Apparent diffusion coefficient and fractional anisotropy of newly diagnosed grade II gliomas

Article (PDF Available)inNMR in Biomedicine 22(4):449-55 · May 2009with23 Reads
DOI: 10.1002/nbm.1357 · Source: PubMed
Abstract
Distinguishing between low-grade oligodendrogliomas (ODs) and astrocytomas (AC) is of interest for defining prognosis and stratifying patients to specific treatment regimens. The purpose of this study was to determine if the apparent diffusion coefficient (ADC) and fractional anisotropy (FA) from diffusion imaging can help to differentiate between newly diagnosed grade II OD and AC subtypes and to evaluate the ADC and FA values for the mixed population of oligoastrocytomas (OA). Fifty-three patients with newly diagnosed grade II gliomas were studied using a 1.5T whole body scanner (23 ODs, 16 ACs, and 14 OAs). The imaging protocol included post-gadolinium T1-weighted images, T2-weighted images, and either three and/or six directional diffusion imaging sequence with b = 1000 s/mm(2). Diffusion-weighted images were analyzed using in-house software to calculate maps of ADC and for six directional acquisitions, FA. The intensity values were normalized by values from normal appearing white matter (NAWM) to generate maps of normalized apparent diffusion coefficient (nADC) and normalized fractional anisotropy (nFA). The hyperintense region in the T2 weighted image was defined as the T2All region. A Mann-Whitney rank-sum test was performed on the 25th, median, and 75th nADC and nFA among the three subtypes. Logistic regression was performed to determine how well the nADC and nFA predict subtype. Lesions diagnosed as being OD had significantly lower nADC and significantly higher nFA, compared to AC. The nADC and nFA values individually classified the data with an accuracy of 87%. Combining the two did not enhance the classification. The patients with OA had nADC and nFA values between those of OD and AC. This suggests that ADC and FA may be helpful in directing tissue sampling to the most appropriate regions for taking biopsies in order to make a definitive diagnosis.
2 Figures

Full-text (PDF)

Available from: Susan M Chang
Received: 31 October 2008, Revised: 14 July 2008, Accepted: 4 November 2008, Published online in Wiley InterScience: 2008
Apparent diffusion coefficient and fractional
anisotropy of newly diagnosed grade II
gliomas
y
Inas S. Khayal
a,b
*, Tracy R. McKnight
a,c
, Colleen McGue
b
,
Scott Vandenberg
d
, Kathleen R. Lamborn
e
, Susan M. Chang
e
,
Soonmee Cha
c,e
and Sarah J. Nelson
a,b,f
Distinguishing between low-grade oligodendrogliomas (ODs) and astrocytomas (AC) is of interest for defining
prognosis and stratifying patients to specific treatment regimens. The purpose of this study was to determine if
the apparent diffusion coefficient (ADC) and fractional anisotropy (FA) from diffusion imaging can help to differ-
entiate between newly diagnosed grade II OD and AC subtypes and to evaluate the ADC and FA values for the mixed
population of oligoastrocytomas (OA). Fifty-three patients with newly diagnosed grade II gliomas were studied using
a 1.5T whole body scanner (23 ODs, 16 ACs, and 14 OAs). The imaging protocol included post-gadolinium T1-weighted
images, T2-weighted images, and either three and/or six directional diffusion imaging sequence with b¼1000 s/mm
2
.
Diffusion-weighted images were analyzed using in-house software to calculate maps of ADC and for six directional
acquisitions, FA. The intensity values were normalized by values from normal appearing white matter (NAWM) to
generate maps of normalized apparent diffusion coefficient (nADC) and normalized fractional anisotropy (nFA). The
hyperintense region in the T2 weighted image was defined as the T2All region. A Mann–Whitney rank-sum test was
performed on the 25th, median, and 75th nADC and nFA among the three subtypes. Logistic regression was
performed to determine how well the nADC and nFA predict subtype. Lesions diagnosed as being OD had significantly
lower nADC and significantly higher nFA, compared to AC. The nADC and nFA values individually classified the data with
an accuracy of 87%. Combining the two did not enhance the classification. The patients with OA had nADC and nFAvalues
between those of OD and AC. This suggests that ADC and FA may be helpful in directing tissue sampling to the most
appropriate regions for taking biopsies in order to make a definitive diagnosis. Copyright ß2008 John Wiley & Sons, Ltd.
Keywords: grade 11 gliomas; oligodendroglioma; astrocytoma; oligoastrocytoma; diffusion imaging; apparent diffusion
coefficient; fractional anisotropy
INTRODUCTION
The World Health Organization (WHO) categorizes low-grade
gliomas into grades I and II tumors. Grade II gliomas are
slow-growing malignant tumors that with time can progress to
grades III or IV tumors. The most common histologic subtypes are
the more homogeneous oligodendroglioma (OD) and astro-
cytoma (AC); and the more heterogeneous oligoastrocytoma
(OA) (1). OD and ACs have distinct biological characteristics that
have been shown to influence response to therapy and outcome.
OAs contain a mixture of both OD and AC and it is thought that
their biological behavior depends on the relative amounts of the
OD and AC components. The prognosis for patients with ODs is
significantly better than patients with ACs, averaging survival
times of 10 years or more as compared to 4 years. (2,3). ODs also
tend to be more responsive to chemotherapy (4–7). The current
gold standard for differentiation between the glioma subtypes is
surgical biopsy, which imparts substantial risk to the patient and
is prone to tissue sampling error (8,9). Knowing that gliomas are
heterogeneous tumors, the accuracy of glioma classification and
(www.interscience.wiley.com) DOI:10.1002/nbm.1357
Research Article
*Correspondence to: I. S. Khayal, University of California, San Francisco,
Department of Radiology and Biomedical Imaging, Byers Hall, Suite 303,
MC 2532, 17oo 4th Street, San Francisco, CA 94158-2330, USA.
aI. S. Khayal, T. R. McKnight, S. J. Nelson
UCSF/UCB Joint Graduate Group in Bioengineering, University of California,
San Francisco, CA, USA
bI. S. Khayal, C. McGue, S. J. Nelson
Surbeck Laboratory of Advanced Imaging, Department of Radiology,
University of California, San Francisco, CA, USA
cT. R. McKnight, S. Cha
Department of Radiology, University of California, San Francisco, CA, USA
dS. Vandenberg
Department of Pathology, University of California, San Francisco, CA, USA
eK. R. Lamborn, S. M. Chang, S. Cha
Department of Neurological Surgery, University of California, San Francisco,
CA, USA
fS. J. Nelson
Program in Bioengineering, University of California, San Francisco, CA, USA
y
Presented in part at the 15th Annual Meeting of ISMRM, Berlin, Germany,
2007.
Abbreviations used DWI, diffusion-weighted imaging; ADC, apparent diffu-
sion coefficient; nADC, normalized apparent diffusion coefficient; FA, fractional
anisotropy; nFA, normalized fractional anisotropy; OD, oligodendroglioma;
AC, astrocytoma; OA, oligoastrocytoma.
NMR Biomed. (2008) Copyright ß2008 John Wiley & Sons, Ltd.
1
grading through biopsies is highly dependent on the extent of
sampling, especially since these tumors often do not enhance,
therefore there is no specific biopsy target. Therefore, finding
non-invasive imaging techniques capable of differentiating these
low-grade tumors is critical, especially for tumors that have
subregions with both phenotypes, but thatmay not have otherwise
been diagnosed as such because the tissue samples used for
histological analysis were unable to represent the entire tumor.
Microscopic molecular movement of water in tumor tissue
reflects tissue properties that include varying levels of structural
alterations, tumor cellularity, and vasogenic edema. Diffusion-
weighted magnetic resonance imaging (DWI) uses strong gradients
to probe the structure of biologic tissues at a microscopic level by
measuring the Brownian motion of water molecules, and has
therefore been used for in vivo tissue characterization (10).
Acquiring data with gradients in three directions allow the cal-
culation of the apparent diffusion coefficient (ADC), while acquiring
data with gradients in six or more directions allow the calculation
of the ADC and the fractional anisotropy (FA).
The ADC can be calculated from images with any number of
gradients applied (11). The directional restriction of water
diffusibility can be measured as the FA and has been shown
to correlate to integrity of myelinated fiber tracts (12,13). Previous
studies are discordant with regards to the use of ADC and FA in
distinguishing low-grade ODs and low-grade ACs. Bulakbasi et al.
(14) reported the ADC within the tumoral or peritumoral
regions cannot differentiate low-grade ACs from low-grade ODs
in 33 patients, while Tozer et al. (15) suggested an ADC histogram
analysis as a possible method for predicting low-grade glioma
subtypes in 27 patients.
The goal of this study was to determine if the ADC and FA from
DWI can help differentiate grade II OD and AC subtypes and to
examine the ADC and FA for the mixed OA subtype, in a larger
patient population that had been considered in previous studies.
MATERIALS AND METHODS
Study population
A total of 53 patients with newly diagnosed grade II glioma were
included in this study. Tissue diagnosis was based upon histologic
examination using criteria defined by the WHO. Twenty-three
patients had grade II ODs (9 females, 14 males) and ranged in age
from 21 to 71 years, with a mean of 43 years. Sixteen patients had
grade II ACs (6 females, 10 males) and ranged in age from 22 to
52 years with a mean of 36 years. Fourteen patients had grade II OA
(6 females, 8 males) and ranged in age from 18 to 62 with a mean
of 40 years. Patients provided informed consent as approved by
the Committee on Human Research at our institution.
Conventional MRI
MR exams were performed with a 1.5T GE Signa Echospeed scanner
(GE Healthcare Technologies Milwaukee, WI, USA), using a standard
quadrature head coil. The MRI examination included axial
T1-weighted pre- and post-gadolinium three-dimensional spoiled
gradient echo (SPGR) images (TR ¼34 ms, TE ¼3 ms, slice
thickness ¼1.5 mm, matrix ¼256 192, FOV ¼260 195 mm
2
,
flip angle ¼408) and axial T2-weighted 3D fast spin echo (FSE)
(TR¼4000 ms, TE ¼104 ms, slice thickness ¼1.5 mm,
matrix ¼256 192, FOV ¼260 195 mm
2
). After each examin-
ation, the images were transferred to a SUN Ultra 10 workstation
(Sun Microsystems, CA, USA) for post-processing.
Diffusion-weighted imaging
Patients were scanned with three directional DWI (TR¼10 000ms,
TE ¼110 ms, matrix size ¼256 256, slice thickness ¼5 mm,
b¼1000 s/mm
2
, or six directional diffusion tensor imaging
(TR¼10 000 ms, TE ¼108 ms, matrix size ¼256 256, slice
thickness ¼3 mm, b¼1000 s/mm
2
), or both. Table 1 shows
the number of patients scanned with three, six and both three
and six directional data. DWI was performed before gadolinium
injection in all cases except for eight-patient scans. Some studies
report no difference in normal tissue or lesions (16) while others
report a very small decrease in ADC of 1–1.3% in normal tissue
(17,18) and 3% difference in lesions (17). This difference is
significantly smaller than the observed differences between the
subgroups. The ADC and FA were calculated on a pixel-by-pixel
basis using software developed in-house, based on published
algorithms (19). The ADC and FA maps were registered to
anatomical imaging by rigidly aligning the T2-weighted (b¼0)
diffusion image to the T2-weighted FSE and applying the
transformation to the ADC and FA maps (20), see Figure 1.
Data processing
The FSE and pre-gadolinium SPGR images were aligned to the
post-gadolinium SPGR using software developed in our
laboratory (21). An in-house semi-automated segmentation
method was used to define the T2 hyperintense region
(T2ALL) on the T2-weighted FSE image (22). The T1-weighted
post-Gd SPGR image was checked for any contrast enhancement.
If small regions of contrast enhancement was present (6/53
patients), the non-enhancing region was contoured as
NEL ¼T2ALL CEL. Normalized apparent diffusion coefficient
(nADC) maps were generated by dividing the ADC maps by the
median ADC value within the normal appearing white matter
(NAWM) mask, which was segmented using FAST (FMRIB’s
Automated Segmentation Tool) Software on the T2-weighted FSE
image (23). The same method was applied to the FA maps to
Table 1. Number of diffusion data sets per subtype, separated into only three directional diffusion-weighted imaging, only six
directional diffusion tensor imaging, patients scanned with both three and six directional diffusion imaging and total number of
diffusion imaging data sets per subtype
Low-grade subtype Only three direction DWI Only six direction DTI Both three and six directions Total
Oligodendroglioma 8 4 11 23
Astrocytoma 8 5 3 16
Oligoastrocytoma 6 2 6 14
Total 22 11 20 53
www.interscience.wiley.com/journal/nbm Copyright ß2008 John Wiley & Sons, Ltd. NMR Biomed. (2008)
I. S. KHAYAL ET AL.
2
generate the normalized fractional anisotropy (nFA) maps, see
Figure 1.
Statistical analysis
The ADC values from three versus six directional data sets were
compared using a Wilcoxon signed-rank test and a correlation
coefficient for the median ADC values from the three and six
directional data sets was calculated. These analyses were
performed to verify that the three and six directional data sets
show the same results and therefore could be combined.
A Mann–Whitney rank-sum test was performed on OD, AC, and
OA median, 25th, and 75th percentile ADC, nADC, and nFA values.
Logistic regression analysis was used to estimate the accuracy of
subtyping patients with OD versus AC using nADC and nFA values.
Due to multiple comparisons, the p-value chosen to be significant
is p<0.01.
RESULTS
Twenty patients were scanned with three directional DWI and six
directional DTI. The Wilcoxon signed-rank test for the three
directional and six directional median nADC values showed no
significant difference for the median (p¼0.94), 25th ( p¼0.55), or
75th ( p¼0.60) percentile nADC values within the non-enhancing
region. There was also a very strong correlation between the
three and six directional median ADC (r¼0.97, p<0.001) and
nADC (r¼0.964, p<0.001) values. Therefore, the patients
scanned with three directional DWI and the patients scanned
with the six directional DTI were analyzed together.
The median, 25th, and 75th percentiles were calculated for the
nADC and nFA values, within the NEL. Normalization was
performed in order to combine three and six directional data. The
differences between the groups were maintained whether or not
the normalization was performed. The correlation coefficient
between the ADC and nADC values was r¼0.9725, p<0.0001.
Other studies have suggested ADC varies with age (24) and brain
or tumor volume (25). In this study, the correlation between ADC
and age showed a weak but significant correlation for all patient
data combined (r¼0.315, p<0.022), but no significant
correlation for patients with ODs (r¼0.352, p<0.099), patients
with ACs (r¼0.192, p<0.477), or patients with OAs (r¼0.025,
p<0.932). The correlation between age and ADC values in
NAWM was also assessed for any of the subtypes separately and
gave OD (r¼0.0608, p<0.78), AC (r¼0.337, p<0.20), OA
(r¼0.154, p<0.60) or for all patients combined (r¼0.099,
p<0.48) showing no correlation. The correlation was also
assessed for the FA values in NAWM with OD (r¼0.076, p<0.79),
AC (r¼0.126, p<0.77), OA (r¼0.383, p<0.35) and all patients
combined (r¼0.119, p<0.53) showing no correlation. There was
no correlation between ADC and tumor volume for patients with
ODs (r¼0.01, p<0.96), AC (r¼0.30, p<0.23), OA (r¼0.29,
p<0.32) and all subtypes combined (r¼0.045, p<0.73). The
tumor volume mean standard deviation (std) were 56.6 63.3,
49.7. 39.7, and 47.1 33.6 cc for patients with OD, AC, and OAs.
A Wilcoxon rank-sum test shows no significant difference in
volume between the groups, OD versus AC ( p¼0.7426), OD
versus OA ( p¼0.9127), and AC versus OA ( p¼0.9834).
There was a larger variation in the NAWM values for the FA than
the ADC, with a variation of 25% in FA as compared to 13% in
ADC. The variation in FA likely arose from the difference in noise
from three to six directions and field strengths and the known
variation across patients. Therefore, normalization was more
important for FA values than for ADC values. Notably, although
there was a larger variation in the NAWM FA, there was no
significant difference between subtypes (see Table 2) within the
NAWM.
The median, 25th, and 75th percentiles for nADC and nFA for
the NEL region are shown in Table 2. The median nADC OD values
were significantly lower ( p<0.0001) than the median nADC AC
values, with the OAs falling in the middle range, as shown in
Figure 2a. This also held true for the 25th and 75th percentile
nADC values showing significant differences with p<0.0004 and
p<0.0001, respectively. The median nFA OD values were
significantly higher ( p¼0.005) than the median nFA AC values,
with the OA nFA values falling in the middle range, as shown in
Figure 2b. This also held for the 25th and 75th percentile nFA
values with significant differences of p¼0.004 and p¼0.006,
respectively.
In addition to the median, 25th, and 75th percentiles the
std and the coefficient of variability (cvb) defined here as the
(std/median).
100 were analyzed. The median stds and cvb for
the ADC within the NEL of grade II ODs, ACs, and OAs were 230,
283, 265 10
3
mm
2
/s and 19, 20, 18%, respectively. The OD has
a significantly lower std than that of the AC ( p¼0.015), but when
normalized by the median, there are no significant differences
between any of the subtypes.
The data were also analyzed by summing the nADC histograms
and nFA histograms of each patient per subtype, shown in
Figure 3, along with the NAWM histograms which overlap for
nADC and nFA. The grade II OD nADC values were lower than the
grade II AC, with the grade II OA falling in between. The nFA
values in the grade II ODs were higher than those in the grade II
OAs and ACs. The overlap of the histograms between subtypes
was greater in the nFA than in the nADC.
The data were also analyzed by examining the median nFA and
the median nADC. There appears to be a significant correlation of
all subtypes (r¼0.80, p<0.0001, n¼33), and separately for
patients with OD (r¼0.739, p¼0.0025, n¼14) and AC (r¼0.9227,
p¼0.0011, n¼8) but no correlation for OA (r¼0.2621,
p<0.530, n¼8). High FA values were found to correlate to
Figure 1. Example patient with an astrocytoma: (a) T1-weighted SPGR
(reference image) with T2ALL mask applied, (b) T1-weighted SPGR with
NAWM mask applied, (c) T2-FSE aligned to T1-weighted SPGR from which
the T2ALL mask is segmented, (d) T2 (b¼0) from diffusion imaging
aligned to the T2-weighted images through a transformation, (e) trans-
formation applied to ADC map, and (f) FA map with T2ALL mask applied.
NMR Biomed. (2008) Copyright ß2008 John Wiley & Sons, Ltd. www.interscience.wiley.com/journal/nbm
ADC AND FA OF NEWLY DIAGNOSED LOW-GRADE GLIOMAS
3
low ADC values for all subtypes (r¼0.791, p<00001), and
separately for patients with OD (r¼0.585, p¼0.0172) and AC
(r¼0.876, p¼0.002) but no correlation for OA (r¼0.427,
p¼0.2912). Visual inspection of maps of the 25th, 50th, and 75th
percentiles revealed that the median values typically reflected
the central tumor while the edge of the tumor contained the 25th
percentile nADC and 75th percentile nFA values. Although the
correlation across patients was strong, the correlation within
patients was not as strong. There was a large range of correlation
coefficient values, ranging from 0.12 to 0.81, with a median
correlation of 0.43, 0.49, 0.45, and 0.44 for patients with
ODs, ACs, OAs, and all subtypes, respectively. The within-patient
NAWM correlation coefficients were significantly lower than
those within the tumor, with a median correlation of 0.17,
0.18, 0.23, and 0.18 for patients with ODs, ACs, OAs, and all
subtypes, respectively.
Logistic regression analysis was used to examine the ability of
the nADC and nFA in subtyping patients with OD versus AC. The
most optimal cutoff value for the median nADC was 1.84,
misclassifying 2 of 23 ODs, 3 of 16 ACs with an overall accuracy of
87%. The most optimal cutoff value for the median nFA was 0.41,
misclassifying 1 of 14 ODs, 1 of 9 ACs, with an overall accuracy of
91%. The same two patients misclassified from the nFA analysis
alone were misclassified when both the nADC and nFA were both
included in the analysis.
DISCUSSION
Non-invasive methods for distinguishing between patients who
have grade II AC and ODs are important because they have
different prognostic factors and response to treatment. This study
shows a significant difference in the ADC and FA values between
newly diagnosed patients with grade II OD and AC lesions, while
patients with the heterogeneous grade II OA had values that fell
in between those of ODs and ACs.
The 23 patients with grade II OD and 16 patients with grade II
AC had a significant difference in the ADC and nADC values with
Figure 2. Boxplots of the median (a) nADC values and (b) nFA values within the NEL for patients with grade II oligodendroglioma (OD), astrocytoma (AC),
and oligoastrocytoma (OA).
Table 2. Descriptive statistics for the NAWM ADC values and non-enhancing lesion nADC values for patients with oligoden-
droglioma (OD), astrocytoma (AC), and oligoastrocytoma (OA) subtypes followed by the
p
-value from the Wilcoxon rank-sum test
n
NAWM ADC Non-enhancing lesion nADC
Median 25th Median 75th
OD 23 751 36 1.42 0.18 1.60 0.22 1.75 0.25
AC 16 762 24 1.75 0.26 2.04 0.29 2.29 0.33
OA 14 767 20 1.64 0.13 1.87 0.18 2.07 0.21
OD versus AC 0.484 <0.0003 <0.0001 <0.0001
OD versus OA 0.324 <0.0009 <0.0005 0.0016
AC versus OA 0.480 0.129 0.0586 0.0586
NAWM FA Non-enhancing lesion nFA
nMedian 25th Median 75th
OD 14 513 62 .395 .065 .475 .073 .580 .070
AC 8 525 18 .290 .029 .375 .037 .480 .063
OA 8 518 49 .345 .042 .440 .050 .560 .060
OD versus AC 0.517 0.003 0.0019 0.0012
OD versus OA 0.973 0.109 0.207 0.183
AC versus OA 0.800 0.010 0.0148 0.0379
NAWM ADC, NAWM FA, non-enhancing lesion nADC and non-enhancing lesion nFA values.
www.interscience.wiley.com/journal/nbm Copyright ß2008 John Wiley & Sons, Ltd. NMR Biomed. (2008)
I. S. KHAYAL ET AL.
4
mean std ADC values of 1221 165 and 1562 204
10
3
mm
2
/s, respectively. Tozer et al. (15) was able to show a
significant difference between 9 patients with grade II OD and
15 patients with grade II AC with mean std ADC values of
1360 150 and 1510 250 10
3
mm
2
/s, respectively, using a
histogram analysis of ADC. Although the mean values obtained
were similar, Bulakbasi et al. (14) reported no significant
difference in the ADC peritumoral values between 10 patients
with grade II ODs and 23 patients with grade II ACs (mean std
ADC values of 1360 220 vs. 1560 390 10
3
mm
2
/s). In the
current study, the ADC values for patients with grade II ACs are
similar, while the ADC values for OD were lower. This may reflect a
difference in the histopathologic classification of patients at UCSF
versus other institutions due to variations in practices, as well as
differences in pathologist training.
Although there was a significant difference in the nFA values
for patients with grade II OD and AC, the patients with grade II OA
had values that fell between those of the ODs and ACs. To our
knowledge, this is the first paper to examine the FA values and
find a separation in values for patients with OD and AC. Patients
with OD showed higher FAvalues than patients with AC.This may
be explained by the more disruptive pattern of infiltration of ACs
as compared to perineuronal satelitosis in ODs.
Yamasaki et al. (26) presented the logistic regression to
discriminate between ADC values for OD and AC tumors for
separating 2 patients (one grade II OD and one grade II OA) from
17 patients with grade II AC with 94% accuracy. Tozer et al. (15)
classified 15 grade II ACs and 9 grade II ODs using ADC histograms
with an accuracy of 83%. This study showed 87% accuracy in
separating 23 patients with grade II ODs from 16 patients with
grade II ACs. Patients with grade II OAs have tumor mixtures and
were then expected to have intermediate ADC and FA values
depending on how close they resembled one or the other type of
the more homogeneous subtypes.
The use of FA values to discriminate between OD and AC
tumors has not previously been shown. This study was able to
distinguish between 14 grade II ODs and 8 grade II ACs with 91%
accuracy. Using the same patients (i.e. six directional data set), the
ADC misclassifies the same two patients as the FA. There is a
significant correlation between nFA and nADC, which would
explain why the same patients were misclassified and suggests
that combining the ADC and FA does not enhance the
classification.
Visual inspection indicated that the median ADC and median
FA values were within the center of the tumor, whereas the lower
25th percentile ADC and 75th percentile FA values were typically
at the edge of the tumor. The data shows a similar significant
correlation between nADC and nFA in the periphery. The variation
between the median nADC and median nFA may reflect the
biology of the tumor, since no significant correlation was found
between values within individual patients. This distribution of
values, border and central, was seen in both patients with OD and
AC and was not found to distinguish between them.
We expect the differences in ADC and FA for OD and AC to arise
from biological differences and not from confounding factors.
There was no significant correlation between age and ADC in any
of the subgroups separately, but there was a weak significant
correlation when all of the patients were analyzed together.
Patients with OD tended to be older with lower ADC, while
patients with AC tended to be younger and have higher ADC
values. This weak correlation observed could be due to the diffe-
rence in age and ADC values between the groups. Interestingly,
there was no correlation between NAWM ADC values and age for
any of the group separately or when combined. This suggests
that age is not a confounding factor. Berger et al. (25) suggests
that the biology of large low-grade gliomas is quite different from
smaller tumors. These data suggest no correlation between
tumor volume and ADC values for any of the groups. There were
Figure 3. Sum of (a) nADC histograms and (b) nFA histograms within the NAWM of all patients and the NEL of all the patients with oligodendrogliomas
(OD), astrocytoma (AC), and oligoastrocytoma (OA).
NMR Biomed. (2008) Copyright ß2008 John Wiley & Sons, Ltd. www.interscience.wiley.com/journal/nbm
ADC AND FA OF NEWLY DIAGNOSED LOW-GRADE GLIOMAS
5
no differences between the tumor volumes for patients with
different grade II subtypes. Therefore, as expected the significant
difference cannot be attributed to confounding factors and
therefore, it can be deduced that the variation is due to a
biological difference between the tumors, especially since the
mixed OAs had values in between those of OD and AC.
The pattern of infiltration varies in ACs and ODs. ODs tend to
infiltrate by perineuronal satelitosis (clustering of neoplastic
oligodendrocytes around neurons) (see Figure 4). ODs tend to
have more persistent neurons as seen in pathology, which may
explain the higher FA values. By sparing the neurons and less
volume of invasion, one would speculate that less edema is
expected, which may explain the lower ADC values in ODs.
Another biological effect that may enhance the separation
between OD and AC in ADC and FA is calcification. Calcification
has been reported in 20–91% of patients (27–29), while
micro-calcifications are seen in 90% of tumors (30). Patients
with calcification are expected to have lower ADC values due to
the expected lack of water movement in the calcified region. The
previous studies do not report the number of patients with
observed calcification. In this study, calcification was present in at
least 40% of the patients with OD. The patients with calcification
tended to have the lower nADC values.
ADC has been suggested to correlate to cell density in a mixed
population of patients with gliomas (31,32), but it is not entirely
clear how much of a role it has in this analysis. Cell density has not
been shown to have a significant difference between OD and AC.
But, it is expected that the same cell density in these groups
would show a difference in FA and ADC because of the difference
in the infiltration patterns (see Figure 4).
Histopathology of the 23 patients with grade II OD and
16 patients with grade II AC were reviewed by a single pathologist
in a systematic method to analyze the biological features of the
available tissue. Of the 23 patients with grade II OD, 4 patients
were determined to also have some features of AC; of the
16 patients with AC, 6 patients were determined to have some
component of OD and of the 14 patients with grade II OA,
1 patient was determined to be an AC. The original patient
classification based on nADC is shown in Figure 5 and the
adjusted classification is shown in Figure 5. Most of the patients
overlapping in the nADC values were re-classified to OAs. It is
important to mention that some of these patients with mixed
features had different focal areas exhibiting features of AC and
OD. This would lead us to believe that low-grade gliomas are
more heterogeneous than once believed and that biopsy location
is an important aspect of accurate classification.
Diffusion parameters can be used to limit the sampling errors
of biopsies and therefore image-guided biopsies of the higher
and lower values of the tumor may help sample the more
definitive regions, especially when trying to determine a mixed
OA from the more homogeneous OD and AC.
In conclusion, this study suggests a significant difference in
ADC and FA values between patients with grade II OD and AC
subtypes. Therefore, nADC and nFA from diffusion tensor imaging
could be utilized as a non-invasive biomarker for subtyping low
grades.
Figure 4. H&E staining for patient with (a) grade II oligodendroglioma, which tend to have an infiltration pattern of perineuronal satelitosis and more
persistent neurons as shown by the arrows, whereas patients with (b) grade II astrocytoma tend to havea diffuse infiltration pattern. This may help explain
the lower ADC and higher FA values in oligodendrogliomas and higher ADC and lower FA values in astrocytomas.
Figure 5. (a) The original patient classification based on nADC and
(b) the adjusted classification based on re-evaluation of histopathology
from a single pathologist. Most of the patients that were overlapping were
re-classified as patients with oligoastrocytomas. This figure is available in
color online at www.interscience.wiley.com/journal/nbm
www.interscience.wiley.com/journal/nbm Copyright ß2008 John Wiley & Sons, Ltd. NMR Biomed. (2008)
I. S. KHAYAL ET AL.
6
Acknowledgements
The authors thank Niles Bruce and Bert Jimenez of the Department
of Radiology at UCSF for their assistance with data acquisition.
This work was supported by grants from the National Institutes of
Health (P50 CA97297), UC Discovery Grants (LSIT01-10107 and
ITL-Bio 04-10148) sponsored jointly with GE Healthcare and the
UCB Graduate Opportunity Program Fellowship.
REFERENCES
1. Lopes MB, Laws ER, Jr. Low-grade central nervous system tumors.
Neurosurg. Focus 2002; 12: E1.
2. Olson JD, Riedel E, DeAngelis LM. Long-term outcome of low-grade
oligodendroglioma and mixed glioma. Neurology 2000; 54:
1442–1448.
3. Shaw EG, Scheithauer BW, O’Fallon JR, Tazelaar HD, Davis DH. Oligo-
dendrogliomas: the Mayo Clinic experience. J. Neurosurg. 1992; 76:
428–434.
4. Fortin D, Macdonald DR, Stitt L, Cairncross JG. PCV for oligodendro-
glial tumors: in search of prognostic factors for response and survival.
Can. J. Neurol. Sci. 2001; 28: 215–223.
5. Glass J, Hochberg FH, Gruber ML, Louis DN, Smith D, Rattner B. The
treatment of oligodendrogliomas and mixed oligodendroglio-
ma-astrocytomas with PCV chemotherapy. J. Neurosurg. 1992; 76:
741–745.
6. Kitange GJ, Smith JS, Jenkins RB. Genetic alterations and chemother-
apeutic response in human diffuse gliomas. Expert Rev. Anticancer
Ther. 2001; 1: 595–605.
7. Mason WP, Krol GS, DeAngelis LM. Low-grade oligodendroglioma
responds to chemotherapy. Neurology 1996; 46: 203–207.
8. Sasaki H, Zlatescu MC, Betensky RA, Johnk LB, Cutone AN, Cairncross
JG, Louis DN. Histopathological-molecular genetic correlations in
referral pathologist-diagnosed low-grade ‘‘oligodendroglioma’’.
J. Neuropathol. Exp. Neurol. 2002; 61: 58–63.
9. van den Bent MJ. New perspectives for the diagnosis and treatment of
oligodendroglioma. Expert Rev. Anticancer Ther. 2001; 1: 348–356.
10. Le Bihan DJ. Differentiation of benign versus pathologic compression
fractures with diffusion-weighted MR imaging: a closer step toward
the ‘‘holy grail’’ of tissue characterization? Radiology 1998; 207:
305–307.
11. Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet
M. MR imaging of intravoxel incoherent motions: application to
diffusion and perfusion in neurologic disorders. Radiology 1986;
161: 401–407.
12. Chenevert TL, Brunberg JA, Pipe JG. Anisotropic diffusion in human
white matter: demonstration with MR techniques in vivo. Radiology
1990; 177: 401–405.
13. Hansen JR. Pulsed NMR study of water mobility in muscle and brain
tissue. Biochim. Biophys. Acta 1971; 230: 482–486.
14. Bulakbasi N, Guvenc I, Onguru O, Erdogan E, Tayfun C, Ucoz T. The
added value of the apparent diffusion coefficient calculation to
magnetic resonance imaging in the differentiation and grading of
malignant brain tumors. J. Comput. Assist. Tomogr. 2004; 28: 735–746.
15. Tozer DJ, Jager HR, Danchaivijitr N, Benton CE, Tofts PS, Rees JH,
Waldman AD. Apparent diffusion coefficient histograms may predict
low-grade glioma subtype. NMR Biomed. 2006.
16. Fitzek C, Mentzel HJ, Fitzek S, Sauner D, Kaiser WA, Reichenbach JR.
Echoplanar diffusion-weighted MRI with intravenous gadoli-
nium-DTPA. Neuroradiology 2003; 45: 592–597.
17. Fırat A, S¸anlı B, Karakas¸H, Erdem G. The effect of intravenous
gadolinium-DTpa on diffusion-weighted imaging. Neuroradiology
2006; 48: 465–470.
18. Yamada K, Kubota H, Kizu O, Nakamura H, Ito H, Yuen S, Tanaka O,
Kubota T, Makino M, Van Cauteren M, Nishimura T. Effect of intrave-
neous gadolinium-DTPA on diffusion-weighted images evaluation of
normal brain and infarcts. Stroke 2002; 33: 1799–1802.
19. Basser PJ. Pierpaoli C. Microstructural and physiological features of
tissues elucidated by quantitative-diffusion-tensor MRI. J. Magn.
Reson. B 1996; 111: 209–219.
20. Hartkens T, Rueckert D, Schnabel JA, Hawkes DJ, Hill DLG. VTK CISG
registration toolkit: An open source software package for affine and
non-rigid registration of single- and multimodal 3D images. BVM 2002,
Leipzig, Springer-Verlag, 2002.
21. Nelson SJ, Nalbandian AB, Proctor E, Vigneron DB. Registration of
images from sequential MR studies of the brain. J. Magn. Reson.
Imaging 1994; 4: 877–883.
22. Saraswathy S. Semi-automated segmentation of brain tumor lesions
in MR images. International Society of Magnetic Resonance Imaging,
Seattle, WA, USA, 2006.
23. Zhang Y, Brady M, Smith S. Segmentation of brain MR images through
a hidden Markov random field model and the expectation-
maximization algorithm. IEEE Trans. Med. Imaging 2001; 20: 45–57.
24. Gideon P, Thomsen C, Henriksen O. Increased self-diffusion of brain
water in normal aging. J. Magn. Reson. Imaging 1994; 4: 185–188.
25. Berger MS, Deliganis AV, Dobbins J, Keles GE. The effect of extent of
resection on recurrence in patients with low grade cerebral hemi-
sphere gliomas. Cancer 1994; 74: 1784–1791.
26. Yamasaki F, Kurisu K, Satoh K, Arita K, Sugiyama K, Ohtaki M, Takaba J,
Tominaga A, Hanaya R, Yoshioka H, Hama S, Ito Y, Kajiwara Y, Yahara K,
Saito T, Thohar MA. Apparent diffusion coefficient of human brain
tumors at MR imaging. Radiology 2005; 235: 985–991.
27. Pirzkall A, Nelson SJ, McKnight TR, Takahashi MM, Li X, Graves EE,
Verhey LJ, Wara WW, Larson DA, Sneed PK. Metabolic imaging of
low-grade gliomas with three-dimensional magnetic resonance spec-
troscopy. Int. J. Radiat. Oncol. Biol. Phys. 2002; 53: 1254–1264.
28. Lee YY, Van Tassel P. Intracranial oligodendrogliomas: imaging find-
ings in 35 untreated cases. AJR Am. J. Roentgenol. 1989; 152: 361–369.
29. Vonofakos D, Marcu H, Hacker H. Oligodendrogliomas: CT patterns
with emphasis on features indicating malignancy. J. Comput. Assist.
Tomogr. 1979; 3: 783–788.
30. Burger P, Scheithauer B, Vogel F. 2002; Surgical Pathology of the
Nervous System and Its Coverings. Churchill Livingstone: New York, NY.
31. Sugahara T, Korogi Y, Kochi M, Ikushima I, Shigematu Y, Hirai T,
Okuda T, Liang L, Ge Y, Komohara Y, Ushio Y, Takahashi M. Usefulness
of diffusion-weighted MRI with echo-planar technique in the evalu-
ation of cellularity in gliomas. J. Magn. Reson. Imaging 1999; 9: 53–60.
32. Gupta RK, Cloughesy TF, Sinha U, Garakian J, Lazareff J, Rubino G,
Rubino L, Becker DP, Vinters HV, Alger JR. Relationships between
choline magnetic resonance spectroscopy, apparent diffusion coeffi-
cient and quantitative histopathology in human glioma.
J. Neurooncol. 2000; 50: 215–226.
NMR Biomed. (2008) Copyright ß2008 John Wiley & Sons, Ltd. www.interscience.wiley.com/journal/nbm
ADC AND FA OF NEWLY DIAGNOSED LOW-GRADE GLIOMAS
7
    • In the central enhancing region, the median (range) normalized ADC and FA were 0.46 (0.16 - 1.02) and 1.40 (0.29 -2.50), respectively. Another study [22] noted similar trends in normalized FA and MD values for Grade II oligodendrogliomas, astrocytomas, and oligoastrocytomas. Figure 4Ashows an unexpected finding.
    [Show abstract] [Hide abstract] ABSTRACT: Introduction Determining the full extent of gliomas during radiotherapy planning can be challenging with conventional T1 and T2 magnetic resonance imaging (MRI). The purpose of this study was to develop a method to automatically calculate differences in the fractional anisotropy (FA) and mean diffusivity (MD) values in target volumes obtained with diffusion tensor imaging (DTI) by comparing with values from anatomically homologous voxels on the contralateral side of the brain. Methods Seven patients with a histologically confirmed glioma underwent postoperative radiotherapy planning with 1.5 T MRI and computed tomography. DTI was acquired using echo planar imaging for 20 noncolinear directions with b = 1000 s/mm2 and one additional image with b = 0, repeated four times for signal averaging. The distribution of FA and MD was calculated in the gross tumor volume (GTV), shells 0-5 mm, 5-10 mm, 10-15 mm, 15-20 mm, and 20-25 mm outside the GTV, and the GTV mirrored in the left-right direction (mirGTV). All images were aligned to a template image, and FA and MD interhemispheric difference images were calculated. The difference in mean FA and MD between the regions of interest was statistically tested using two-sided paired t-tests with α = 0.05. Results The mean FA in mirGTV was 0.20 ± 0.04, which was larger than the FA in the GTV (0.12 ± 0.03) and shells 0-5 mm (0.15 ± 0.03) and 5-10 mm (0.17 ± 0.03) outside the GTV. The mean MD (×10-3 mm2/s) in mirGTV was 0.93 ± 0.09, which was smaller than the MD in the GTV (1.48 ± 0.19) and the peritumoral shells. The distribution of FA and MD interhemispheric differences followed the same trends as FA and MD values. Conclusions This study successfully implemented a method for calculation of FA and MD differences by comparison of voxel values with anatomically homologous voxels on the contralateral side of the brain. Further research is warranted to determine if radiotherapy planning using these images can be used to improve target delineation.
    Full-text · Article · Oct 2016
    • Although conventional sequences allow excellent visualization of these tumors, they still have limitations as regards precise definition of lesion boundaries and the monitoring of treatment-related changes and tumor recurrence . Advanced MRI techniques such as diffusionweighted imaging, perfusion-weighted imaging, and MR spectroscopy provide a better estimation of tumor extension and potentially higher accuracy in tumor grading [23, 24]. PET can provide important biochemical and metabolic information in addition to the morphological, functional, and metabolic information obtainable by MRI.
    [Show abstract] [Hide abstract] ABSTRACT: Hybrid PET/MRI is a recently developed technique, which is attracting growing interest among the medical community owing to its potential clinical and research applications. PET/MRI is of special interest for neuroscience, given that PET and MRI are the neuroimaging methods of choice for many clinical and scientific applications. The first clinical studies conducted have tested the performance of PET/MRI in oncology indications, neurodegenerative disorders and epilepsy, using aminoacidic tracers and somatostatin-receptor imaging (68Ga-DOTATOC), 18F-fluorodeoxyglucose (18F-FDG), and 11C-flumazenil, respectively, and shown that the acquisition of both brain PET and MRI can be performed in a single session on a hybrid PET/MRI system achieving quality comparable to that of PET/CT and MRI acquired separately, but with some important advantages. Combined acquisition of PET and MRI maximizes the clinical information and optimizes the registration of both modalities, while minimizing patient discomfort. Sparing the patient the CT scan can reduce radiation exposure while the accurate coregistration due to the identical positioning opens new windows for better region-specific evaluation of PET data, particularly when acquired with tracers that provide little anatomical information. PET and MRI parameters can be systematically combined for diagnostic interpretation and new options concerning partial volume and motion correction can be exploited. There are still open issues, such as the selection of clinical indications, the influence of the combined design on the performance of each modality, and, in particular, the validation of quantitative PET measures with respect to attenuation correction. Overall, PET/MRI hybrid imaging is an exciting new modality and potentially the future modality of choice for neuroimaging investigations.
    Full-text · Article · Feb 2013
    • Most imaging studies of brain tumors have focused on differentiating among tumor grades and assess prognosis, while imaging markers of specific histology types are scarce and poorly validated. Recent studies using diffusion weighted imaging reported significant differences in apparent diffusion coefficient and fractional anisotropy values as well as histograms in low-grade astrocytomas vs. oligodendrogliomas232425. Increased amino acid tumor tissue uptake, as compared to normal brain tissue, has been reported in low-grade gliomas by 11 C-methionine PET [26], with relatively higher concentration values in oligodendrogliomas272829.
    [Show abstract] [Hide abstract] ABSTRACT: Increased tryptophan metabolism via the kynurenine pathway is a major mechanism of tumor immuno-resistance. α-[(11)C]Methyl-L: -tryptophan (AMT) is a positron emission tomography (PET) tracer for tryptophan catabolism, and increased AMT uptake has been demonstrated in brain tumors. In this study we evaluated the use of AMT PET for detection of low-grade gliomas and glioneuronal tumors, and determined if kinetic parameters of AMT uptake can differentiate among tumor types. AMT PET images were obtained in 23 patients with newly diagnosed low-grade brain tumors (WHO grade II gliomas and WHO grade I dysembryoplastic neuroepithelial tumors [DNETs]). Kinetic variables, including the unidirectional uptake rate (K-complex) and volume of distribution (VD; which characterizes tracer transport), were measured using a graphical approach from tumor dynamic PET and blood-input data, and metabolic rates ([Formula: see text]) were also calculated. These values as well as tumor/cortex ratios were compared across tumor types. AMT PET showed increased tumor/cortex K-complex (n = 16) and/or VD ratios (n = 15) in 21/23 patients (91%), including 11/13 tumors with no gadolinium enhancement on MRI. No increases in AMT were seen in an oligodendroglioma and a DNET. Astrocytomas and oligoastrocytomas showed higher [Formula: see text] tumor/cortex ratios (1.66 ± 0.46) than oligodendrogliomas (0.96 ± 0.21; P = 0.001) and DNETs (0.75 ± 0.39; P < 0.001). These results demonstrate that AMT PET identifies most low-grade gliomas and DNETs by high uptake, even if these tumors are not contrast-enhancing on MRI. Kinetic analysis of AMT uptake shows significantly higher tumor/cortex tryptophan metabolic ratios in astrocytomas and oligoastrocytomas in comparison with oligodendrogliomas and DNETs.
    Full-text · Article · May 2011
    • We demonstrate that OPC-like human oligodendroglioma cells from both 1p/19q and EGFR-driven tumors were highly tumorigenic. MRI analyses from patients with grade II–III gliomas demonstrated that oligodendrogliomas and astrocytomas could be subclassified based on MRI diffusion patterns (Khayal et al., 2009) and association with the lateral ventricles, a NSC-rich region. Interestingly , all 1p/19q deleted tumors arose in WM regions, while two EGFR-driven p53 mutant human tumors were associated with the lateral ventricles.
    [Show abstract] [Hide abstract] ABSTRACT: Malignant astrocytic brain tumors are among the most lethal cancers. Quiescent and therapy-resistant neural stem cell (NSC)-like cells in astrocytomas are likely to contribute to poor outcome. Malignant oligodendroglial brain tumors, in contrast, are therapy sensitive. Using magnetic resonance imaging (MRI) and detailed developmental analyses, we demonstrated that murine oligodendroglioma cells show characteristics of oligodendrocyte progenitor cells (OPCs) and are therapy sensitive, and that OPC rather than NSC markers enriched for tumor formation. MRI of human oligodendroglioma also suggested a white matter (WM) origin, with markers for OPCs rather than NSCs similarly enriching for tumor formation. Our results suggest that oligodendroglioma cells show hallmarks of OPCs, and that a progenitor rather than a NSC origin underlies improved prognosis in patients with this tumor.
    Full-text · Article · Dec 2010
    • Fifty-six patients with newly diagnosed grade 2 glioma according to the World Health Organization who had been scanned 1 day before surgery were selected for this study, including 24 with oligodendrogliomas (10 females and 14 males; median age, 43 years; range, 21- 71 years), 18 with astrocytomas (7 females and 11 males; median age, 33.5 years; range, 22-52 years), and 14 with oligoastrocytomas (5 females and 9 males; median age, 45 years; range, 18-62 years). The ADC values for a subset of this patient population had been reported in a previous study [20], but the correlation of these data with other imaging parameters had not been considered. Informed consent to participate in the study was obtained using a protocol that had been reviewed and approved by the committee on human research at our institution.
    [Show abstract] [Hide abstract] ABSTRACT: The purpose of this study was to derive quantitative parameters from magnetic resonance (MR) spectroscopic, perfusion, and diffusion imaging of grade 2 gliomas according to the World Health Organization and to investigate how these multiple imaging modalities can contribute to evaluating their histologic subtypes and spatial characteristics. MR spectroscopic, perfusion, and diffusion images from 56 patients with newly diagnosed grade 2 glioma (24 oligodendrogliomas, 18 astrocytomas, and 14 oligoastrocytomas) were retrospectively studied. Metabolite intensities, relative cerebral blood volume (rCBV), and apparent diffusion coefficient (ADC) were statistically evaluated. The 75th percentile rCBV and median ADC were significantly different between oligodendrogliomas and astrocytomas (P < .0001) and between oligodendrogliomas and oligoastrocytomas (P < .001). Logistic regression analysis identified both 75th percentile rCBV and median ADC as significant variables in the differentiation of oligodendrogliomas from astrocytomas and oligoastrocytomas. Group differences in metabolite intensities were not significant, but there was a much larger variation in the volumes and maximum values of metabolic abnormalities for patients with oligodendroglioma compared with the other tumor subtypes. Perfusion and diffusion imaging provide quantitative MR parameters that can help to differentiate grade 2 oligodendrogliomas from grade 2 astrocytomas and oligoastrocytomas. The large variations in the magnitude and spatial extent of the metabolic lesions between patients and the fact that their values are not correlated with the other imaging parameters indicate that MR spectroscopic imaging may provide complementary information that is helpful in targeting therapy, evaluating residual disease, and assessing response to therapy.
    Full-text · Article · Dec 2009
  • [Show abstract] [Hide abstract] ABSTRACT: Intel Corporation
    Conference Paper · Feb 2002 · Translational oncology
Show more