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Table of Contents
ORIGINAL ARTICLE
Year : 2022  |  Volume : 11  |  Issue : 3  |  Page : 150-156

A prospective study to evaluate the role of multiparametric magnetic resonance imaging in the grading of gliomas using magnetic resonance imaging perfusion and diffusion and multivoxel magnetic resonance spectroscopy


1 Department of Radiology, Fortis Hospital, Mohali, India
2 Department of Radiology, Homi Bhabha Cancer Hospital and Research Centre, Sangrur and Mullanpur, Mohali, India
3 Department of Neurosurgery, Fortis Hospital, Mohali, India
4 Department of Radiology, AIIMS, Bathinda, Punjab, India

Date of Submission27-Mar-2022
Date of Decision21-Apr-2022
Date of Acceptance24-Apr-2022
Date of Web Publication08-Jun-2022

Correspondence Address:
Rahat Brar
Department Of Radiology, Homi Bhabha Cancer Hospital & Research Center, Sangrur & Mullanpur, Punjab. A Unit of Tata Memorial Centre Mumbai
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jcsr.jcsr_50_22

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  Abstract 


Background: The primary aim of this study was to evaluate the individual and combined efficacy of magnetic resonance imaging (MRI) parameters, which include MRI perfusion, MRI diffusion-weighted imaging (DWI) and magnetic resonance spectroscopy (MRS) in the grading of gliomas as low grade versus high grade. The pre-operative imaging-based grading of gliomas by multiparametric MRI was compared with the gold standard histopathological studies.
Methods: A total of 22 patients referred to the radiology department for multiparametric MRI of the brain with presumptive diagnosis of glioma on computed tomography/MRI were included in the study. Conventional T1, T2 and fluid-attenuated inversion recovery images were obtained followed by perfusion MRI using gadopentetate dimeglumine (Magnevist) administration. This was followed by DWI and MRS.
Results: Our statistical analysis demonstrated that a cut-off of apparent diffusion coefficient value of 954.085 (10–6 mm2/Sec) provides a sensitivity and specificity of 87.5% and 85.7%, respectively, in differentiating low-grade gliomas (LGGs) from high-grade gliomas (HGGs). A choline/creatine ratio cut-off value of 2 provides sensitivity and specificity of 100% and 92.9%, respectively, while a cut-off value of 1.45 of choline/N-acetylaspartate ratio provides both sensitivity and specificity of 100% in differentiating LGG from HGG. A cut-off of 1.9 for maximum relative cerebral blood volume (rCBV) value provides both sensitivity and specificity of 100% in differentiating LGGs from HGGs.
Conclusions: We concluded that perfusion MRI (rCBV) was the best parameter among perfusion MRI, DWI and MRI spectroscopy in differentiating HGGs from LGGs. Combined multiparametric results showed a diagnostic accuracy, sensitivity, specificity, positive predictive value and negative predictive value of 86.4%, 82.4%, 100%, 100% and 62.5%, respectively, on comparison with gold standard histopathological grading.

Keywords: Glioma, grading, magnetic resonance imaging, magnetic resonance imaging diffusion, magnetic resonance imaging perfusion, magnetic resonance spectroscopy, multiparametric


How to cite this article:
Chopra H, Brar R, Rathore DS, Dwivedi A, Prasad A, Rana S, Budhiraja M, Goudihalli SR, Singh P. A prospective study to evaluate the role of multiparametric magnetic resonance imaging in the grading of gliomas using magnetic resonance imaging perfusion and diffusion and multivoxel magnetic resonance spectroscopy. J Clin Sci Res 2022;11:150-6

How to cite this URL:
Chopra H, Brar R, Rathore DS, Dwivedi A, Prasad A, Rana S, Budhiraja M, Goudihalli SR, Singh P. A prospective study to evaluate the role of multiparametric magnetic resonance imaging in the grading of gliomas using magnetic resonance imaging perfusion and diffusion and multivoxel magnetic resonance spectroscopy. J Clin Sci Res [serial online] 2022 [cited 2022 Aug 12];11:150-6. Available from: https://www.jcsr.co.in/text.asp?2022/11/3/150/347041




  Introduction Top


The incidence of central nervous system (CNS) tumours in India is 5–10/100000 population, and it constitutes 2% of all tumours.[1] Intracranial tumours can be intra-axial or extra-axial. Among intra-axial tumours, they can be of neuroepithelial type, intraventricular type, lymphomas and metastases. Extra-axial tumours include meningiomas, cranial nerve sheath tumours, pituitary region tumours, skull base tumours and meningeal metastases. Among intra-axial tumours, neuroepithelial tumours are a common type. Gliomas are the most common neuroepithelial tumours. Based on the cell of origin, gliomas may be astrocytic, oligodendrocytic and ependymal type. Out of the gliomas, astrocytoma being the most common ranges from pilocytic astrocytoma (WHO grades I) to glioblastoma (WHO grade IV). Recently, based on deoxyribonucleic acid (DNA) profiling and gene expression, gliomas are characterised. The most relevant gene abnormalities are isocitrate dehydrogenase (IDH) 1 and IDH 2, methylguanine-DNA methyltransferase, 1p and 19q chromosomal translocation and epidermal growth factor receptor overexpression.

Different radiological modalities can be used in the evaluation of gliomas. Among these, magnetic resonance imaging (MRI) is the main modality of choice for the evaluation of the gliomas. Evaluation based on MRI can range from conventional MRI sequences to advanced physiological MRI. The usefulness of conventional MRI sequences is, however, limited because they provide only anatomical details of gliomas and cannot give details about physiological picture of gliomas, which is required for grading of gliomas.

The categorisation of gliomas in high and low grades is of paramount importance as it decides the treatment strategy and also helps in predicting the prognosis.[2],[3] The standard treatment for high-grade gliomas (HGGs) is surgical intervention followed by chemo-radiation, while that of low-grade gliomas (LGGs) is surgical excision alone.

The histopathological grading of gliomas depends on various factors such as vascular endothelial proliferation, presence or absence of necrosis, nuclear atypia and mitosis.[2] However, the histological grading of gliomas also has its own limitations. These include errors due to lack of representative sample from the lesion, which is a common occurrence in stereotactic and guided biopsies where samples may be taken from non-representative site. Sometimes, obtaining adequate samples for testing is not possible as the lesion is deep seated in eloquent areas of the brain.[4],[5],[6] Due to these limitations, neurosurgeons are heavily dependent on imaging modalities like MRI for this grading of gliomas. In fact, the final histology and grade of glioma are usually correlated with the MRI findings before the final decision on grading is reached.

With the advent of advanced physiological MRI imaging, grading of gliomas has been attempted using perfusion-weighted MRI (PW-MRI), diffusion-weighted MRI (DW-MRI) and magnetic resonance spectroscopy (MRS).Grading of gliomas is essential for predicting treatment response and overall survival of patients. These physiological MR imaging methods are also useful in the assessment of infiltration of gliomas in peritumoural regions, which is very important for pre-operative grading. Increased vascular density and higher cellular density are features of HGGs.

The limitation of histopathology in the assessment of angiogenesis in tumours can be overcome with PW-MRI. Perfusion MRI is used to evaluate hemodynamic characteristics of the lesion, which includes parameters such as vascularity, blood volume, blood flow and permeability. The main parameters used in PW-MRI for grading of gliomas are relative cerebral blood volume (rCBV), cerebral blood flow (CBF) and mean transit time. In HGGs, rCBV and CBF usually increased.

Assessment of glioma grade

Using magnetic resonance spectroscopy

Gliomas usually have a characteristic signature pattern on spectroscopy and show a decreased level of NAA and an increased level of Cho. This leads to a raised choline/N-acetylaspartate (Cho/NAA) ratio. Elevated Cho correlates with a worse prognosis of the tumour as it signifies higher membrane turnover and also a higher cellular density. There are, however, few exceptions as some studies have suggested that even HGGs can have lower levels of Cho than LGGs due to the presence of necrosis in HGGs as all metabolites tend to have low values due to a higher degree of necrosis.[7] While at the same time, they also exhibit an elevated level of lactate (Lac) and lipid (Lip) metabolite. Lactate peak tilts the diagnosis towards a high-grade lesion, while an elevated level of myo-inositol is a marker of LGGs. Elevated lipid levels are a marker of tissue necrosis and membrane breakdown, which is a hallmark of HGG.

Using perfusion magnetic resonance imaging

Vascular morphology plays a critical role in determining the malignant potential of gliomas. This, in turn, affects the overall survival and prognosis of the patient.[8] The most important perfusion parameter which determines this is rCBV. Thus, quantitative values of rCBV assist in differentiating LGGs from HGGs.[9] Low-grade astrocytomas are known to have a lower rCBV than anaplastic astrocytomas and glioblastomas.[10] A few exceptions to this rule exist as non-astrocytic gliomas and oligodendrogliomas can have high rCBV even in low-grade settings.[11]

Using diffusion imaging

The apparent diffusion coefficient (ADC) maps obtained from diffusion-weighted imaging (DWI) have been of great help in delineating the lesion from peritumoural oedema and also in defining the extent of the solid enhancing tumour. DWI also helps in cases where the lesion shows subtle or no enhancement on post-contrast images. It also differentiates tumour necrosis from cystic degeneration.[12]

As the tumour cells show decreased intracellular mobility, the contrast-enhancing regions of the lesions have lower ADC values. On the other hand, necrotic regions exhibit higher ADC values.[13],[14] In total, LGGs have high ADC values, whereas HGGs have low ADC values.[15],[16]

Aims and objectives

The primary aim of the study was to evaluate the individual and combined efficacy of MRI parameters – MRI perfusion, MRI diffusion and multivoxel MRS – in the grading of gliomas and also to compare the results of pre-operative grading of gliomas by multiparametric study with the gold standard histopathological studies.


  Material and Methods Top


The present study was a hospital-based prospective cohort study. The sample size was estimated based on assumption that the incidence of CNS tumours is 1%–2%. The incidence of gliomas is 38% of these CNS tumours, which will be approximately 1%.[17] To estimate this proportion with a 95% confidence interval of proportion and margin of error as 5%, the sample size required was 15 subjects using this formula:

n0 = z2(p)(q)/(d2)

Where n0 is sample size, z is value for selected alpha level, for example, 1.96 for (0.05), i.e., at 95% confidence level. p is the estimated proportion of attribute that is present in population. q is 1 − p. d is acceptable margin of error for the proportion being estimated (we have taken 5%), it was decided to include 20 patients in total.

All patients referred to the radiology department for multiparametric MRI of the brain with presumptive diagnosis of glioma on computed tomography (CT)/MRI were included in the study.

Conventional MR sequences: MRI examinations were conducted on 3-Tesla MRI system (Siemens Magnetom Spectra – A Tim + Dot System) in supine position using standard 16-channel head coil. Conventional T1, T2 and fluid-attenuated inversion recovery images were obtained with TR/TE being 2500/45, 5000/90, 9000/90, respectively. Matrix size of 128 × 128, slice thickness of 5.0 mm, 1.5 mm gap, flip angle of 140, voxel size of 0.7 mm × 0.7 mm × 5.0 mm was used. Post-contrast images were obtained using multiplanar T1 FS (TR/TE 1980/50; slice thickness of 5 mm with 1 mm gap) and 3D MPRAGE (TR/TE 1900/6; slice thickness of 1 mm with 0.5 mm gap) sequences.

Dynamic magnetic resonance perfusion imaging

DSC MRI was performed using a T2 -weighted single-shot gradient-echo echo-planar imaging during contrast medium gadopentetate dimeglumine (Magnevist) administration. Parameters of the sequence were TR/TE of 1600/35 mSec, FOV of 240 mm × 240 mm, matrix size of 96 × 128, slice thickness of 5 mm, interslice gap of 0, flip angle of 60, number of slices of 26 and acquisition time of 83 Sec, and 53 images were obtained at intervals equal to the repetition time. After 8 Sec, an 18-mL contrast bolus of gadopentetate dimeglumine (0.5 mmol/mL) was administered intravenously using hand injection and immediately followed by a 15-mL bolus of saline injected through an antecubital angiocatheter.

The measurements of CBV were obtained from perfusion maps using ROIs. Correct placement of ROIs on the solid portion of the tumour was ensured. Manual measurement of the maximum signal enhancement on colour-coded DSC CBV maps was done, and the highest CBV values were obtained from these ROIs. The ROI size was kept uniform to avoid errors (12 × 12 pixels, 17.58 mm2). The ROI placement was kept away from vessels and areas of necrosis. Then, the rCBV ratios were obtained by dividing the tumour value by the corresponding contralateral white matter values.

Diffusion-weighted imaging (DWI) was done using 2D echo-planar imaging obtained at three b values of 0, 500 and 1000 in x, y and z directions. MRS: Multivoxel MRS was done using intermediate TE 2D chemical shift imaging with TR 1700, TE 140 mSec, flip angle 90, bandwidth 1000 Hz, vector size 1024 and voxel size 11.4 mm × 11.4 mm × 15 mm.

The sensitivity and specificity of _______ were calculated using _____ as gold standard.


  Results Top


The age distribution of the patients included in this study ranged between 23 years and 83 years (mean age of 48.73 years). Out of 22 patients, 12 (54.5%) were <50 years and 10 (45.5%) were ≥50 years. The study included 22 patients, whereby 14 (64%) of them were male and 8 (36%) were female. The majority of these intra-axial lesions were supratentorial and predominantly affected the frontal and parietal lobes. Most of the lesions showed peripheral enhancing margins on T1WI post-contrast (n = 15, 68.2%), followed by no obvious enhancement in 3 patients (13.6%) and patchy post-contrast enhancement in 3 patients (13.6%). On diffusion-weighted images (DWI), the majority of patients (45.5%) showed patchy areas of diffusion restriction, while there was an equal distribution of patients showing subtle restriction (18.2%) and restriction in peripheral region only (18.2%). On the other hand, 13.6% of patients did not show any diffusion restriction.

Using multiparametric MRI, HGG was diagnosed in 14 patients (64%) and LGG in 8 patients (36%), while the final histopathological diagnosis showed HGG in 17 (67%) and low grade in the remaining 5 patients (23%).

In DWI, parameter used was ADC (10–6 mm2/Sec) with a mean value for low grade was 1180.2288 and for HGGs was 844.8728. In perfusion-weighted imaging (PWI), parameter used was rCBV with a mean value for low grade was 1.2 and for HGGs was 6.4. The mean value of choline/creatine (Cho/Cr) and Cho/NAA for low grade and HGGs was 1.6 and 3.9 and 1.2 and 3.4, respectively, in MRS [Table 1].{Table 1}

Individual magnetic resonance imaging parameter study

Diffusion magnetic resonance imaging and apparent diffusion coefficient (10–6 mm2/Sec)

With a cut-off of 954.085, minimum ADC values provides sensitivity and specificity of 87.5% and 85.7%, respectively, in differentiating LGGs from HGGs [Table 2]. With a cut-off of 1.9, maximum rCBV values provide sensitivity and specificity of 100% and 100%, respectively, in differentiating LGGs from HGGs [Table 3].{Table 2}{Table 3}

Magnetic resonance spectroscopy (choline/creatine)

Statistical analysis demonstrated that with a cut-off value of 2, Cho/Cr ratios provide sensitivity and specificity of 100% and 92.9%, respectively, in differentiating LGGs from HGGs [Table 4].{Table 4}

Magnetic resonance spectroscopy (choline/N-acetylaspartate)

Statistical analysis demonstrated that with a cut-off value of 1.45, Cho/NAA ratios provide sensitivity and specificity of 100% and 100%, respectively, in differentiating LGGs from HGGs [Table 5].{Table 5}

On multiparametric MRI study, of 22 patients, 8 patients were assigned as LGG and 14 patients as HGG. On histopathological grading, of 22 patients, 5 patients were assigned as LGG and 17 patients as HGG [Table 6]. Based on a comparison analysis of both MRI multiparametric study and histopathological grading, the following results were available with combined diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of all parameters studied in MRI perfusion and diffusion and MRS [Table 7].{Table 6}{Table 7}

Diagnostic accuracy = (TP + TN)/Total Cases × 100 = 86.4%

Sensitivity = TP/(TP + FN) × 100 = 14/(14 + 3) × 100 = 82.4%

Specificity = TN/(TN + FP) × 100 = 5/(5 + 0) × 100 = 100.0%

PPV = TP/(TP + FP) × 100 = 14/(14 + 0) = 100.0%

NPV = TN/(TN + FN) × 100 = 5/(5 + 3) = 62.5%


  Discussion Top


Our study was a prospective study, which included 22 patients who were referred to our department with presumptive diagnosis of glioma on CT/MRI. We performed multiparametric MRI in these patients using MRI perfusion, DW-MRI and MRS for the purpose of pre-operative grading of gliomas. This pre-operative grading of gliomas was correlated with histopathological grading (reference standard). The age of the patients ranged from 23 to 83 years. The study included 14 males.

The main reason why the segregation of brain tumours into LGGs and HGGs is important is because the treatment modalities and approaches are entirely different for these two disease entities. LGGs are sometimes just followed up on serial MRIs, while HGGs require more aggressive treatment options. Since conventional MRI gives limited information on grading of gliomas, their biological behaviour and prognosis is difficult to predict. Multiparametric MRI tries to fill in this lacuna and is gaining attention for this particular reason.

Apparent diffusion coefficient in gliomas

ADC maps obtained from DWI can provide physiologic information by detecting regional variation in the diffusion of free water within brain tissue. Prior studies have reported mixed results as to the utility of ADC maps in establishing the grade of glioma, with some authors finding a correlation between glioma grade and ADC and other not finding ADC maps that are useful. In our study, the minimum ADC values were found to be quite different between patients with LGGs and HGGs, and the minimum ADC values were consistently lower in HGGs (grade III and IV) than in LGGs (grade II).

A study[2] showed that minimum ADC value correlated well with histologic cellularity and grade of the tumours. The higher ADC values in LGGs are because of the increase in the water content of the interstitial spaces, while the areas of high cellularity lead to an increase in the restriction to the motion of water molecules and hence a decrease in the ADC. In contradiction to this study,[2] other studies[18],[19] did not find a significant difference between ADC values of LGGs and HGGs. In a study[20] on a relatively small population that concluded that HGGs have higher ADC values in the peritumoural oedema than LGGs.

A study[21] defined a threshold ADC value of 98.50 mm2/Sec and obtained a sensitivity of 90% and a specificity of 87.1%. However, in this study, LGGs included both grades I and II gliomas and oligodendrogliomas.

A study[22] showed that ADC difference values helped distinguish grade 2 from grade 3 tumours at a cut-off value of 310 × 10–6 mm2/Sec. They also showed that minimum ADCs optimally helped distinguish grade I from higher-grade tumours at a cut-off value of 1470 × 10–6 mm2/Sec and grade 4 from lower-grade tumours at cut-off value of (1010 × 10–6 mm2/Sec). When tumours were graded using combined minimum ADC and ADC cut-off values mentioned above (two-parameter method), the following predictive values were obtained: grade 1 tumours 73%, grade II tumours 100%, grade 3 tumours 67% and grade IV tumours 91%. The authors[22] summarised that using a combination of minimum ADCs and ADC difference values helps in accurate grading of astrocytic tumours.

In our study, a cut-off ADC value of 954.085 (10–6 mm2/Sec) provided a sensitivity and specificity of 87.5% and 85.7%, respectively, in differentiating LGGs from HGGs. Our results demonstrated the potential diagnostic usefulness of the minimum ADC values to distinguish HGGs from LGGs.

Diagnostic value of magnetic resonance spectroscopic imaging in the non-invasive grading of gliomas

Since MRS provides information about the levels of various cellular metabolites that are relevant for defining tumour burden and also evaluating the aggressiveness of tumours, it has become an increasingly important tool for the diagnostic workup of patients with gliomas.

Liu et al.[23] showed that HGGs had a higher Cho/Cr and Cho/NAA ratios than LGGs. The NAA/Cr ratios were significantly lower in high grade than in LGGs. At a threshold value of 2.01 for Cho/Cr, the sensitivity, specificity, positive predictive and NPVs were 86%, 90%, 95% and 75%, respectively, for differentiating LGGs from HGGs. Threshold cut-off values of 2.49 and 0.97 for Cho/NAA and NAA/Cr, respectively, were useful in differentiating LGGs from HGGs.

A study[24] reported that HGGs demonstrated increased Cho/NAA and increased Cho/Cr ratios. Furthermore, lactate and lipid were predominantly detected in patients with HGGs. A correlation between choline and NAA levels with histopathological proliferation index Ki-67 in a study[25] and the authors[25] concluded that MRS is a helpful method for the detection of aggressive areas within gliomas.

In a study[26] it was reported that the means of maximum Cho/NAA, Cho/Cr and minimum NAA/Cr ratios were 5.90, 4.73 and 0.40, respectively, in HGGs, and 1.65, 1.84 and 1.65, respectively, in LGGs, which were useful in differentiating HGGs from LGGs. In our study, a cut-off value of 2, Cho/Cr ratios provide sensitivity and specificity of 100% and 92.9%, respectively, and with a cut-off value of 1.45, Cho/NAA ratios provide sensitivity and specificity of 100% and 100%, respectively, in differentiating LGG from HGG. Our results demonstrate that the Cho/NAA and Cho/Cr ratios are quite useful to distinguish HGGs from LGGs, with Cho/NAA ratio appearing to have higher specificity than Cho/Cr.

Diagnostic value of perfusion magnetic resonance imaging in the non-invasive grading of gliomas

Gliomas are very a group of heterogeneous tumours.The HGGs are invasive and extremely vascular lesions as they develop neovascularity through neoangiogenesis. They invade through white matter tracts and have a higher proportion of immature and highly permeable vessels. The rCBV maps obtained from DSC MRI can predict areas of malignant transformation or tumour dedifferentiation in at-risk primary LGG before these are picked up on conventional MRI. This information is especially helpful in planning stereotactic biopsies which further help in giving accurate information on tumour grade. It was reported that the mean maximum rCBV in HGGs (6.10 ± 3.98) was higher than in LGG (1.74 ± 0.57) and was useful in differentiating these two.[26]

It has been reported that the sensitivity, specificity, PPV and NPV of 100%, 67%, 87% and 100%, respectively, could be reached by threshold values of 1.33 for rCBV for differentiating high-grade astrocytomas from pilocytic astrocytomas.[27] A threshold of 1.75 for rCBV provides sensitivity, specificity, PPV and NPV of 95%, 57.5%, 87% and 79%, respectively, for determining HGGs.[28] In our study, with a cut-off of 1.9, maximum rCBV values provide sensitivity and specificity of 100% and 100%, respectively, in differentiating LGGs from HGGs. Our results demonstrated that perfusion MRI (rCBV) was the best parameter among DWI and magnetic resonance spectroscopic imaging (MRSI) in differentiating HGGs from LGGs.

On multiparametric MRI study, out of 22 patients, 8 patients were assigned as LGG and 14 patients as HGG. On histopathological grading, out of 22 patients, 5 patients were assigned as LGG and 17 patients as HGG. Based on the comparison, true positives were 14, true negatives were 5, false negatives were 3 and there was no false positive.

In DWI, minimum ADC value inversely correlated with cellularity of tumour, i.e., low value in HGGs and high value in LGGs, so it was useful in the grading of gliomas. In MRS, Cho/Cr and Cho/NAA ratios were associated with grade of tumour in HGGs. In PWI, maximum rCBV values correlated directly with grade of tumour, i.e., high rCBV value favours HGGs and low rCBV means LGGs. In our study, statistical analysis demonstrated that with a cut-off of 1.9, maximum rCBV values provided a sensitivity and specificity of 100% in differentiating LGGs from HGGs. Hence, perfusion MRI (rCBV) was the best parameter among all the multiple parameters (perfusion MRI, DWI and MRSI) in differentiating HGGs from LGGs.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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