mm (TR_4410, TE_77), T₂_tse_cro_320_4mm (TR_5510 ms, TE_77), and t1_fl2d_tra (TR_401 ms, TE_2.48 ms). All data were obtained by using 5mm thick sections with distance factor 20%, FOV read_220mm and FOV phase_75%.

PWI Parameters

Dynamic contrast–enhanced T2^*-weighted gradient-echo echo-planar images were acquired  during  the  first  pass  of  a  bolus of Dotarem, 1 mmol/mL at a dose of 0.1 mmol/kg. Nineteen sections were selected for perfusion MR imaging to cover the tumor based on T2-weighted and FLAIR images. For the sixth acquisition, Dotarem was injected at a rate of 4 mL/s with a power injector followed by administration of a 20-mL bolus of saline at 4 mL/s.

DWI parameters

DWI was performed before administration of contrast medium by using a multi-shot spin-echo echo-planar imaging sequence with the following parameters: TR/TE_3500/93 ms, image acquisition in the axial plane, slice thickness 4-mm, distance factor 30%, number of slice_21, FOV read 220mm , FOV phase 100mm , b-values of 0 and 1000 s/mm².

Perfusion MRI Data Evaluation

DSC images were processed by using commercially available Perfusion Map Analysis (PMA) software. The beginning and the end of the first-pass bolus were determined through inspection of the time–signal-intensity curve and care was taken to exclude any recirculation-related signal intensity. Cerebral blood volume refers to the amount of blood in a given region of brain tissue at any time, commonly measured in milliliters per 100 g of brain tissue. Because the CBV must be expressed relative to an internal reference, we normalized it by expressing ratios relative to values measured in the normal white matter of the contralateral lobe. We have referred to these relative values as rCBV. If we want to gain rCBV map, Arterial Input Function (AIF) should not be determined for bolus of contrast. Color-coded rCBV maps were generated to target regions of maximum abnormality. We placed 3 ROIs (Region of Interest) within the tumor, on areas showing the highest intra-tumoral rCBV on the maps. The standardized ROI was 2–3mm², which was used for most of the tumor and white matter measurements (Figure 1). Care was taken not to include large intra- or peri-tumoral vessels because these can confound perfusion measurements. The maximum rCBV value in intratumoral ROIs was selected for quantitative analysis and correlated with co­rresponding specimen histo­pathology. This method has been demonstrated to provide the most optimal inter observer and intra observer re­producibility (20).

Diffusion MRI Data Evaluation

ADC maps and values were calculated by using commercially available PMA software

with the ROIs. This software has been designed for analysis both DWI and PWI images and we can calculate ADC with draw ROIs in ADC map on interested area of tumor. Five circular ROIs (areas ranging from 1 to 2 mm²) were manually constructed and placed over the whole tumor to select the region of the maximum hypo intensity co­rresponding to the solid portion of tumor. Afterward, the minimum ADC obtained was selected for the analysis. The ROIs were carefully placed to avoid cystic, necrotic, and hemo­rrhagic regions that might influence ADC values (Figure 1).

Figure 1: Images of T2 (a), ADC (b) , PWI (c) and rCBV map (d) in a 47 year-old  man with a right temporal grade II astrocytoma. We placed 3 ROIs within the tumor, on areas showing the highest intra-tumoral rCBV on the map, and we calculated ADC with draw five ROIs in ADC map, on interested area of tumor.

Statistical Analysis

An access database was created to track maximum rCBV, minimum ADC, and tumor grade of diffuse Gliomas. Using the Statistical Package for the Social Sciences Version 16 software (SPSS), we performed statistical analysis. At first, we check the normality of our data with Kolmogorov–Smirnov Z test. With these test we found that both group values (ADC & rCBV) have normality distribution; so mean rCBV and ADC values were compared among the tumor groups with the Standard t test. A p<0.001 indicated a statistically significant difference. A ROC curve analysis was obtained to determine the cutoff point of rCBV and ADC values that had the best combination of specificity and sensitivity for differentiating high and low-grade Gliomas. In the ROC curve, the size of the area under the curve indicated the degree of the relationship between rCBV or ADC values and the grade of the Gliomas: the closer to one, have stronger relationship.

Results

Patient Population

The group consisted of 19 patients (8 men and 11 women), with ages ranging from 11 to 62 years. We investigated 9 low-grade  (47.37%) and 10 high-grade Gliomas (52.63%).

Evaluation of rCBVmax & ADCmin Values for Glioma Grading

rCBV values were significantly effective in grading glial tumors as low-grade and high-grade (p=0.0009). Although     some overlap of distribution was shown between the values of the two groups, rCBV values were statistically different between grade II and grade IV Gliomas. ADC measurements were significantly effective in grading glial  tumors as low-grade and high-grade (p=0.0004). As we found in Diffusion data analysis, some overlap of distribution was shown between the ADC values of two groups. (Table1)