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Gliomas: Histopathologic Evaluation of Changes in Directionality and Magnitude of Water Diffusion at Diffusion-Tensor MR Imaging¹

¹ From the Department of Neurosurgery, Neurocenter (A.S., O.G., M.B., C.N.), Departments of Neuropathology (R.B.) and Neurology (T.H.), University of Erlangen-Nuremberg, Erlangen, Germany; Department of Radiology, Landesklinikum St. Poelten, Propst Fuehrer Strasse 4, A-3100 St. Poelten, Austria (A.S., E.S.); and MR Centre of Excellence, Medical University of Vienna, Vienna, Austria (S.G., E.M.). Received June 4, 2005; revision requested August 1; revision received September 2; accepted September 22; final version accepted November 17. A.S. supported by the German Research Society (SFB 603, C9). Address correspondence to E.S. (e-mail: erich.salomonowitz{at}stpoelten.lknoe.at).

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION ADVANCES IN KNOWLEDGE References

 
Purpose: To retrospectively correlate changes in fractional anisotropy (FA) and mean diffusivity in gliomas at diffusion-tensor magnetic resonance (MR) imaging with the degree of tumor cell infiltration determined histologically.

Materials and Methods: The institutional review board required neither ethics committee approval nor patient informed consent for this study. Twenty patients (eight women, 12 men; age range, 18–53 years) with glioma (seven World Health Organization grade II and 13 grade III tumors) underwent diffusion-tensor MR imaging at 1.5 T. Diffusion-tensor data were obtained with an echo-planar imaging sequence with six diffusion directions (b = 1000 sec/mm²), isotropic 1.9-mm voxels, and five averages. FA and mean diffusivity values were calculated from diffusion-tensor data. Coregistration with a three-dimensional MR imaging data set (used for stereotactic brain biopsies) enabled correlation of FA and mean diffusivity values with the histopathologic findings total cell number (CN), tumor CN, and percentage tumor infiltration (TI) by using linear, exponential, and logarithmic models. Student t and Mann-Whitney U tests were performed.

Results: Histopathologic findings of 77 MR image–guided stereotactic biopsies in all 20 patients were correlated with FA and mean diffusivity values at the biopsy locus. For FA and mean diffusivity, a logarithmic model showed strongest correlation with tumor CN and total CN; a linear model showed strongest correlation with percentage TI. For FA there were negative logarithmic (R = –0.802, P < .001) and linear (R = –0.796, P < .001) correlations with tumor CN and percentage TI, respectively. For mean diffusivity there were positive logarithmic (R = 0.557, P < .001) and linear correlations (R = 0.521, P < .001) with tumor CN and percentage TI, respectively. Differences between correlations for FA and mean diffusivity versus tumor CN (P < .001) and percentage TI (P < .001) were significant.

Conclusion: FA is better than mean diffusivity for assessment and delineation of different degrees of pathologic changes (ie, TI) in glioma.

© RSNA, 2006

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION ADVANCES IN KNOWLEDGE References

 
The highly organized structure of brain white matter affects the molecular movement of water. Diffusion is a three-dimensional (3D) process, and the mobility of water in brain tissue is not equal in all directions but is faster parallel to myelinated axonal fibers (1). Magnitude and directionality (anisotropy) of water diffusion therefore reflect the microstructure of white matter tissue. In contrast, the diffusivity in brain gray matter is largely independent of the orientation of the tissue. Hence, for gray matter it is actually sufficient to measure a scalar apparent diffusion coefficient by using conventional diffusion-weighted magnetic resonance (MR) imaging methods (2).

Pathologic processes (eg, tumors, stroke, multiple sclerosis) result in changes in diffusion because of several reasons, such as loss of tissue organization or changes in extracellular space. Cerebral gliomas cause not only disruption or displacement of white matter structures but also widening of fiber bundles due to tumor infiltration (TI) or edema (3–10). Information on white matter involvement and glioma microstructure is essential for neurosurgical planning with the aim of maximum tumor resection and minimum damage to healthy brain. Routine MR imaging methods do not enable these questions to be answered adequately for the care of patients with glioma.

Diffusion-tensor MR imaging enables the macroscopic detection of anisotropic water diffusion due to elongated structures such as white matter bundles (11,12). Diffusion-tensor MR imaging measures differences in the diffusion displacement of water in three dimensions—depending on image position—by employing at least six diffusion gradient directions. The diffusion tensor D for each image voxel is estimated from this series of experiments, which include an MR imaging acquisition performed without diffusion weighting (b = 0 sec/mm²). The magnitude and directionality of water diffusion—referred to as mean diffusivity and fractional anisotropy (FA), respectively—can be calculated by using D. Mean diffusivity is mathematically equivalent to the well-known apparent diffusion coefficient obtained with standard diffusion-weighted imaging methods (2,13).

Several researchers (5,7,10,13,14) have recommended histologic or clinical evaluation of the results of diffusion-tensor MR imaging methods used for the examination of patients with tumors. The purpose of our study was to retrospectively correlate the changes in FA and mean diffusivity in gliomas at diffusion-tensor MR imaging with the degree of tumor cell infiltration determined histologically.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION ADVANCES IN KNOWLEDGE References

 
Patients
Our study included 20 patients (age range, 18–53 years; mean age, 34.2 years ± 8.1 [standard deviation]) with biopsy-proved diagnoses of supratentorial World Health Organization (WHO) grade II (seven patients) or grade III (13 patients) glioma. Eight patients were women (age range, 29–53 years; mean age, 38.3 years ± 7.8), and 12 patients were men (age range, 18–45 years; mean age, 31.5 years ± 7.4). There were no significant differences in age between male and female patients (two-sided Student t test, P = .071). Eighteen patients underwent image-guided stereotactic tumor resection, and two patients underwent stereotactic biopsy. All lesions were confirmed histopathologically by a neuropathologist (R.B., with 6 years of experience). For this retrospective study, the ethics committee of the University of Erlangen-Nuremberg did not require its approval or informed consent. However, signed informed consent was obtained from all patients for imaging and for the surgical procedures performed.

MR Imaging Methods
MR imaging was performed with a 1.5-T clinical whole-body unit (Magnetom Sonata; Siemens, Erlangen, Germany) equipped with the standard head coil. For diffusion-tensor imaging, a diffusion-weighted echo-planar imaging sequence was used with the following parameters: repetition time msec/echo time msec, 9200/86; matrix size, 128 x 128; field of view, 240 x 240 mm; section thickness, 1.9 mm; and bandwidth, 1502 Hz/pixel. Sixty sections were measured with no intersection gap and an isotropic voxel size of 1.9 x 1.9 x 1.9 mm³. Diffusion-gradient encoding in six directions with a b value of 1000 sec/mm² and an additional measurement without diffusion-gradient encoding (b = 0 sec/mm²) were performed.

The sequence design was based on the use of balanced diffusion gradients to minimize eddy current artifacts. For sufficient signal-to-noise ratios, five averages were applied, requiring a total diffusion-tensor imaging data acquisition time of 5 minutes 31 seconds. Immobilization of the patient's head to minimize artifacts due to patient motion was achieved by fixation of the head in a headrest.

Five adhesive skin fiducial marks were placed in a scattered pattern on the head surface for registration to the frameless stereotactic system. To obtain an MR data set for neuronavigation, a 3D anatomic magnetization-prepared rapid-acquisition gradient-echo sequence was performed in a single session 1 day before surgery with the following parameters: 2020/4.38; field of view, 25 x 25 cm; 1-mm isotropic voxels; and 160 sections (15).

Data Processing
Diffusion-tensor imaging data were transferred off-line to a workstation (Inspiron 8200; Dell, Round Rock, Tex) for analysis. Data processing was performed with software (DTI-Studio, version 2.3; H. Jiang, S. Mori; Johns Hopkins University, Baltimore, Md). The diffusion tensor (D) was calculated by one author (A.S., with 4 years of experience with brain MR imaging) for each voxel by using a multivariate linear fitting method (16,17). Diagonalization of the tensor yielded three eigenvalues (₁, ₂, and ₃) and three eigenvectors (17,18). Parametric maps of mean diffusivity (MD) and FA (19) were calculated by using the following equations:

and

Mean diffusivity has the unit square millimeters per second, while FA values are unitless and vary between 0 (isotropic diffusion) and 1 (complete anisotropy). To compare means of mean diffusivity and FA in tumor and contralateral normal-appearing white matter (NAWM), regions of interest were drawn to cover approximately 90% of the lesion and an area equal in size on the contralateral side. Region-of-interest drawing was performed in consensus by a neurosurgeon (O.G., with 12 years of experience in brain tumor imaging) and a radiologist (E.S., with 31 years of neuroradiology experience). The average mean diffusivity and FA values for the lesion and the contralateral NAWM were calculated by averaging them over the regions of interest.

MR Image–guided Stereotactic Biopsy
Before tumor resection, biopsies were performed by one of two neurosurgeons (O.G. or C.N., with 13 and 15 years of neurosurgery experience, respectively) in several regions according to the information from the 3D magnetization-prepared rapid-acquisition gradient-echo examination by using a stereotactic needle tracked by a navigation system (VectorVision Sky; Brain LAB, Heimstetten, Germany). This procedure was performed before the actual tumor resection and thus ensured a minimal interference of brain shift, which would render neuronavigation inaccurate. The coordinates of each biopsy locus were labeled and documented within the 3D MR imaging data set by one of two neurosurgeons (O.G. or C.N.). No perioperative adverse effects were observed during or after biopsy sampling.

Diffusion-tensor imaging data sets were transferred to the planning workstation of the navigation system for retrospective registration to the 3D MR imaging data sets by using image fusion software (VV² Planning, version 1.3; BrainLAB). This automated registration process uses a rigid registration algorithm that applies an intensity-based pyramidal approach by using mutual information (20). Histopathologic findings for each specimen were traced back to the exact voxel positions in the diffusion-tensor imaging data set (by A.S.). To increase statistical accuracy and to account for possible minor matching inaccuracies, the averages of the mean diffusivity and FA values were calculated from a 3 x 3 x 3-voxel cube around the determined voxel position. The volume of the voxel cube thus evaluated was 5.7 x 5.7 x 5.7 mm³.

Histopathologic Evaluation
All glioma specimens were histologically examined by a neuropathologist (R.B.). The tumors were classified according to the WHO guidelines for tumors of the nervous system by using hematoxylin-eosin staining, as well as a standardized panel of immunohistochemical markers (MAP2, GFAP, p53, and Ki67).

The following antibodies were used: glial fibrillary acidic protein (GFAP clone 6F2; DAKO, Glostrup, Denmark), Ki67 epitope (Mib1; Dianova, Hamburg, Germany), and p53 (DAKO). For detection of the microtubule-associated protein MAP2, we used the monoclonal antibody clone C (supplied by Beat Riederer, MD, Lausanne, Switzerland). Commercially available antibodies reacting with either high- or low-molecular-weight isoforms of MAP2 were used (clone HM-2¹⁰, Sigma-Aldrich, Munich, Germany; and MAB364, Chemicon, Hampshire, England). The specificity of MAP2 in human brain tumor samples has been extensively studied with in situ hybridization, Western blotting experiments, and immunohistochemical analysis (21,22).

Quantitative assessment of glial tumor cells and preexisting brain parenchyma was microscopically performed by using analysis software (Soft Imaging System, Leinenfeld-Echterdingen, Germany) at x200 magnification averaged over five 348 x 261-µm subfields. Only MAP2 immunoreactive cells with a distinct nucleus were taken into account. The averaged number of glial tumor cells was defined as mean tumor cell number (CN). The sum of the averaged number of glial tumor cells and preexisting brain parenchymal cells was defined as mean total CN. The ratio of mean tumor CN to mean total CN was defined as percentage TI.

Statistical Analysis
Data were analyzed by one author (A.S.) with statistical software (SPSS, version 12; SPSS, Chicago, Ill). For comparisons of mean FA and mean diffusivity values averaged over tumor regions of interest and contralateral control regions, a two-sided paired Student t test, with significant differences indicated by P < .05, was used for statistical analysis. The differences in mean FA and average mean diffusivity between tumor and contralateral NAWM for each patient were calculated. The means and the 95% confidence intervals (CIs) for these differences were determined. Standard error estimates for sample means were denoted by the standard error of the mean (SEM). Comparisons of diffusion-tensor imaging measurements between groups (patients with WHO grade II glioma and those with WHO grade III glioma) were performed by using the Mann-Whitney U test.

Linear, exponential, and logarithmic regression analyses were performed for correlations of diffusion-tensor imaging metrics (FA and mean diffusivity) with the three histologic parameters (mean tumor CN, mean total CN, and percentage TI). The significance of differences between correlations for FA and mean diffusivity were tested by using a t test (23). Linear regressions were performed to determine intercept points, ranges of intersection determined as the CI, and SEMs at a percentage TI of 0 for FA and mean diffusivity.

Interpretations of the correlation coefficients were made by A.S., O.G., and E.S., with consideration of the specifications for interpretation of correlation coefficients given by Zou et al (24).

	RESULTS

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Mean FA and Average Mean Diffusivity
The mean FA for WHO grade II and III gliomas was 0.179 (standard deviation, 0.046; SEM, 0.010) compared with a mean FA for the equal-sized region of interest in contralateral NAWM of 0.389 (standard deviation, 0.046; SEM, 0.010). The mean difference between FA values in gliomas and contralateral NAWM was –0.210 (95% CI: –0.232, –0.187). Differences were significant (P < .001, t test).

The average mean diffusivity for WHO grade II and III gliomas was 1.557 x 10^–3 mm²/sec (standard deviation, 0.353; SEM, 0.079) compared with an average mean diffusivity for the equal-sized region of interest in contralateral NAWM of 0.826 x 10^–3 mm²/sec (standard deviation, 0.089; SEM, 0.020). The mean difference between mean diffusivity values in gliomas and contralateral NAWM was 0.731 x 10^–3 mm²/sec (95% CI: 0.574, 0.888). These differences were significant (P < .001, t test).

Grade II versus Grade III Glioma
The mean FA for the subgroup of patients with a WHO grade II glioma was 0.196 (standard deviation, 0.032; SEM, 0.012), while that for the patients with a WHO grade III glioma was 0.170 (standard deviation, 0.051; SEM, 0.014). The average mean diffusivity for WHO grade II glioma was 1.504 x 10^–3 mm²/sec (standard deviation, 0.242; SEM, 0.091), and that for WHO grade III glioma was 1.585 x 10^–3 mm²/sec (standard deviation, 0.407; SEM, 0.113). Comparisons of FA and mean diffusivity values between patients with WHO grade II glioma and patients with WHO grade III glioma with the Mann-Whitney U test revealed significant differences only for FA in tumor (P = .046). All other comparisons revealed no significant differences.

Histopathologic Correlation with Stereotactic Biopsy Specimens
In this study, 77 stereotactic biopsy specimens were evaluated histopathologically and correlated with diffusion-tensor imaging metrics (FA and mean diffusivity) by means of coregistration of patient anatomy and diffusion-tensor imaging data by using image fusion by semiautomatic rigid registration (25) (Fig 1).

View larger version (96K): [in this window] [in a new window] [Download PPT slide]   Figure 1a: Histopathologic correlation of diffusion-tensor imaging metrics with stereotactic biopsy specimens. (a–c) Screen shots from frameless stereotactic system of (a) transverse FA map and (b) transverse and (c) coronal reconstructions of fused 3D T1-weighted magnetization-prepared rapid-acquisition gradient-echo (2020/4.38) MR data set. Pink lines show manually segmented tumor border plotted for surgical planning. Yellow dots show sites of stereotactic biopsies 1–6. (d–f) Histopathologic results for biopsy numbers 2 (14% Map2c-positive cells; tumor CN, 16; total CN, 114), 4 (26% Map2c-positive cells; tumor CN, 32; total CN, 122), and 6 (65% Map2c-positive cells; tumor CN, 86; total CN, 133). Biopsies 2 and 4 were classified as from tumor-infiltrated edema or tumor border. Biopsy 6 was classified as from solid tumor. (Histopathologic parameters of d–f are means of five microscopic subfields evaluated for each biopsy.)

View larger version (97K): [in this window] [in a new window] [Download PPT slide]   Figure 1b: Histopathologic correlation of diffusion-tensor imaging metrics with stereotactic biopsy specimens. (a–c) Screen shots from frameless stereotactic system of (a) transverse FA map and (b) transverse and (c) coronal reconstructions of fused 3D T1-weighted magnetization-prepared rapid-acquisition gradient-echo (2020/4.38) MR data set. Pink lines show manually segmented tumor border plotted for surgical planning. Yellow dots show sites of stereotactic biopsies 1–6. (d–f) Histopathologic results for biopsy numbers 2 (14% Map2c-positive cells; tumor CN, 16; total CN, 114), 4 (26% Map2c-positive cells; tumor CN, 32; total CN, 122), and 6 (65% Map2c-positive cells; tumor CN, 86; total CN, 133). Biopsies 2 and 4 were classified as from tumor-infiltrated edema or tumor border. Biopsy 6 was classified as from solid tumor. (Histopathologic parameters of d–f are means of five microscopic subfields evaluated for each biopsy.)

View larger version (78K): [in this window] [in a new window] [Download PPT slide]   Figure 1c: Histopathologic correlation of diffusion-tensor imaging metrics with stereotactic biopsy specimens. (a–c) Screen shots from frameless stereotactic system of (a) transverse FA map and (b) transverse and (c) coronal reconstructions of fused 3D T1-weighted magnetization-prepared rapid-acquisition gradient-echo (2020/4.38) MR data set. Pink lines show manually segmented tumor border plotted for surgical planning. Yellow dots show sites of stereotactic biopsies 1–6. (d–f) Histopathologic results for biopsy numbers 2 (14% Map2c-positive cells; tumor CN, 16; total CN, 114), 4 (26% Map2c-positive cells; tumor CN, 32; total CN, 122), and 6 (65% Map2c-positive cells; tumor CN, 86; total CN, 133). Biopsies 2 and 4 were classified as from tumor-infiltrated edema or tumor border. Biopsy 6 was classified as from solid tumor. (Histopathologic parameters of d–f are means of five microscopic subfields evaluated for each biopsy.)

View larger version (122K): [in this window] [in a new window] [Download PPT slide]   Figure 1d: Histopathologic correlation of diffusion-tensor imaging metrics with stereotactic biopsy specimens. (a–c) Screen shots from frameless stereotactic system of (a) transverse FA map and (b) transverse and (c) coronal reconstructions of fused 3D T1-weighted magnetization-prepared rapid-acquisition gradient-echo (2020/4.38) MR data set. Pink lines show manually segmented tumor border plotted for surgical planning. Yellow dots show sites of stereotactic biopsies 1–6. (d–f) Histopathologic results for biopsy numbers 2 (14% Map2c-positive cells; tumor CN, 16; total CN, 114), 4 (26% Map2c-positive cells; tumor CN, 32; total CN, 122), and 6 (65% Map2c-positive cells; tumor CN, 86; total CN, 133). Biopsies 2 and 4 were classified as from tumor-infiltrated edema or tumor border. Biopsy 6 was classified as from solid tumor. (Histopathologic parameters of d–f are means of five microscopic subfields evaluated for each biopsy.)

View larger version (132K): [in this window] [in a new window] [Download PPT slide]   Figure 1e: Histopathologic correlation of diffusion-tensor imaging metrics with stereotactic biopsy specimens. (a–c) Screen shots from frameless stereotactic system of (a) transverse FA map and (b) transverse and (c) coronal reconstructions of fused 3D T1-weighted magnetization-prepared rapid-acquisition gradient-echo (2020/4.38) MR data set. Pink lines show manually segmented tumor border plotted for surgical planning. Yellow dots show sites of stereotactic biopsies 1–6. (d–f) Histopathologic results for biopsy numbers 2 (14% Map2c-positive cells; tumor CN, 16; total CN, 114), 4 (26% Map2c-positive cells; tumor CN, 32; total CN, 122), and 6 (65% Map2c-positive cells; tumor CN, 86; total CN, 133). Biopsies 2 and 4 were classified as from tumor-infiltrated edema or tumor border. Biopsy 6 was classified as from solid tumor. (Histopathologic parameters of d–f are means of five microscopic subfields evaluated for each biopsy.)

View larger version (149K): [in this window] [in a new window] [Download PPT slide]   Figure 1f: Histopathologic correlation of diffusion-tensor imaging metrics with stereotactic biopsy specimens. (a–c) Screen shots from frameless stereotactic system of (a) transverse FA map and (b) transverse and (c) coronal reconstructions of fused 3D T1-weighted magnetization-prepared rapid-acquisition gradient-echo (2020/4.38) MR data set. Pink lines show manually segmented tumor border plotted for surgical planning. Yellow dots show sites of stereotactic biopsies 1–6. (d–f) Histopathologic results for biopsy numbers 2 (14% Map2c-positive cells; tumor CN, 16; total CN, 114), 4 (26% Map2c-positive cells; tumor CN, 32; total CN, 122), and 6 (65% Map2c-positive cells; tumor CN, 86; total CN, 133). Biopsies 2 and 4 were classified as from tumor-infiltrated edema or tumor border. Biopsy 6 was classified as from solid tumor. (Histopathologic parameters of d–f are means of five microscopic subfields evaluated for each biopsy.)

View larger version (12K): [in this window] [in a new window] [Download PPT slide]   Figure 2a: Scatterplots of FA versus tumor CN from stereotactic biopsies. (a) Linear regression (line) of FA versus tumor CN. Regression analysis resulted in R = –0.669 and P < .001. Linear regression fit is represented by FA = 0.24 – 0.3 x 10–3 x tumor CN. (b) Logarithmic regression (line) of FA versus tumor CN for all biopsies sampled. Regression analysis resulted in R = –0.802 and P < .001. Logarithmic regression fit is represented by FA = 0.37 – 0.04 x ln(tumor CN). Conversion of this formula resulted in tumor CN = 10 405 x e–25xFA.

View larger version (13K): [in this window] [in a new window] [Download PPT slide]   Figure 2b: Scatterplots of FA versus tumor CN from stereotactic biopsies. (a) Linear regression (line) of FA versus tumor CN. Regression analysis resulted in R = –0.669 and P < .001. Linear regression fit is represented by FA = 0.24 – 0.3 x 10–3 x tumor CN. (b) Logarithmic regression (line) of FA versus tumor CN for all biopsies sampled. Regression analysis resulted in R = –0.802 and P < .001. Logarithmic regression fit is represented by FA = 0.37 – 0.04 x ln(tumor CN). Conversion of this formula resulted in tumor CN = 10 405 x e–25xFA.

View larger version (13K): [in this window] [in a new window] [Download PPT slide]   Figure 3a: Scatterplots of mean diffusivity (MD) versus tumor CN from stereotactic biopsies. (a) Linear regression (line) of mean diffusivity versus tumor CN. Regression analysis resulted in R = 0.411 and P < .001. Linear regression fit is represented by mean diffusivity = 1.32 – 1.2 x 10–3 x tumor CN. (b) Logarithmic regression (line) of mean diffusivity versus tumor CN for all biopsies sampled. Regression analysis resulted in R = 0.557 and P < .001. Logarithmic regression fit is represented by mean diffusivity = 0.74 – 0.17 x ln(tumor CN). Conversion of this formula resulted in tumor CN = 77.7 x e–5.9xFA. (Mean diffusivity values are not multiplied by 10–3.)

View larger version (13K): [in this window] [in a new window] [Download PPT slide]   Figure 3b: Scatterplots of mean diffusivity (MD) versus tumor CN from stereotactic biopsies. (a) Linear regression (line) of mean diffusivity versus tumor CN. Regression analysis resulted in R = 0.411 and P < .001. Linear regression fit is represented by mean diffusivity = 1.32 – 1.2 x 10–3 x tumor CN. (b) Logarithmic regression (line) of mean diffusivity versus tumor CN for all biopsies sampled. Regression analysis resulted in R = 0.557 and P < .001. Logarithmic regression fit is represented by mean diffusivity = 0.74 – 0.17 x ln(tumor CN). Conversion of this formula resulted in tumor CN = 77.7 x e–5.9xFA. (Mean diffusivity values are not multiplied by 10–3.)

Regression analyses of FA and mean diffusivity revealed strongly negative and moderately positive linear correlations versus percentage TI (Fig 4), respectively. Intercept values at a percentage TI of 0% were obtained at an FA value of 0.278 (95% CI: 0.258, 0.294; SEM, 0.034) and a mean diffusivity value of 1.172 x 10^–3 mm²/sec (95% CI: 1.021, 1.322; SEM, 0.287), respectively, which might be interpreted as ranges of threshold values for brain tissue that was not infiltrated by tumor and/or peritumoral edema.

View larger version (13K): [in this window] [in a new window] [Download PPT slide]   Figure 4a: Scatterplots of FA and mean diffusivity (MD) versus percentage TI. (a) Linear regression (solid line) of FA versus percentage TI for all biopsies sampled. Regression analysis resulted in R = –0.796 and P < .001. Linear regression fit is represented by FA = 0.28 – 1.5 x 10–3 x percentage TI. (b) Linear regression (solid line) of mean diffusivity versus percentage TI for all biopsies sampled. Regression analysis resulted in R = 0.521 and P < .001. Linear regression fit is represented by mean diffusivity = 1.17 – 5.9 x 10–3 x percentage TI. Dotted lines = 95% CIs.

View larger version (14K): [in this window] [in a new window] [Download PPT slide]   Figure 4b: Scatterplots of FA and mean diffusivity (MD) versus percentage TI. (a) Linear regression (solid line) of FA versus percentage TI for all biopsies sampled. Regression analysis resulted in R = –0.796 and P < .001. Linear regression fit is represented by FA = 0.28 – 1.5 x 10–3 x percentage TI. (b) Linear regression (solid line) of mean diffusivity versus percentage TI for all biopsies sampled. Regression analysis resulted in R = 0.521 and P < .001. Linear regression fit is represented by mean diffusivity = 1.17 – 5.9 x 10–3 x percentage TI. Dotted lines = 95% CIs.

	DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION ADVANCES IN KNOWLEDGE References

 
In a recent study, Provenzale et al (13) investigated the diffusion-weighted and diffusion-tensor imaging characteristics of peritumoral white matter and peritumoral NAWM. They found that apparent diffusion coefficients in glioma and in the peritumoral region of glioma increased significantly compared with the apparent diffusion coefficients in the contralateral NAWM. Regarding FA values, they found a greater decrease in anisotropy in hyperintense areas around high-grade glioma than in hyperintense areas around meningioma and a significant decrease in FA values in NAWM near the hyperintense areas around glioma (relative to contralateral NAWM), whereas no decrease was seen in the transition from NAWM to the hyperintense areas around meningioma. Provenzale et al concluded that diffusion-tensor imaging seems to be unsuited for the differentiation of the peritumoral infiltration zone of gliomas and viable tumor on T2-weighted MR images. Lu and colleagues (14), in a recent publication, report that measurement of the peritumoral mean diffusivity allows differentiation between intra- and extraaxial tumors. By introducing a TI index, they were able to distinguish differences in tumor-infiltrated edema in glioma from purely vasogenic edema, which occurs in extraaxial lesions like meningiomas and metastatic disease.

Mean diffusivity values obtained in our study are in agreement with those in earlier published studies (7,13,14,26) for both tumor area and contralateral NAWM. However, regarding FA values, the results are not unequivocal. Our FA values are in agreement with data published by Provenzale et al (13) and Lu et al (14). Differing values were communicated by Tropine et al (26) (0.123 for tumor; 0.504 for contralateral NAWM) and Sinha et al (7) (0.11 for tumor core; 0.48 for contralateral NAWM). This difference may be attributed to the higher number of glioblastomas in their patient groups.

Our results regarding subgroups of WHO grade II and III gliomas showed only a weakly significant (P = .046) difference for FA in tumor. Owing to the small number of patients with WHO grade II glioma in our study (ie, eight), this result is not representative. Hence, we conclude that diffusion-tensor imaging may not be useful for the preoperative differentiation of WHO grade II and III gliomas. This supports the findings of Provenzale et al regarding low-grade glioma and glioblastoma.

Although numerous publications exist in which the role of diffusion-tensor imaging was investigated for characterization of peritumoral infiltration area and solid tumor, an often suggested investigation of a correlation between diffusion-tensor imaging metrics and histopathologic findings (5,7,10,13,14) has not yet been sufficiently addressed. In two publications by Beppu and colleagues (3,27), an attempt was made to predict cell density and proliferation in glioblastoma through correlation with computed tomography–guided stereotactic samples. Their finding of a linear increase in FA with increasing tumor CN is in contrast to the commonly obtained finding of a decrease in FA from normal to pathologic tissue. The authors did not describe how the correlation of the stereotactic specimens to the diffusion-tensor imaging metrics was established. A reason for the different results may be that the regions of interest chosen for the measurement of the FA values that corresponded to the biopsy site were rather large in the studies by Beppu et al. Also, they investigated a rather heterogeneous patient population in one study and only glioblastoma in the other study while performing only one biopsy per case, which may not yield sufficient information on the pathologic situation.

In our study, the correlation of FA and mean diffusivity versus total CN and tumor CN, respectively, showed a logarithmic relationship between histologic parameters and diffusion properties. For small tumor CNs, the logarithmic regression lines demonstrate the typical course—namely, a strong decrease in FA and an increase in mean diffusivity for low tumor CNs followed by an asymptotic plateauing trend for high tumor CNs.

We hypothesize that these findings reflect the infiltrative invasion mechanism of gliomas: enlargement of the extracellular space but widely preserved normal microstructure of the fiber bundles in the border zone. Increased TI causes a decrease in the extracellular space (eg, edema) and a decrease in directionality of diffusion because of derangement of the microstructures.

The key finding of our study is that the comparisons of mean diffusivity and FA in correlation with histopathologic findings demonstrate that the FA values render a better correlation with all histopathologic parameters (mean tumor CN, mean total CN, and percentage TI). On the other hand, the ranges of interception for FA and mean diffusivity values at a percentage TI of 0% might lead to the interpretation that these values could serve as a threshold for the diagnosis of non–tumor-infiltrated brain tissue and/or peritumoral edema.

Limitations of this study were related to the spatial distortions inherent in the echo-planar imaging sequence, so registration with the standard anatomic image data used for navigation, which is necessary to locate the biopsy position in the diffusion-tensor imaging data, may have caused some inaccuracy. Furthermore, the patient registration error of the navigation system, which is typically on the order of 1–2 mm, also influences this correct spatial correlation. We tried to consider these errors by averaging the FA and mean diffusivity values of a cube measuring 3 x 3 x 3 voxels around the biopsy position.

In conclusion, FA is better than mean diffusivity for assessment and delineation of different degrees of pathologic changes (ie, TI) in glioma.

	ADVANCES IN KNOWLEDGE

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION ADVANCES IN KNOWLEDGE References

 

• Stereotactic correlation of results of glioma histopathologic examination and diffusion-tensor imaging reveals a logarithmic correlation of fractional anisotropy (FA) and mean diffusivity with tumor cell number.
  
• FA shows stronger correlation with histopathologic findings than with mean diffusivity.
  

	FOOTNOTES

 

Abbreviations: CI = confidence interval • CN = cell number • FA = fractional anisotropy • NAWM = normal-appearing white matter • SEM = standard error of the mean • 3D = three-dimensional • TI = tumor infiltration • WHO = World Health Organization

Authors stated no financial relationship to disclose.

Author contributions: Guarantors of integrity of entire study, A.S., O.G., E.S., C.N.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; literature research, O.G., R.B., S.G., E.M., E.S., C.N.; clinical studies, O.G., C.N.; experimental studies, A.S., O.G., R.B., C.N.; statistical analysis, A.S., T.H., E.S.; and manuscript editing, all authors

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TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION ADVANCES IN KNOWLEDGE References

 

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