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Perfusion MRI (dynamic susceptibility contrast imaging) with different measurement
approaches for the evaluation of blood flow and blood volume in human gliomas
Thomsen, Henrik; Larsson, Elna-Marie; Steffensen, Elena
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Acta Radiologica
Publication date:
2012
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Citation for pulished version (APA):
Thomsen, H., Larsson, E-M., & Steffensen, E. (2012). Perfusion MRI (dynamic susceptibility contrast imaging)
with different measurement approaches for the evaluation of blood flow and blood volume in human gliomas.
Acta Radiologica, 53, 95-101.
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Download date: 11. May. 2017
Original article
Perfusion MRI (dynamic susceptibility contrast imaging) with
different measurement approaches for the evaluation
of blood flow and blood volume in human gliomas
H Thomsen1,2, E Steffensen3 and E-M Larsson3,4
1
Den Sundhedsfaglige Kandidatuddannelse, Aarhus Universitet Bygning 1264, Århus, Denmark; 2University College Nordjylland, Aalborg,
Denmark; 3Aalborg Hospital/Aarhus University Hospital, Department of Radiology, Aalborg, Denmark; 4Uppsala University Hospital,
Department of Radiology, Uppsala, Sweden
Correspondence to: H Thomsen. Email: [email protected]
Abstract
Background: Perfusion magnetic resonance imaging (MRI) is increasingly used in the evaluation of brain
tumors. Relative cerebral blood volume (rCBV) is usually obtained by dynamic susceptibility contrast (DSC)
MRI using normal appearing white matter as reference region. The emerging perfusion technique arterial
spin labelling (ASL) presently provides measurement only of cerebral blood flow (CBF), which has not been
widely used in human brain tumor studies.
Purpose: To assess if measurement of blood flow is comparable with measurement of blood volume in
human biopsy-proven gliomas obtained by DSC-MRI using two different regions for normalization and two
different measurement approaches.
Material and Methods: Retrospective study of 61 patients with different types of gliomas examined with
DSC perfusion MRI. Regions of interest (ROIs) were placed in tumor portions with maximum perfusion on
rCBF and rCBV maps, with contralateral normal appearing white matter and cerebellum as reference
regions. Larger ROIs were drawn for histogram analyses. The type and grade of the gliomas were obtained
by histopathology. Statistical comparison was made between diffuse astrocytomas, anaplastic
astrocytomas, and glioblastomas.
Results: rCBF and rCBV measurements obtained with the maximum perfusion method were correlated
when normalized to white matter (r ¼ 0.60) and to the cerebellum (r ¼ 0.49). Histogram analyses of rCBF and
rCBV showed that mean and median values as well as skewness and peak position were correlated (0.61 ,
r , 0.93), whereas for kurtosis and peak height, the correlation coefficient was about 0.3 when comparing
rCBF and rCBV values for the same reference region. Neither rCBF nor rCBV quantification provided a
statistically significant difference between the three types of gliomas. However, both rCBF and rCBV tended
to increase with tumor grade and to be lower in patients who had undergone resection/treatment.
Conclusion: rCBF measurements normalized to white matter or cerebellum are comparable with the
established rCBV measurements used for the clinical evaluation of cerebral gliomas.
Keywords: MR imaging – modalities/techniques, MR diffusion/perfusion, brain/brain stem – structures, tissue
characterization – topics
Submitted May 8, 2011; accepted for publication October 24, 2011
In brain tumor evaluation, magnetic resonance dynamic susceptibility contrast imaging (DSC-MRI) is presently the most
widely used MRI perfusion technique (1), allowing estimation of the cerebral perfusion in terms of relative cerebral
blood volume (rCBV) and relative cerebral blood flow
(rCBF). Visual evaluation of rCBV and rCBF maps can be
supplemented by quantitative measurements of the perfusion
values in regions of interest (ROI) placed in tumor tissue.
Tumor type and malignancy grade are determined by histopathological investigation. Perfusion MRI allows quantitative
characterization of gliomas and can potentially be a diagnostic
alternative to histopathological examination, which is invasive
Acta Radiologica 2012; 53: 95– 101. DOI: 10.1258/ar.2011.110242
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H Thomsen et al.
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and may not be possible to perform in some patients.
A ROI-based maximum perfusion approach has been
used in several studies to grade gliomas (2–4). Other studies
have used histogram-based analyses, obtaining a larger
number of the parameters characterizing the tumor region
(5–7).
Clinically established methods for quantitative perfusion
measurements usually include normalization of the mean
or maximum perfusion values in the tumor region in
relation to mean values in normal appearing white matter.
Normalized rCBV measurements are considered to be the
clinical standard (8, 9).
However, white matter is often affected by treatment
and/or edema or may be invaded by diffuse tumor
growth. When perfusion is measured with other techniques
(e.g. O15-PET, SPECT, and arterial spin labelling [ASL]
MRI) the cerebellum has often been used as a reference
region (10 –12). These techniques measure CBF but usually
not CBV. Thus the measurement of rCBF values by
DSC-MRI perfusion in tumors with the cerebellum as a
reference region needs to be evaluated (12).
The aim of this study was to assess if measurement of
blood flow is comparable with measurement of blood
volume in human biopsy-proven gliomas obtained by
DSC-MRI using two different regions for normalization
and two different measurement approaches.
Material and Methods
Patients
This is a retrospective study of patients with cerebral
gliomas who had undergone DSC-MRI examination at the
Department of Radiology of our hospital during the
period October 1, 2006 to September 30, 2008. Seventy-five
patients were enrolled in the study using the following
inclusion criteria: the diagnosis was a cerebral glioma,
DSC-MRI of the brain had been performed, and histopathological diagnosis based on tissue from needle biopsy or surgical resection had been obtained.
Subsequently, 14 patients were later excluded because of
technical reasons: artifacts in images and inadequate histopathological diagnoses.
The final study population consisted of 61 patients, 31
men and 30 women. Twenty-three patients had DSC-MRI
at baseline before the start of treatment and 38 patients
after the start of treatment. Each patient was only assessed
on one occasion in this study.
The National Danish Board of Health and the Danish
Data Protection Agency approved this study.
MR imaging and postprocessing
MRI protocol: MRI was performed on 3T and 1.5T systems
(Signa HDx; GE Healthcare, Milwaukee, WI, USA) with
morphological sequences (transverse T2-weighted fast spin
echo [FSE], transverse T1-weighted fluid attenuated inversion recovery [FLAIR] before and after contrast injection,
coronal T1-weighted spin echo [SE] after contrast injection),
transverse diffusion-weighted imaging (DWI) and transverse
DSC perfusion imaging. The DSC imaging was performed
with a single-shot gradient echo planar imaging (EPI)
sequence with TR/TE ¼ 1400/29 msec, matrix 128 128,
acceleration factor ¼ 2, FOV 24 cm, slice thickness 5 mm
with 1 mm inter-slice gap, 24 slices, scan time 1.5 min.
Gadolinium-based contrast agent (Gadovist; Bayer Schering
Pharma AG, Berlin, Germany), 0.1 mmol/kg, was injected
intravenously at 5 mL/s.
Postprocessing: rCBV and rCBF maps were calculated
using an established tracer kinetic model applied to firstpass data (13, 14) (NordicIce NordicImagingLab, Bergen,
Norway) (15). Deconvolution of the measured signal-time
curves was performed using singular value decomposition
with arterial input function of about 5 pixels retrieved
from the middle cerebral artery branches in the hemisphere
contra lateral to the tumor. Correction for contrast agent
leakage in the tumor due to blood –brain barrier disruption
was also included in the postprocessing (16).
Quantitative image analysis
Tumor tissue was identified on T1-weighted contrastenhanced images and/or T2-weighted images and simultaneously on the perfusion images. ROIs were placed in
tumor tissue with maximum signal intensity on rCBF
maps, and in normal-appearing white matter and cerebellum as reference regions. (Fig. 1). Care was taken to avoid
edema and blood vessels by comparison with
T2-weighted images at the corresponding slice positions.
All ROIs were drawn by a technician and subsequently
checked and corrected if needed by an MRI-physicist with
experience in image postprocessing and an experienced
neuroradiologist.
In addition, ROIs representing a substantial part of the
tumor for histogram analyses were drawn over three slices
on rCBF maps. All ROIs was copied to rCBV images.
The size of the ROIs varied from 0.25– 1.19 cm2 for the
small ROIs and from 3.3 –86 cm2 for the large ROIs used
for histogram analyses. For the histogram analyses, slices
through the central largest portion of the tumor were
chosen to minimize partial volume effects. Postoperative
cavities were avoided, whereas central necrosis was
included as a part of the large ROI for the histogram analyses when they were present on the slices with the most
substantial part of the tumor. This method was chosen to
keep the methodology of the histogram ROI drawing as
simple and operator independent as possible (Fig. 1).
Patients with small residual tumors after treatment (less
than 3 cm2) were not included in the histogram analyses.
In low grade gliomas, small ROIs were defined by the
experienced radiologist according to the most prominent
tumor signal on CBF images, whereas the large ROIs were
drawn according to morphological MR images.
The perfusion values of normal appearing white matter
and cerebellum were used to obtain normalized maximum
rCBF and rCBV calculated as ratios of mean perfusion
values in tumor ROI with maximum perfusion to mean
values in reference regions.
Histograms were normalized to the total number of pixels
in the ROIs and to the mean values in reference regions; the
Perfusion MRI (dynamic susceptibility contrast imaging) with different measurement approaches
97
.................................................................................................................................................
Fig. 1 Example of the ROI placement on rCBF and rCBV maps together with T1-weighted post-contrast images for tumor delineation. The upper row depicts ROI
placed in a region with maximum rCBF; the second row includes an example of the larger ROI drawing used for histogram analyses placed on rCBF images and
reference ROIs for the cerebellum. The last row presents choice of the ROI for the reference regions
number of histogram bins was 108 as proposed by Emblem
et al. (7). The following histogram metrics were used for analyses: mean, median, standard deviation (SD), peak position
(PP), peak height (PH), skewness, and kurtosis.
Statistical analysis
The analysis of results obtained with the maximum perfusion ROI approach included descriptive statistics, logarithmic transformation of data in cases of non-normal
distribution, and one-way analysis of variance ANOVA.
Stratification into groups of untreated and treated tumors
and correlation analysis for CBF and CBV measurements
were made to compare the influence of the choice of reference region on results assessed by maximum perfusion
ROI approach and the histogram method.
Results
The histopathological diagnoses of the majority of the 61
gliomas investigated in our study were glioblastomas (n ¼
38), diffuse astrocytomas (n ¼ 8), and anaplastic astrocytomas
(n ¼ 8). Oligodendrogliomas, anaplastic oligodendrogliomas,
and gangliogliomas were the smallest groups, the number of
tumors being n ¼ 4, n ¼ 2, and n ¼ 1, respectively. Tumor
types, WHO grades, number of patients, mean age of patients,
rCBF, and rCBV values calculated with white matter tissue
and cerebellum tissue as reference are summarized in Table 1.
Table 1 shows higher perfusion values for glioblastoma
than for diffuse and anaplastic astrocytomas.
The results of comparison of median perfusion values for
diffuse astrocytoma, anaplastic astrocytoma, and glioblastoma are shown in Table 2.
The stratified analysis of untreated and treated tumor
groups did not show any statistically significant difference
in perfusion values between these tumor groups; however,
Table 2 shows lower values of rCBF and rCBV for tumors
undergoing treatment.
Figs. 2 and 3 illustrate the correlation between rCBF and
rCBV measurements in the maximum enhancement perfusion tumor region for both reference ROIs in white
matter and cerebellum (Pearson correlation coefficient r ¼
0.60 and r ¼ 0.49, respectively).
Mean values for histogram metrics for rCBF and rCBV
measurements in tumor regions normalized to cerebellum
and white matter for all tumors included in our study are
presented in Table 3. Pearson correlation coefficients
for comparison between rCBF and rCBV measurements
with different normalization regions are shown in Table 4.
98
H Thomsen et al.
.................................................................................................................................................
Table 1 Patients (n), age, and mean relative perfusion values obtained with maximum perfusion ROI approach for different tumor types
Histopathological
diagnosis
Diffuse
Astrocytoma
WHO grade
II
Patients (n)
8
Age (mean years) (sd)
37.2 (9.6)
White matter as reference tissue
Mean rCBV (sd)
2.90 (1.47)
Mean rCBF (sd)
5.08 (4.25)
Cerebellum as reference tissue
Mean rCBV (sd)
1.81 (1.29)
Mean rCBF (sd)
2.51 (2.06)
Anaplastic
Astrosytoma
Glioblastoma
Oligodendroglioma
Anaplastic
Oligodendroglioma
Ganglioglioma
III
8
45.8 (13.8)
IV
38
55.5 (12.7)
II
4
55.1 (26.0)
III
2
57.9 (16.7)
I
1
41
3.73 (1.83)
4.53 (2.57)
6.35 (5.05)
6.72 (3.48)
3.65 (1.45)
4.10 (1.91)
7.97 (2.73)
7.66 (2.12)
4.72
3.51
2.79 (2.29)
2.21 (1.72)
2.99 (2.02)
2.70 (1.36)
2.62 (1.85)
2.03 (1.04)
3.24 (2.18)
2.64 (1.34)
1.78
1.23
Table 2 Medians for diffuse astrocytoma WHO II, anaplastic astrocytoma WHO III, and glioblastoma WHO IV divided into groups of untreated and
treated patients
All tumors
WHO
(n)
White matter as reference tissue
rCBV
II
8
III
8
IV
38
rCBF
II
8
III
8
IV
38
Cerebellum as reference tissue
rCBV
II
8
III
8
IV
38
rCBF
II
8
III
8
IV
38
Untreated tumor
Treated tumor
Median (95% CI)
(n)
Median (95% CI)
(n)
Median (95% CI)
2.66
3.69
4.51
3.66
3.75
5.81
(1.39 –5.09)
(1.93 –7.08)
(3.34 –6.08)
(2.28 –5.85)
(2.34 –6.00)
(4.68 –7.21)
2
4
12
2
4
12
4.40
4.13
5.75
9.71
3.89
6.90
(1.24 –15.65)
(1.80 –9.46)
(3.14 –10.52)
(2.20 –42.80)
(1.62 –9.33)
(4.85 –9.81)
6
4
26
6
4
26
2.25
3.30
4.08
2.64
3.61
5.41
(1.08 – 4.67)
(1.44 – 7.56)
(2.78 – 6.01)
(1.12 – 6.22)
(1.51 – 8.67)
(4.33 – 6.78)
1.39
2.19
2.17
1.69
1.68
2.34
(0.72 –2.70)
(1.13 –4.24)
(1.60 –2.94)
(1.03 –2.77)
(1.03 –2.76)
(1.87 –2.94)
2
4
12
2
4
12
2.14
2.23
2.61
4.14
1.67
3.00
(0.49 –9.30)
(0.86 –5.81)
(1.46 –4.66)
(0.79 –21.98)
(0.57 –4.90)
(2.16 –4.14)
6
4
26
6
4
26
1.21
2.16
1.99
1.25
1.70
2.10
(0.52 – 2.83)
(0.83 – 5.58)
(1.35 – 2.97)
(0.18 – 3.25)
(0.58 – 5.05)
(1.68 – 2.61)
The highest correlation was observed for mean values (histogram metric) with white matter as reference region (r ¼
0.93); the lowest correlation was observed for kurtosis and
peak height (r ¼ 0.3).
Our study has shown that CBF measurements with cerebellum and normal-appearing white matter as reference region
are correlated with the established rCBV measurements
used for quantification of relative tumor perfusion. The correlation was slightly higher when normal-appearing white
matter was used as reference region. Thereby methods
that only provide CBF measurements, such as the increasingly used ASL perfusion technique, can potentially
replace the DSC perfusion for evaluation of gliomas. ASL
is useful particularly in patients with renal failure or previous adverse effects after injection of gadolinium-based
contrast agents. Cerebellum has previously been used as
Fig. 2 Scatterplot showing rCBF and rCBV in tumor tissue normalized to
normal-appearing white matter in diffuse astrocytoma, anaplastic astrocytoma, and glioblastoma. Pearson correlation coefficient is 0.60
Fig. 3 Scatterplot showing rCBF and rCBV in tumor tissue normalized to cerebellum in diffuse astrocytoma, anaplastic astrocytoma, and glioblastoma.
Pearson correlation coefficient is 0.49
Discussion
Perfusion MRI (dynamic susceptibility contrast imaging) with different measurement approaches
99
.................................................................................................................................................
Table 3 Mean values and standard deviations in parentheses of histogram metrics for CBF and CBV analyses for all tumors in the study
Median
Mean
SD
Peak position
Mean values (SD) of histogram metrics for CBF analyses in tumour with different reference regions
White matter as reference region
2.38 (1.63)
3.00 (1.81)
2.55 (1.74)
1.61 (1.33)
Cerebellum as reference region
0.93 (0.52)
1.17 (0.57)
0.99 (0.50)
0.65 (0.54)
Mean values (SD) of histogram metrics for CBV analyses in tumor with different reference regions
White matter as reference region
2.58 (1.93)
3.21 (2.14)
2.68 (1.58)
1.62 (1.73)
Cerebellum as reference region
1.22 (0.72)
1.54 (0.80)
1.31 (0.70)
0.79 (0.76)
reference region in a few studies (11, 12) when comparing
CBF measurement assessed by different techniques.
We did not find statistically significant differences
between the perfusion parameters for the different tumor
types: diffuse astrocytoma, anaplastic astrocytoma, and
glioblastoma. A comprehensive review of studies performed
during the last 5 years showed that some studies found a
statistically significant difference between tumor groups
(2 – 4, 17, 18), and others did not (8, 19). The results of our
study are in accordance with the theory of tumors: tumor
development and angiogenesis showing increasing perfusion of tumor tissue with higher tumor grade (20 – 23).
Table 2 shows that there is a trend towards increasing
values for glioblastoma (WHO IV) in comparison with
diffuse astrocytoma (WHO II), which is in agreement with
other studies (18, 24). As seen from Table 5, several
studies report higher values of rCBV in gliomas of high
grade than gliomas of low grade (2 – 4, 17, 18, 24– 27).
Normalized rCBV values in our study are in agreement
with the values found in the literature (Tables 3 and 5).
The stratified analysis of patients groups with untreated
and treated tumors did not show any statistically significant
difference between the two strata with our ROI methods.
However, in general, lower median values were observed
for rCBF and rCBV in the group with treated tumors.
It can be seen from Fig. 2 that for the majority of tumors
higher values of rCBF corresponded to higher values of
rCBV for calculations performed using both reference
regions. The Pearson correlation coefficient was r ¼ 0.60 in
the case of normalization to white matter and r ¼ 0.49
when the cerebellum was used. The lower correlation coefficients observed in our study in comparison with r ¼ 0.65
in Jarnum et al. (12) and r ¼ 0.75 in Senturk et al. (28) can
be explained by a more homogeneous tumor population
in our study. We did not observe extremely high values
for normalized perfusion using cerebellum as reference
region. From our experience and taking into account the discussion above, it is difficult to recommend one of the investigated reference regions; both white matter and cerebellum
Peak height
Skewness
Kurtosis
0.08 (0.04)
2.18 (0.95)
5.06 (5.65)
0.08 (0.03)
2.09 (0.84)
4.44 (4.35)
can be used for relative measurements of perfusion values
in cerebral gliomas. However, the white matter perfusion
may potentially more often be affected by, for example, radiation therapy and tumor invasion than the cerebellum. In
some of the patients, we have observed abnormally low
values in the white matter compared with the rest of the
patients resulting in higher rCBF values.
The maximum perfusion ROI approach has been reported to
have an ability to differentiate between different tumor types
(2–4), but it also has been reported to suffer from observer
bias (7). To increase reproducibility of the measurements, placement of up to four ROIs in different parts of the tumor has
been proposed (5, 6). In our study, we have instead used
three experts with technical, image postprocessing, and neuroradiological skills to agree on maximum perfusion tumor
region on perfusion images to verify our ROI measurements.
The histogram analysis method has been found to be less
observer-dependent (5, 7), and we have used 108 bins for
ROI histogram visualization and analyses, as recommended
by Emblem et al. (7). Histogram metrics (mean, median, SD,
and PP) were calculated for histograms normalized to both
white matter and cerebellum. As skewness, PH, and kurtosis do not depend on choice of reference region, these parameters were calculated with only normalization to the
total number of pixels.
The mean values for histogram metrics obtained in our
study are comparable with calculations performed by
other groups. Emblem et al. (7) gives PH values from
0.07– 0.16 for high and low-grade gliomas, respectively;
another study (29) shows values from 0.024 – 0.12, which is
in good agreement with a PH of about 0.08 (Table 3) in
our study. The mean values for median, mean, SD, and
PP, as well as skewness, kurtosis, and PH can be found in,
for example, Law et al. (5). While there is a good correspondence between median (from 1.14 + 0.49 to 2.72 +0.68,
depending on tumor grade (5) versus 2.38 + 1.63 in our
study), mean (from 1.24 + 0.47 to 2.83 + 0.68 (5) and
2.55 + 1.74 in our study), SD values (from 0.49 + 0.32 to
2.32 + 0.29 and 3.34 + 1.75 in our study), and PP (from
Table 4 Pearson correlation coefficients for different histogram metrics for CBF and CBV analyses for all tumors presented in the study
Median
Mean
SD
Pearson correlation coefficients for rCBF versus rCBV
White matter as reference region
0.91
0.93
0.88
Cerebellum as reference region
0.80
0.76
0.69
Pearson correlation coefficients for white matter versus cerebellum reference
rCBF
0.82
0.79
0.80
rCBV
0.80
0.76
0.73
Calculation performed for histograms without normalization to reference region
NC ¼ not calculated
Peak position
0.76
0.72
region
0.90
0.90
Peak height
Skewness
Kurtosis
0.30
0.43
0.3
NC
NC
NC
NC
NC
NC
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Table 5 rCBV measurements in four studies with calculated weighted common estimates
Reference/
study
Gliomas of
low grade
Gliomas of
high grade
Hakyemez
et al. (4)
Fan et al. (26)
Law et al. (5)
Costanzo
et al. (2)
Hakyemez
et al. (4)
Fan et al. (26)
Law et al. (5)
Costanzo
et al. (2)
n
rCBV
estimate
sd
11
1.69
0.51
0.15
42.29
71.47
7
31
11
1.09
1.51
2.00
0.26
0.64
1.50
0.10
0.11
0.45
103.55
75.68
4.89
112.87
114.28
9.78
26
5.76
3.35
0.66
2.32
13.34
8
31
25
3.27
5.54
4.30
1.54
2.37
1.20
0.54
0.43
0.24
3.37
5.52
17.36
11.03
30.58
74.65
se
†
‡
w
Estimate w
Common estimate§
(95% CI)
sdf common
estimate sef common
estimate††
1.36 (1.23 –1.49)
0.86
0.07
4.54 (4.17 –4.9)
2.27
0.19
Given estimate from reference/study
p
se ¼ sd/ n
w ¼ 1/se2
P
P
§
Common estimate ¼ (Estimatei wi)/ (wi)
p
2
2
2
2
sdf ¼ ((sd1 þ sd2 þ sd3 þ sd4)/4)
p
††
sef ¼ 1/ w1 þ w2 þ w3 þ w4
†
‡
1.10 + 0.61 to 3.34 + 1.75 and 1.61 + 1.33 in our study), the
remaining histogram metrics are difficult to compare due to
the different number of histogram bins used.
Due to a good correlation for rCBF and rCBV histogram
metrics calculated with different reference regions (correlation
coefficients 0.73 r 0.90, Table 4), we suggest that both
reference ROIs can be used for histogram analyses of brain
tumors. Low correlations for kurtosis, skewness, and PH parameters comparing unnormalized rCBF and rCBV measurements reflect the fact that rCBF and rCBV distributions in
tumor have different shapes (Figs. 4 and 5). However, the
typical differences in histogram shapes for CBV for different
tumor types (5) are accompanied by differences in shapes for
CBF measurements. Potentially, rCBF could be used in the
same manner as rCBV to train radiologist to recognize histogram shape patterns for different tumors (7).
The main limitation of our study is the relatively small
number of samples, which contributes to the statistical
uncertainty and could be the reason for the statistically nonsignificant results. This presumption is supported by the
sample size calculation. Table 5 shows rCBV measurements
in four studies with calculated weighted common estimates
used for sample size calculations.
Although DSC-MRI is in widespread clinical use, tumor
studies with this technique often include a relatively small
number of patients, from 10 to up to 92 patients (3, 5).
Our study is based on clinical data collected during 2
years at our hospital. Since gliomas have a relatively low
incidence (20), the number of patients was relatively small.
The sample size calculations for this study estimated
sample sizes between 20 and 75, and only the glioblastoma
group met this requirement. To obtain a reasonable sample
size, we have included both untreated and treated patients
in our study. The sample size and inclusion of both
treated and untreated patients was also the main reason
for not performing statistical analyses for histograms
Fig. 4 Example of CBV distribution for glioblastoma (in yellow) and anaplastic astrocytoma (in blue). CBV values are normalized to cerebellum
Fig. 5 Example of CBF distribution for glioblastoma (in yellow) and anaplastic astrocytoma (in blue). CBF values for the same patients as in Fig.4 normalized to cerebellum
Perfusion MRI (dynamic susceptibility contrast imaging) with different measurement approaches
101
.................................................................................................................................................
metrics and no attempts to provide classification based on
visual inspection of histograms.
Another limitation is the fact that our patients had been
examined at two different field strengths. Field strength
affects tissue characteristics and image quality. Improved
quality of the calculated parametric perfusion maps may
be seen at higher field strength due to higher signal-to-noise
ratio. However, as we compared patients on the individual
basis and used normalized values of CBF and CBV we
could disregard the impact of this factor compared, for
example, to inclusion of both treated and untreated
patient in the study.
In conclusion, rCBF values may be used as an alternative
to rCBV values for MRI evaluation of perfusion in gliomas.
Comparison of the results obtained with cerebellum and
white matter as reference regions shows that the cerebellum
can be used as a valid reference region for the calculation of
relative perfusion values.
Conflict of interest: None.
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