Physica Medica xxx (2015) 1e7
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Original paper
Diffusion Tensor Imaging in brain tumors: A study on gliomas and metastases T.S. Papageorgiou a, *, D. Chourmouzi b, A. Drevelengas b, K. Kouskouras c, A. Siountas a a
Medical Physics Laboratory, Department of Medicine, Aristotle University of Thessaloniki, 54124, Greece Department of Radiology, Interbalkan Medical Center, 57001 Thessaloniki, Greece c Laboratory of Radiology, Department of Medicine, Aristotle University of Thessaloniki, 54124, Greece b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 23 December 2014 Received in revised form 3 March 2015 Accepted 19 March 2015 Available online xxx
Purpose: To explore the role of Diffusion Tensor Imaging in preoperative glioma grading, as well as in differentiation between gliomas and metastatic brain tumors. We measured diffusion tensor variables in enhancement and edema regions, which were compared between the different subject groups. Materials and methods: We performed DTI in 48 patients (11 Low Grade Gliomas, 27 High Grade Gliomas, 10 Single Metastatic brain tumors). We measured FA, l1, l2, l3, ADC, Cl, Cp, Cs, RA, and VR in enhancing portions of tumors and edema regions. Additionally, ratios of enhancement to edema values were created for each variable. Results: In peritumoral edema, Cl and RA were proven to be significantly different in pair-wise comparisons, in addition to ADC, Cp, Cs and VR in enhancement regions. Enhancement to edema values were significantly different as well. Conclusion: Diffusion tensor indices could be used for the differentiation between low and high grade gliomas, as well as for distinction between gliomas and metastases. © 2015 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.
Keywords: Diffusion Tensor Imaging Magnetic resonance imaging Glioma grading
Introduction Brain gliomas represent 80% of primary malignant brain tumors [1]. According to World Health Organization classification of brain tumors [2], high grade gliomas constitute a variety of glial tumors of grade 3 and grade 4 pathologies, such as astrocytic high grade gliomas (anaplastic astrocytoma, glioblastoma multiforme), anaplastic oligodendrogliomas, anaplastic oligoastrocytomas and anaplastic ependymomas. In particular, high grade astrocytomas are the most common primary brain malignancies in adults, and the 4th greatest cause of cancer caused death [3]. On the other hand, low grade gliomas consist of a heterogeneous group of glial tumors, representing 15% brain tumors in adults. Low grade astrocytomas tend to affect a younger age group. One in five cancer patients will develop brain metastases [4]. Metastatic tumors appear 10 times more often than primary brain neoplasms. 170,000 new cases are being diagnosed in the United * Corresponding author. Tel.: þ30 6945134670. E-mail addresses: [email protected] (T.S. Papageorgiou), dchourm@hol. gr (D. Chourmouzi), [email protected] (A. Drevelengas), [email protected] (K. Kouskouras), [email protected] (A. Siountas).
States each year [5]. Brain metastases appear in greater numbers in population groups where lung cancer and melanoma have a higher incidence, since they represent oncologic malignancies with high metastatic frequency to the brain [6]. Majority of secondary brain tumors metastasize to the brain from lung and breast primary tumors [7]. Staging of brain gliomas remains a key issue for diagnostic efficacy and treatment planning, as survival rates differ significantly between the two groups. Progression from low to high grade gliomas is characterized by increased cellularity, cellular atypia, and higher mitotic index [8]. Magnetic resonance imaging is considered the most sensitive method for brain tumor diagnosis [9]. Typical brain tumor imaging protocols include a combination of MR pulse sequences [10]. Diffusion MRI is a magnetic resonance imaging modality known for the ability to quantify water diffusivity in tissue structures. Usually, diffusion magnetic resonance imaging is applied as a modified T2 pulse, in which additional diffusion gradients have been attached. As a result, signal is reduced in high diffusivity tissues. Diffusion MR imaging has been recognized as an important technique for differential diagnosis of brain tumors, which has now
http://dx.doi.org/10.1016/j.ejmp.2015.03.010 1120-1797/© 2015 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.
Please cite this article in press as: Papageorgiou TS, et al., Diffusion Tensor Imaging in brain tumors: A study on gliomas and metastases, Physica Medica (2015), http://dx.doi.org/10.1016/j.ejmp.2015.03.010
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Table 1 Selected patient groups. Tumor type
Low grade gliomas
High grade gliomas
Metastases
Number of cases (N) Percentage %
11 (6 males e 5 females) 22.9
27 (14 males e 13 females) 56.3
10 (4 males e 6 females) 20.8
been identified as a standard part of a usual MRI brain imaging protocol [11]. In contrast to traditional diffusion imaging techniques, diffusion tensor imaging can analyze diffusivity in three main directions [12]. Furthermore, diffusion tensor imaging has been reported to hold possible diagnostic utility over brain tumor staging and classification [13]. Directional diffusion indices can be used for calculation of several anisotropic variables, which have been referred as diagnostically effective for staging and classification of brain tumors [14,15]. Thus, diffusion tensor imaging has been described as a promising technique, thereinafter gaining significance in brain tumor imaging protocols [16]. In present study, we aimed to confirm the diagnostic potential of diffusion tensor imaging in the differentiation between high and low grade gliomas, as well as in the distinction between gliomas and metastatic brain tumors. Furthermore, we aimed to prove the ability of diffusion tensor imaging to demonstrate tumor-induced changes caused by infiltration of surrounding tissue. Materials and methods Patients Our study included 48 patients, of which 11 were diagnosed with histologically proven low grade gliomas (age 13e51 mean age 31), 27 patients with confirmed high grade gliomas (age 26e84 mean age 58), and 10 of patients with histologically confirmed single metastases (age 53e74 mean age 65), which were examined with Diffusion Tensor imaging pulse (Table 1). Imaging was performed before any kind of surgical treatment. Research was conducted in accordance with Aristotle University Research Deontology Code. No radiotherapy or chemotherapy was performed prior to scanning. Patients with previously performed biopsy were rejected. Patients were included in the study only if no kind of therapy or interventional procedure was applied. Inclusion criterion was posterior histological confirmation of malignancy. Exclusion criteria included any kind of therapy application prior to scanning. In order to make sure that neural development had been completed, subjects younger than 10 years of age were excluded from the study. Small volumes of interest were not counted in the results in order to avoid partial volume effects. Data acquisition Examination was performed with a General Electric Signa HDxt 3 T scanner. A protocol including several T1 and T2 pulses was used, in addition to T2-FLAIR (Fluid Attenuated Inversion Recovery), Contrast enhanced 3D-T1, Diffusion Weighted Imaging and Diffusion Tensor Imaging. DTI was performed with a Spin EchoeEcho Planar Imaging sequence. A b0 image, along with 25 non-collinear directions of b ¼ 1000 images, were collected. TR (Repetition Time) was 6500 msec, TE (Echo Time) 102 msec, slice thickness 3.2 mm, with a 256 256 matrix and a slice gap of 3.2 mm.
with MRIConvert software (Lewis Center for Neuroimaging, University of Oregon). Nifti formatted images were preprocessed with Statistical Parametric Mapping software (Wellcome Trust Center for Neuroimaging) [17], which is a suit of MATLAB (Mathworks) [18]. We used MATLAB version R2010a. Motion is one of the most common artifacts in Diffusion Tensor Imaging. It occurs mainly due to head movement. Head motion can be classified as a combination of translation and rotation [19]. Rigid body motion correction was applied. Another common artifact is the eddy current artifact [20]. Eddy current artifact occurs due to the rapidly switched magnetic fields applied, which induce currents in the metallic and conductive surfaces of the MRI scanner. Current movement creates additional magnetic fields, which alter the original one. As a result, produced images are unreliable and distorted [21]. Although eddy current artifacts exist in many kinds of MR images, they are exponentially higher in Echo Planar Imaging sequences (commonly used in DTI), since gradients are applied for much longer time compared to conventional MR imaging. Pre-processing pipeline included eddy current correction, which, in addition to motion correction, was performed with ACID (Artefact Correction in Diffusion MRI) toolbox for SPM [22]. Corrected DTI images were exported as Nifti images from SPM. Postprocessing Corrected images were imported to MedINRIA software (ASCLEPIOS Research Project) [23]. Post-contrast 3D-T1 and T2FLAIR images were co-registered to b0 images. Examiner was blind to histological results. Volumes of interest were set in the enhancement region of tumors, excluding necrotic and cystic regions. Another collection of volumes were set in peritumoral edema (Example at Fig. 1). Positioning of volumes of interest was supervised by radiologist with 20 years of experience. Tensor metrics where instantly calculated, and saved as excel data sheets. By applying additional diffusion weighting gradients to a standard T2 pulse sequence, diffusion imaging alters the upcoming pixel intensity by the following equation
I ¼ I0 $eb$ADC
(1)
where I is the diffusion image signal, I0 is the T2 image signal, b the diffusion weighting constant, and ADC the Apparent Diffusion Coefficient. b depends on the diffusion gradients added to T2 pulse sequence, as shown in Fig. 2. Diffusion tensor imaging calculates diffusivity not as a single measurement, but as a 3x3 tensor. After diagonalization, its three main eigenvalues (l1, l2, l3) become the basis of several variables. Diffusion can then be displayed as an ellipsoid, whose 3 main axons correspond to the tensor eigenvalues
Data preprocessing and image analysis Data was exported from scanner workstation and was converted to Nifti (Neuroimaging Informatics Technology Initiative) format
where l1, l2, l3 represent DXX, DYY and DZZ respectively. We used diffusion tensor variables defined by the following equations
Please cite this article in press as: Papageorgiou TS, et al., Diffusion Tensor Imaging in brain tumors: A study on gliomas and metastases, Physica Medica (2015), http://dx.doi.org/10.1016/j.ejmp.2015.03.010
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Figure 1. Post-contrast (3D-T1) axial slice of a patient with Glioblastoma Multiforme (a). A necrotic region is obvious (white arrow). Enhancing and edema VOIs (b,c), pointed by black arrows. ADC map, along with FA and Cp maps of the same slice (d,e,f).
Mean diffusivity
ADC ¼
ðl1 þ l2 þ l3 Þ 3
(3)
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðl1 l2 Þ2 þ ðl2 l3 Þ2 þ ðl1 l3 Þ2 rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Fractional anisotropy FA ¼ ffi 2 l21 þ l22 þ l23 (4) qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðl1 l2 Þ2 þ ðl2 l3 Þ2 þ ðl1 l3 Þ2 pffiffiffi Relative anisotropy RA ¼ ðl1 þ l2 þ l3 Þ 2 (5)
Linear anisotropy
cl ¼
ðl1 l2 Þ ðl1 þ l2 þ l3 Þ
(6)
Planar anisotropy
cp ¼
2ðl2 l3 Þ ðl1 þ l2 þ l3 Þ
(7)
Isotropy
cs ¼
Volume Ratio
3l3 ðl1 þ l2 þ l3 Þ VR ¼
27l1 l2 l3 ðl1 þ l2 þ l3 Þ3
(8)
(9)
Apparent diffusion coefficient gives a general idea of parenchymal diffusion in human tissue. It consists of the mean value of the three diffusion tensor eigenvalues, representing diffusivity in three main directions. Fractional and relative anisotropy are anisotropic variables, with values getting closer to 1 in complete anisotropy (l1 >> l2 z l3 or l1 z l2 >> l3), while getting closer to 0 for spherical diffusion (l1 z l2 z l3). Volume ratio and spherical isotropy follow the exact opposite route. Fractional and relative anisotropy approach 1 in both tubular (l1 >> l2 z l3) and planar (l1 z l2 >> l3) anisotropy, while Cl and Cp express only the first and the second case respectively. Thus, Cl is elevated in cases of single fiber orientation, while Cp is elevated when two fiber orientations exist at the same plane. In human brain, FA and Cl seem to coincide in 70% of brain neural networks, meaning that the majority of human brain axonal structure consists of single orientated fibers [25]. In contrast to FA, Cl and Cp reflect the geometric shape of diffusion ellipsoid [26]. Analysis and statistics
Figure 2. Added diffusion gradients appear as gray rectangles before and after the T2 noted with a D. them is pulse. d represents their duration, while the time gap between g represents their intensity. b is given by equation b ¼ g2 d2 D 13 d g 2 [24].
Statistical analysis was performed with SPSS 17.0. Mean values and Standard Deviation for FA, ADC, l1, l2, l3, Cl, Cp, Cs, RA and VR
Please cite this article in press as: Papageorgiou TS, et al., Diffusion Tensor Imaging in brain tumors: A study on gliomas and metastases, Physica Medica (2015), http://dx.doi.org/10.1016/j.ejmp.2015.03.010
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Tumor Enhancement
Edema
Ratio enhancement to edema
Low grade gliomas
0.1605 0.1507 (0.1051e0.2642) High grade 0.2342 gliomas 0.24 (0.3261e0.381) Metastatic 0.1714 tumors 0.1532 (0.1037e0.2929) Low grade 0.1605 gliomas 0.1507 (0.1051e0.2642) High grade 0.2342 gliomas 0.24 (0.0549e0.3809) Metastatic 0.1714 tumors 0.1532 (0.1037e0.2929) Low grade 0.9329 gliomas 0.4861 (0.3703e2.9685) High grade 1.0855 gliomas 1.0895 (0.1787e2.5675) Metastatic 1.0794 tumors 0.9603 (0.4161e1.6733)
1.6521 1.3444 (0.9021e3.1634) 1.086 1.05 (0.4438e1.6944) 1.5615 1.1487 (0.776e5.9462) 1.6521 1.3444 (0.9021e3.1634) 1.0855 1.05 (0.4438e1.6944) 1.5615 1.1487 (0.776e5.9462) 1.6011 1.4121 (0.4508e3) 0.8565 0.8427 (0.3043e1.6389) 1.1643 0.7787 (0.5132e4.6239)
1.8867 1.5371 (1.031e3.7203) 1.3347 1.319 (1.1980e1.9028) 1.8183 1.3631 (0.8553e6.7337) 1.887 1.5371 (1.031e3.7204) 1.3347 1.319 (0.7048e1.9028) 1.8183 1.3631 (0.8554e6.7337) 1.4178 1.2 (0.4722e2.6061) 0.8357 0.8415 (0.4073e1.3254) 1.1524 0.7465 (0.5347e4.4829)
1.64 1.34 (0.87e3.07) 1.08 1.06 (0.4e1.69) 1.55 1.14 (0.77e5.88) 1.6420 1.3428 (0.874e3.0714) 1.0829 1.0621 (0.4066e1.6915) 1.5484 1.1439 (0.7744e5.8764) 1.6206 1.5273 (0.4407e2.8917) 0.8855 0.8732 (0.2904e1.6332) 1.1505 0.7959 (0.5145e4.4711)
l3 $103 mm sec
2
1.4272 1.1562 (0.8013e2.6984) 0.839 0.8355 (0.2199e1.6062) 1.3179 0.9444 (0.6918e5.2285) 1.4272 1.1562 (0.8013e2.6984) 0.839 0.8355 (0.2199e1.6063) 1.3179 0.9444 (0.6918e5.2285) 1.9684 1.9296 (0.4363e3.8941) 0.868 0.7944 (0.1768e2.2343) 1.206 0.7354 (0.4844e5.0201)
Cl
Cp
Cs
RA
VR
0.0543 0.0492 (0.0277e0.0902) 0.0805 0.0769 (0.0185e0.1284) 0.0612 0.0483 (0.0369e0.1232) 0.0543 0.0492 (0.0278e0.0902) 0.0805 0.0769 (0.0185e0.1428) 0.0612 0.0483 (0.0369e0.1232) 0.8692 0.45 (0.3298e2.6766) 0.9281 0.9033 (0.1832e2.2814) 1.1157 1.0037 (0.3597e2.0431)
0.1037 0.0919 (0.0775e0.1777) 0.1557 0.1631 (0.0341e0.2942) 0.1068 0.1046 (0.0616e0.1651) 0.1037 0.0919 (0.0775e0.1777) 0.1557 0.1631 (0.0341e0.2942) 0.1068 0.1046 (0.0616e0.1651) 1.0458 0.5671 (0.3361e3.5899) 1.4539 1.4667 (0.1633e3.1864) 1.0979 1.1315 (0.4706e1.7720)
0.842 0.8576 (0.7321e0.8909) 0.7453 0.7561 (0.2873e0.9474) 0.8286 0.842 (0.6765e0.9016) 0.842 0.8576 (0.7321e0.8909) 0.7453 0.7561 (0.2873e0.9474) 0.8286 0.842 (0.6765e0.9016) 1.1717 1.2244 (0.7986e1.4486) 0.9624 0.9548 (0.3384e1.3726) 1.002 1.0015 (0.8186e1.2156)
0.1623 0.1424 (0.1068e0.2398) 0.2014 0.2152 (0.0581e0.3391) 0.1454 0.133 (0.078e0.2627) 0.1623 0.1424 (0.1068e0.2398) 0.2014 0.2152 (0.0581e0.2810) 0.1454 0.133 (0.078e0.2627) 0.9357 0.6286 (0.432e2.158) 0.9630 1 (0.2207e1.8565) 0.9281 0.7882 (0.3726e1.6786)
0.0358 0.0361 (0.034e0.0366) 0.0335 0.0345 (0.0146e0.0369) 0.0353 0.0359 (0.031e0.0366) 0.0358 0.0361 (0.034e0.0366) 0.0335 0.0345 (0.0146e0.0369) 0.0353 0.0359 (0.031e0.0366) 1.0806 1.0806 (0.9264e1.279) 0.9718 0.9916 (0.4078e1.1645) 0.9908 0.9987 (0.8683e1.0801)
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Please cite this article in press as: Papageorgiou TS, et al., Diffusion Tensor Imaging in brain tumors: A study on gliomas and metastases, Physica Medica (2015), http://dx.doi.org/10.1016/j.ejmp.2015.03.010
Table 2 Diffusion Tensor mean and median values of different subject groups. Range is given in brackets. 2 2 2 FA ADC $103 mm l1 $103 mm l2 $103 mm sec sec sec
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were calculated for each VOI. Results for each category and patient group were tested under ShapiroeWilk normality test. Differences between pairs of subject groups were calculated using ManneWhitney statistical model in cases where at least one of the groups exhibited non-Gaussian distribution of values. In the reverse case, student t-test was applied. When statistically significant differences were found in pair-wise analysis, Receiver Operating Characteristic (ROC) curves were created. Results Results are shown in Table 2 and Figs. 3 and 4. Pairs of brain tumor types with statistically significant differences are displayed in Table 3, along with differentiating indices. ROC curve results are displayed near the variables. Discussion Lower ADC values in enhancement regions of high grade gliomas have been linked to increased cellularity [11]. Our results indicate statistically significant difference between low and high grade gliomas. Regarding anisotropic indices, previous results have been inconclusive [27e30]. We found that higher planar anisotropy in enhancement regions is correlated with higher cellularity of high grade gliomas. Planar anisotropy is increased, which could represent a significant aspect of tumor cellularity, since planar anisotropy may be interpreted as a measure of fiber crossing. In edema regions, linear anisotropy appears different between distinct tumor types, presenting lower values for high grade gliomas and metastatic tumors, in comparison to low grade gliomas. Additionally, relative anisotropy appears significantly lower in high grade gliomas. Edema zone of high grade gliomas contains invasive tumor cells, due to white matter infiltration, leading to decreased linear and relative anisotropy in nearby space [31]. Low grade gliomas present higher linear anisotropy, probably indicating less deep tumor infiltration than high grade gliomas. Regions with high Cp have been reported in peritumoral regions of patients with several brain tumor types [32]. Wang et al. [33] noticed statistically significant difference in FA, Cl, and Cp values between glioblastomas and metastatic brain tumors in peritumoral edema regions.
Figure 4. Results of FA, Cl, Cp, RA and VR for edema regions.
High grade gliomas seem to present two complementary characteristics. The first one is their higher anisotropic variables in enhancement regions, compared to low grade gliomas, probably occurring as a result of increased cellularity. On the other hand, their edema regions present low anisotropy, since high grade gliomas are highly infiltrative tumors. Consequently, anisotropic variables of high grade gliomas present higher enhancement to edema ratios in comparison to low grade gliomas. On the other hand, isotropy indices, such as Cs, tend to appear with lower enhancement to edema ratio in high grade gliomas. Summarizing data collected from high grade gliomas, anisotropy in their enhancement region is relatively higher than the other two tumor types, mainly because of planar anisotropy increase. Tubular anisotropy doesn't present any statistically significant
Table 3 ROC curve results in variables successful in pair-wise comparisons. ADC, l1, l2, l3 2 are given in 103 mm sec units. Comparison
Figure 3. Results of FA, Cl, Cp, RA and VR for enhancement regions.
Treshold
High grade gliomas vs Low grade Gliomas ADC(enhancement) 1.3220 l1(enhancement) 1.0751 l2(enhancement) 1.3719 l3(enhancement) 1.4252 Cp(enhancement) 0.8663 Cs(enhancement) 0.9996 VR(enhancement) 0.9972 Cl(edema) 0.0659 RA(edema) 0.1807 VR(ratio enhancement to edema) 0.9972 Cs(ratio enhancement to edema) 1.1429 ADC(ratio enhancement to edema) 1.0785 l1(ratio enhancement to edema) 1.0750 l2(ratio enhancement to edema) 1.3719 l3(ratio enhancement to edema) 1.4252 Low grade gliomas vs metastases Cl(edema) 0.0644 VR(ratio enhancement to edema) 1.0408 Low grade gliomas vs metastases Cs(enhancement) 0.8022 Cl(edema) 0.0659 Cl(ratio enhancement to edema) 1.4613
AUC
Sensitivity
Specificity
0.817 0.817 0.817 0.81 0.770 0.77 0.77 0.732 0.707 0.770 0.770 0.817 0.817 0.817 0.810
0.833 0.833 0.833 0.833 0.833 0.833 0.833 0.714 0.714 0.833 0.667 0.833 0.833 0.833 0.833
0.857 0.952 0.952 0.905 0.81 0.714 0.762 0.800 0.800 0.762 0.857 0.810 0.952 0.952 0.905
0.800 0.750
0.875 0.667
0.800 0.900
0.727 0.732 0.686
0.727 0.714 0.545
0.800 0.800 0.800
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difference, indicating similar levels of single-orientated fibers. In edema regions, anisotropy of high grade gliomas seems to be reduced in comparison to low grade gliomas, possibly due to tubular anisotropy changes. Planar anisotropy does not appear statistically significant difference in edema regions between high and low grade gliomas. Low grade gliomas, as noted above, appear to be less cellular than high grade gliomas. In contrast, their enhancement regions do not hold any statistically significant difference in ADC or anisotropy values from enhancement regions of metastatic tumors. This could be interpreted as a result of similar levels of cellularity. On the other hand, edema regions of metastatic tumors and low grade gliomas, appear to have similar levels of Cp. Edema of metastatic tumors seems to be less anisotropic than low grade gliomas, due to reduction of tubular anisotropy (Cl). This could be an indication of reduced single orientation fiber points in low grade glioma edema, in comparison to metastases, although crossing points remain at the same level. Metastatic tumors present different characteristics from low grade gliomas. In their edema areas, they exhibit lower tubular anisotropy, indicating greater fiber displacement. D to their different cellular origins, metastatic tumors are well circumscribed, pushing fibers away instead of infiltrating them. As a result, all kinds of anisotropy variables present lower values than the other two tumor types. Limitations Apart from common limitations affecting retrospective studies, our study suffered from two additional limitations. Firstly, low grade gliomas and metastatic group samples were rather small, limiting our ability to pursue statistically significant differences between diverse subject groups. Additionally, VOI placements may have not been absolutely successful. We placed VOIs in enhancement regions, based on bright tumor areas of 3D-T1 post-contrast scans. Possible edema, necrotic, or cystic regions were excluded with a two-level procedure: High ADC areas, in addition to areas bright in T2-FLAIR images, were excluded from tumor enhancement VOIs. As for edema regions, we placed VOIs on the bright T2-FLAIR areas surrounding tumor VOIs. Though as precise as possible, case of mistaken VOI placement still remains. Conclusion In conclusion, our study confirms the ability of diffusion tensor imaging to help discriminating between gliomas and metastatic brain tumors, as long as motion-induced and eddy current artifacts have been corrected. Additionally, several variables achieved to differentiate high grade from low grade gliomas. References [1] Goodenberger ML, Jenkinse RB. Genetics of adult glioma. Cancer Genet 12 Dec 2012;205:613e21. [2] Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, et al. The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol Aug 2007;114(2):97e109. http://dx.doi.org/10.1007/s00401007-0243-4. [3] Davis FG, Freels S, Grutsch J, Barlas S, Brem S. Survival rates in patients with primary malignant brain tumors stratified by patient age and tumor histological type: an analysis based on Surveillance, Epidemiology, and End Results (SEER) data, 1973e1991. J Neurosurg Jan 1998;88(1):1e10. [4] Dorai Z, Sawaya R, Yung WKA. Brain metastasis. In: Rutka JT, Grossman SA, Westpal M, editors. Neuro-oncology of CNS tumors. Springer; 2006. p. 304e17.
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Twenty-five pitfalls in the analysis of diffusion MRI data. NMR Biomed 2010 Aug;23(7):803e20. http://dx.doi.org/10.1002/ nbm.1543. [22] Mohammadi S, Moller HE, Kugel H, Muller DK, Deppe M. Correcting eddy current and motion effects by affine whole-brain registrations: evaluation of three-dimensional distortions and comparison with slicewise correction. Magn Reson Med 2010 Oct;64(4):1047e56. http://dx.doi.org/10.1002/ mrm.22501. [23] Toussaint N, Souplet JC, Fillard P. MedINRIA: medical image navigation and research tool by INRIA. In: Proc. of MICCAI'07 workshop on interaction in medical image analysis and visualization; 2007. [24] Tofts P. Quantitative MRI of the brain. Measuring changes caused by disease. Chichester: John Wiley and Sons; 2003. [25] New image contrasts from diffusion tensor imaging: theory, meaning, and usefulness of DTI-based image contrast. In: Tournier JD, Mori S, editors. Introduction to diffusion tensor imaging and higher order models. 2nd ed. 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Original paper
Diffusion Tensor Imaging in brain tumors: A study on gliomas and metastases T.S. Papageorgiou a, *, D. Chourmouzi b, A. Drevelengas b, K. Kouskouras c, A. Siountas a a
Medical Physics Laboratory, Department of Medicine, Aristotle University of Thessaloniki, 54124, Greece Department of Radiology, Interbalkan Medical Center, 57001 Thessaloniki, Greece c Laboratory of Radiology, Department of Medicine, Aristotle University of Thessaloniki, 54124, Greece b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 23 December 2014 Received in revised form 3 March 2015 Accepted 19 March 2015 Available online xxx
Purpose: To explore the role of Diffusion Tensor Imaging in preoperative glioma grading, as well as in differentiation between gliomas and metastatic brain tumors. We measured diffusion tensor variables in enhancement and edema regions, which were compared between the different subject groups. Materials and methods: We performed DTI in 48 patients (11 Low Grade Gliomas, 27 High Grade Gliomas, 10 Single Metastatic brain tumors). We measured FA, l1, l2, l3, ADC, Cl, Cp, Cs, RA, and VR in enhancing portions of tumors and edema regions. Additionally, ratios of enhancement to edema values were created for each variable. Results: In peritumoral edema, Cl and RA were proven to be significantly different in pair-wise comparisons, in addition to ADC, Cp, Cs and VR in enhancement regions. Enhancement to edema values were significantly different as well. Conclusion: Diffusion tensor indices could be used for the differentiation between low and high grade gliomas, as well as for distinction between gliomas and metastases. © 2015 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.
Keywords: Diffusion Tensor Imaging Magnetic resonance imaging Glioma grading
Introduction Brain gliomas represent 80% of primary malignant brain tumors [1]. According to World Health Organization classification of brain tumors [2], high grade gliomas constitute a variety of glial tumors of grade 3 and grade 4 pathologies, such as astrocytic high grade gliomas (anaplastic astrocytoma, glioblastoma multiforme), anaplastic oligodendrogliomas, anaplastic oligoastrocytomas and anaplastic ependymomas. In particular, high grade astrocytomas are the most common primary brain malignancies in adults, and the 4th greatest cause of cancer caused death [3]. On the other hand, low grade gliomas consist of a heterogeneous group of glial tumors, representing 15% brain tumors in adults. Low grade astrocytomas tend to affect a younger age group. One in five cancer patients will develop brain metastases [4]. Metastatic tumors appear 10 times more often than primary brain neoplasms. 170,000 new cases are being diagnosed in the United * Corresponding author. Tel.: þ30 6945134670. E-mail addresses: [email protected] (T.S. Papageorgiou), dchourm@hol. gr (D. Chourmouzi), [email protected] (A. Drevelengas), [email protected] (K. Kouskouras), [email protected] (A. Siountas).
States each year [5]. Brain metastases appear in greater numbers in population groups where lung cancer and melanoma have a higher incidence, since they represent oncologic malignancies with high metastatic frequency to the brain [6]. Majority of secondary brain tumors metastasize to the brain from lung and breast primary tumors [7]. Staging of brain gliomas remains a key issue for diagnostic efficacy and treatment planning, as survival rates differ significantly between the two groups. Progression from low to high grade gliomas is characterized by increased cellularity, cellular atypia, and higher mitotic index [8]. Magnetic resonance imaging is considered the most sensitive method for brain tumor diagnosis [9]. Typical brain tumor imaging protocols include a combination of MR pulse sequences [10]. Diffusion MRI is a magnetic resonance imaging modality known for the ability to quantify water diffusivity in tissue structures. Usually, diffusion magnetic resonance imaging is applied as a modified T2 pulse, in which additional diffusion gradients have been attached. As a result, signal is reduced in high diffusivity tissues. Diffusion MR imaging has been recognized as an important technique for differential diagnosis of brain tumors, which has now
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Please cite this article in press as: Papageorgiou TS, et al., Diffusion Tensor Imaging in brain tumors: A study on gliomas and metastases, Physica Medica (2015), http://dx.doi.org/10.1016/j.ejmp.2015.03.010
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Table 1 Selected patient groups. Tumor type
Low grade gliomas
High grade gliomas
Metastases
Number of cases (N) Percentage %
11 (6 males e 5 females) 22.9
27 (14 males e 13 females) 56.3
10 (4 males e 6 females) 20.8
been identified as a standard part of a usual MRI brain imaging protocol [11]. In contrast to traditional diffusion imaging techniques, diffusion tensor imaging can analyze diffusivity in three main directions [12]. Furthermore, diffusion tensor imaging has been reported to hold possible diagnostic utility over brain tumor staging and classification [13]. Directional diffusion indices can be used for calculation of several anisotropic variables, which have been referred as diagnostically effective for staging and classification of brain tumors [14,15]. Thus, diffusion tensor imaging has been described as a promising technique, thereinafter gaining significance in brain tumor imaging protocols [16]. In present study, we aimed to confirm the diagnostic potential of diffusion tensor imaging in the differentiation between high and low grade gliomas, as well as in the distinction between gliomas and metastatic brain tumors. Furthermore, we aimed to prove the ability of diffusion tensor imaging to demonstrate tumor-induced changes caused by infiltration of surrounding tissue. Materials and methods Patients Our study included 48 patients, of which 11 were diagnosed with histologically proven low grade gliomas (age 13e51 mean age 31), 27 patients with confirmed high grade gliomas (age 26e84 mean age 58), and 10 of patients with histologically confirmed single metastases (age 53e74 mean age 65), which were examined with Diffusion Tensor imaging pulse (Table 1). Imaging was performed before any kind of surgical treatment. Research was conducted in accordance with Aristotle University Research Deontology Code. No radiotherapy or chemotherapy was performed prior to scanning. Patients with previously performed biopsy were rejected. Patients were included in the study only if no kind of therapy or interventional procedure was applied. Inclusion criterion was posterior histological confirmation of malignancy. Exclusion criteria included any kind of therapy application prior to scanning. In order to make sure that neural development had been completed, subjects younger than 10 years of age were excluded from the study. Small volumes of interest were not counted in the results in order to avoid partial volume effects. Data acquisition Examination was performed with a General Electric Signa HDxt 3 T scanner. A protocol including several T1 and T2 pulses was used, in addition to T2-FLAIR (Fluid Attenuated Inversion Recovery), Contrast enhanced 3D-T1, Diffusion Weighted Imaging and Diffusion Tensor Imaging. DTI was performed with a Spin EchoeEcho Planar Imaging sequence. A b0 image, along with 25 non-collinear directions of b ¼ 1000 images, were collected. TR (Repetition Time) was 6500 msec, TE (Echo Time) 102 msec, slice thickness 3.2 mm, with a 256 256 matrix and a slice gap of 3.2 mm.
with MRIConvert software (Lewis Center for Neuroimaging, University of Oregon). Nifti formatted images were preprocessed with Statistical Parametric Mapping software (Wellcome Trust Center for Neuroimaging) [17], which is a suit of MATLAB (Mathworks) [18]. We used MATLAB version R2010a. Motion is one of the most common artifacts in Diffusion Tensor Imaging. It occurs mainly due to head movement. Head motion can be classified as a combination of translation and rotation [19]. Rigid body motion correction was applied. Another common artifact is the eddy current artifact [20]. Eddy current artifact occurs due to the rapidly switched magnetic fields applied, which induce currents in the metallic and conductive surfaces of the MRI scanner. Current movement creates additional magnetic fields, which alter the original one. As a result, produced images are unreliable and distorted [21]. Although eddy current artifacts exist in many kinds of MR images, they are exponentially higher in Echo Planar Imaging sequences (commonly used in DTI), since gradients are applied for much longer time compared to conventional MR imaging. Pre-processing pipeline included eddy current correction, which, in addition to motion correction, was performed with ACID (Artefact Correction in Diffusion MRI) toolbox for SPM [22]. Corrected DTI images were exported as Nifti images from SPM. Postprocessing Corrected images were imported to MedINRIA software (ASCLEPIOS Research Project) [23]. Post-contrast 3D-T1 and T2FLAIR images were co-registered to b0 images. Examiner was blind to histological results. Volumes of interest were set in the enhancement region of tumors, excluding necrotic and cystic regions. Another collection of volumes were set in peritumoral edema (Example at Fig. 1). Positioning of volumes of interest was supervised by radiologist with 20 years of experience. Tensor metrics where instantly calculated, and saved as excel data sheets. By applying additional diffusion weighting gradients to a standard T2 pulse sequence, diffusion imaging alters the upcoming pixel intensity by the following equation
I ¼ I0 $eb$ADC
(1)
where I is the diffusion image signal, I0 is the T2 image signal, b the diffusion weighting constant, and ADC the Apparent Diffusion Coefficient. b depends on the diffusion gradients added to T2 pulse sequence, as shown in Fig. 2. Diffusion tensor imaging calculates diffusivity not as a single measurement, but as a 3x3 tensor. After diagonalization, its three main eigenvalues (l1, l2, l3) become the basis of several variables. Diffusion can then be displayed as an ellipsoid, whose 3 main axons correspond to the tensor eigenvalues
Data preprocessing and image analysis Data was exported from scanner workstation and was converted to Nifti (Neuroimaging Informatics Technology Initiative) format
where l1, l2, l3 represent DXX, DYY and DZZ respectively. We used diffusion tensor variables defined by the following equations
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Figure 1. Post-contrast (3D-T1) axial slice of a patient with Glioblastoma Multiforme (a). A necrotic region is obvious (white arrow). Enhancing and edema VOIs (b,c), pointed by black arrows. ADC map, along with FA and Cp maps of the same slice (d,e,f).
Mean diffusivity
ADC ¼
ðl1 þ l2 þ l3 Þ 3
(3)
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðl1 l2 Þ2 þ ðl2 l3 Þ2 þ ðl1 l3 Þ2 rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Fractional anisotropy FA ¼ ffi 2 l21 þ l22 þ l23 (4) qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðl1 l2 Þ2 þ ðl2 l3 Þ2 þ ðl1 l3 Þ2 pffiffiffi Relative anisotropy RA ¼ ðl1 þ l2 þ l3 Þ 2 (5)
Linear anisotropy
cl ¼
ðl1 l2 Þ ðl1 þ l2 þ l3 Þ
(6)
Planar anisotropy
cp ¼
2ðl2 l3 Þ ðl1 þ l2 þ l3 Þ
(7)
Isotropy
cs ¼
Volume Ratio
3l3 ðl1 þ l2 þ l3 Þ VR ¼
27l1 l2 l3 ðl1 þ l2 þ l3 Þ3
(8)
(9)
Apparent diffusion coefficient gives a general idea of parenchymal diffusion in human tissue. It consists of the mean value of the three diffusion tensor eigenvalues, representing diffusivity in three main directions. Fractional and relative anisotropy are anisotropic variables, with values getting closer to 1 in complete anisotropy (l1 >> l2 z l3 or l1 z l2 >> l3), while getting closer to 0 for spherical diffusion (l1 z l2 z l3). Volume ratio and spherical isotropy follow the exact opposite route. Fractional and relative anisotropy approach 1 in both tubular (l1 >> l2 z l3) and planar (l1 z l2 >> l3) anisotropy, while Cl and Cp express only the first and the second case respectively. Thus, Cl is elevated in cases of single fiber orientation, while Cp is elevated when two fiber orientations exist at the same plane. In human brain, FA and Cl seem to coincide in 70% of brain neural networks, meaning that the majority of human brain axonal structure consists of single orientated fibers [25]. In contrast to FA, Cl and Cp reflect the geometric shape of diffusion ellipsoid [26]. Analysis and statistics
Figure 2. Added diffusion gradients appear as gray rectangles before and after the T2 noted with a D. them is pulse. d represents their duration, while the time gap between g represents their intensity. b is given by equation b ¼ g2 d2 D 13 d g 2 [24].
Statistical analysis was performed with SPSS 17.0. Mean values and Standard Deviation for FA, ADC, l1, l2, l3, Cl, Cp, Cs, RA and VR
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Tumor Enhancement
Edema
Ratio enhancement to edema
Low grade gliomas
0.1605 0.1507 (0.1051e0.2642) High grade 0.2342 gliomas 0.24 (0.3261e0.381) Metastatic 0.1714 tumors 0.1532 (0.1037e0.2929) Low grade 0.1605 gliomas 0.1507 (0.1051e0.2642) High grade 0.2342 gliomas 0.24 (0.0549e0.3809) Metastatic 0.1714 tumors 0.1532 (0.1037e0.2929) Low grade 0.9329 gliomas 0.4861 (0.3703e2.9685) High grade 1.0855 gliomas 1.0895 (0.1787e2.5675) Metastatic 1.0794 tumors 0.9603 (0.4161e1.6733)
1.6521 1.3444 (0.9021e3.1634) 1.086 1.05 (0.4438e1.6944) 1.5615 1.1487 (0.776e5.9462) 1.6521 1.3444 (0.9021e3.1634) 1.0855 1.05 (0.4438e1.6944) 1.5615 1.1487 (0.776e5.9462) 1.6011 1.4121 (0.4508e3) 0.8565 0.8427 (0.3043e1.6389) 1.1643 0.7787 (0.5132e4.6239)
1.8867 1.5371 (1.031e3.7203) 1.3347 1.319 (1.1980e1.9028) 1.8183 1.3631 (0.8553e6.7337) 1.887 1.5371 (1.031e3.7204) 1.3347 1.319 (0.7048e1.9028) 1.8183 1.3631 (0.8554e6.7337) 1.4178 1.2 (0.4722e2.6061) 0.8357 0.8415 (0.4073e1.3254) 1.1524 0.7465 (0.5347e4.4829)
1.64 1.34 (0.87e3.07) 1.08 1.06 (0.4e1.69) 1.55 1.14 (0.77e5.88) 1.6420 1.3428 (0.874e3.0714) 1.0829 1.0621 (0.4066e1.6915) 1.5484 1.1439 (0.7744e5.8764) 1.6206 1.5273 (0.4407e2.8917) 0.8855 0.8732 (0.2904e1.6332) 1.1505 0.7959 (0.5145e4.4711)
l3 $103 mm sec
2
1.4272 1.1562 (0.8013e2.6984) 0.839 0.8355 (0.2199e1.6062) 1.3179 0.9444 (0.6918e5.2285) 1.4272 1.1562 (0.8013e2.6984) 0.839 0.8355 (0.2199e1.6063) 1.3179 0.9444 (0.6918e5.2285) 1.9684 1.9296 (0.4363e3.8941) 0.868 0.7944 (0.1768e2.2343) 1.206 0.7354 (0.4844e5.0201)
Cl
Cp
Cs
RA
VR
0.0543 0.0492 (0.0277e0.0902) 0.0805 0.0769 (0.0185e0.1284) 0.0612 0.0483 (0.0369e0.1232) 0.0543 0.0492 (0.0278e0.0902) 0.0805 0.0769 (0.0185e0.1428) 0.0612 0.0483 (0.0369e0.1232) 0.8692 0.45 (0.3298e2.6766) 0.9281 0.9033 (0.1832e2.2814) 1.1157 1.0037 (0.3597e2.0431)
0.1037 0.0919 (0.0775e0.1777) 0.1557 0.1631 (0.0341e0.2942) 0.1068 0.1046 (0.0616e0.1651) 0.1037 0.0919 (0.0775e0.1777) 0.1557 0.1631 (0.0341e0.2942) 0.1068 0.1046 (0.0616e0.1651) 1.0458 0.5671 (0.3361e3.5899) 1.4539 1.4667 (0.1633e3.1864) 1.0979 1.1315 (0.4706e1.7720)
0.842 0.8576 (0.7321e0.8909) 0.7453 0.7561 (0.2873e0.9474) 0.8286 0.842 (0.6765e0.9016) 0.842 0.8576 (0.7321e0.8909) 0.7453 0.7561 (0.2873e0.9474) 0.8286 0.842 (0.6765e0.9016) 1.1717 1.2244 (0.7986e1.4486) 0.9624 0.9548 (0.3384e1.3726) 1.002 1.0015 (0.8186e1.2156)
0.1623 0.1424 (0.1068e0.2398) 0.2014 0.2152 (0.0581e0.3391) 0.1454 0.133 (0.078e0.2627) 0.1623 0.1424 (0.1068e0.2398) 0.2014 0.2152 (0.0581e0.2810) 0.1454 0.133 (0.078e0.2627) 0.9357 0.6286 (0.432e2.158) 0.9630 1 (0.2207e1.8565) 0.9281 0.7882 (0.3726e1.6786)
0.0358 0.0361 (0.034e0.0366) 0.0335 0.0345 (0.0146e0.0369) 0.0353 0.0359 (0.031e0.0366) 0.0358 0.0361 (0.034e0.0366) 0.0335 0.0345 (0.0146e0.0369) 0.0353 0.0359 (0.031e0.0366) 1.0806 1.0806 (0.9264e1.279) 0.9718 0.9916 (0.4078e1.1645) 0.9908 0.9987 (0.8683e1.0801)
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Table 2 Diffusion Tensor mean and median values of different subject groups. Range is given in brackets. 2 2 2 FA ADC $103 mm l1 $103 mm l2 $103 mm sec sec sec
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were calculated for each VOI. Results for each category and patient group were tested under ShapiroeWilk normality test. Differences between pairs of subject groups were calculated using ManneWhitney statistical model in cases where at least one of the groups exhibited non-Gaussian distribution of values. In the reverse case, student t-test was applied. When statistically significant differences were found in pair-wise analysis, Receiver Operating Characteristic (ROC) curves were created. Results Results are shown in Table 2 and Figs. 3 and 4. Pairs of brain tumor types with statistically significant differences are displayed in Table 3, along with differentiating indices. ROC curve results are displayed near the variables. Discussion Lower ADC values in enhancement regions of high grade gliomas have been linked to increased cellularity [11]. Our results indicate statistically significant difference between low and high grade gliomas. Regarding anisotropic indices, previous results have been inconclusive [27e30]. We found that higher planar anisotropy in enhancement regions is correlated with higher cellularity of high grade gliomas. Planar anisotropy is increased, which could represent a significant aspect of tumor cellularity, since planar anisotropy may be interpreted as a measure of fiber crossing. In edema regions, linear anisotropy appears different between distinct tumor types, presenting lower values for high grade gliomas and metastatic tumors, in comparison to low grade gliomas. Additionally, relative anisotropy appears significantly lower in high grade gliomas. Edema zone of high grade gliomas contains invasive tumor cells, due to white matter infiltration, leading to decreased linear and relative anisotropy in nearby space [31]. Low grade gliomas present higher linear anisotropy, probably indicating less deep tumor infiltration than high grade gliomas. Regions with high Cp have been reported in peritumoral regions of patients with several brain tumor types [32]. Wang et al. [33] noticed statistically significant difference in FA, Cl, and Cp values between glioblastomas and metastatic brain tumors in peritumoral edema regions.
Figure 4. Results of FA, Cl, Cp, RA and VR for edema regions.
High grade gliomas seem to present two complementary characteristics. The first one is their higher anisotropic variables in enhancement regions, compared to low grade gliomas, probably occurring as a result of increased cellularity. On the other hand, their edema regions present low anisotropy, since high grade gliomas are highly infiltrative tumors. Consequently, anisotropic variables of high grade gliomas present higher enhancement to edema ratios in comparison to low grade gliomas. On the other hand, isotropy indices, such as Cs, tend to appear with lower enhancement to edema ratio in high grade gliomas. Summarizing data collected from high grade gliomas, anisotropy in their enhancement region is relatively higher than the other two tumor types, mainly because of planar anisotropy increase. Tubular anisotropy doesn't present any statistically significant
Table 3 ROC curve results in variables successful in pair-wise comparisons. ADC, l1, l2, l3 2 are given in 103 mm sec units. Comparison
Figure 3. Results of FA, Cl, Cp, RA and VR for enhancement regions.
Treshold
High grade gliomas vs Low grade Gliomas ADC(enhancement) 1.3220 l1(enhancement) 1.0751 l2(enhancement) 1.3719 l3(enhancement) 1.4252 Cp(enhancement) 0.8663 Cs(enhancement) 0.9996 VR(enhancement) 0.9972 Cl(edema) 0.0659 RA(edema) 0.1807 VR(ratio enhancement to edema) 0.9972 Cs(ratio enhancement to edema) 1.1429 ADC(ratio enhancement to edema) 1.0785 l1(ratio enhancement to edema) 1.0750 l2(ratio enhancement to edema) 1.3719 l3(ratio enhancement to edema) 1.4252 Low grade gliomas vs metastases Cl(edema) 0.0644 VR(ratio enhancement to edema) 1.0408 Low grade gliomas vs metastases Cs(enhancement) 0.8022 Cl(edema) 0.0659 Cl(ratio enhancement to edema) 1.4613
AUC
Sensitivity
Specificity
0.817 0.817 0.817 0.81 0.770 0.77 0.77 0.732 0.707 0.770 0.770 0.817 0.817 0.817 0.810
0.833 0.833 0.833 0.833 0.833 0.833 0.833 0.714 0.714 0.833 0.667 0.833 0.833 0.833 0.833
0.857 0.952 0.952 0.905 0.81 0.714 0.762 0.800 0.800 0.762 0.857 0.810 0.952 0.952 0.905
0.800 0.750
0.875 0.667
0.800 0.900
0.727 0.732 0.686
0.727 0.714 0.545
0.800 0.800 0.800
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T.S. Papageorgiou et al. / Physica Medica xxx (2015) 1e7
difference, indicating similar levels of single-orientated fibers. In edema regions, anisotropy of high grade gliomas seems to be reduced in comparison to low grade gliomas, possibly due to tubular anisotropy changes. Planar anisotropy does not appear statistically significant difference in edema regions between high and low grade gliomas. Low grade gliomas, as noted above, appear to be less cellular than high grade gliomas. In contrast, their enhancement regions do not hold any statistically significant difference in ADC or anisotropy values from enhancement regions of metastatic tumors. This could be interpreted as a result of similar levels of cellularity. On the other hand, edema regions of metastatic tumors and low grade gliomas, appear to have similar levels of Cp. Edema of metastatic tumors seems to be less anisotropic than low grade gliomas, due to reduction of tubular anisotropy (Cl). This could be an indication of reduced single orientation fiber points in low grade glioma edema, in comparison to metastases, although crossing points remain at the same level. Metastatic tumors present different characteristics from low grade gliomas. In their edema areas, they exhibit lower tubular anisotropy, indicating greater fiber displacement. D to their different cellular origins, metastatic tumors are well circumscribed, pushing fibers away instead of infiltrating them. As a result, all kinds of anisotropy variables present lower values than the other two tumor types. Limitations Apart from common limitations affecting retrospective studies, our study suffered from two additional limitations. Firstly, low grade gliomas and metastatic group samples were rather small, limiting our ability to pursue statistically significant differences between diverse subject groups. Additionally, VOI placements may have not been absolutely successful. We placed VOIs in enhancement regions, based on bright tumor areas of 3D-T1 post-contrast scans. Possible edema, necrotic, or cystic regions were excluded with a two-level procedure: High ADC areas, in addition to areas bright in T2-FLAIR images, were excluded from tumor enhancement VOIs. As for edema regions, we placed VOIs on the bright T2-FLAIR areas surrounding tumor VOIs. Though as precise as possible, case of mistaken VOI placement still remains. Conclusion In conclusion, our study confirms the ability of diffusion tensor imaging to help discriminating between gliomas and metastatic brain tumors, as long as motion-induced and eddy current artifacts have been corrected. Additionally, several variables achieved to differentiate high grade from low grade gliomas. References [1] Goodenberger ML, Jenkinse RB. Genetics of adult glioma. Cancer Genet 12 Dec 2012;205:613e21. [2] Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, et al. The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol Aug 2007;114(2):97e109. http://dx.doi.org/10.1007/s00401007-0243-4. [3] Davis FG, Freels S, Grutsch J, Barlas S, Brem S. Survival rates in patients with primary malignant brain tumors stratified by patient age and tumor histological type: an analysis based on Surveillance, Epidemiology, and End Results (SEER) data, 1973e1991. J Neurosurg Jan 1998;88(1):1e10. [4] Dorai Z, Sawaya R, Yung WKA. Brain metastasis. In: Rutka JT, Grossman SA, Westpal M, editors. Neuro-oncology of CNS tumors. Springer; 2006. p. 304e17.
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