CA 02581904 2007-03-08
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TUMOR GRADING FROM BLOOD VOLUME MAPS
Field of the invention
The present invention relates to tumor grading, in particular tumor grading by
analysis of
blood volume maps obtained by e.g. perfusion imaging using contrast agents.
Background of the invention
It is often of interest to locate and characterize tumors in a non-invasive
manner, this is
especially relevant for brain tumors (gliomas) due to their inherent
inaccessibility. Here,
MRI (Magnetic Resonance Imaging) and CT (Computed tomography) imaging are
typically
the imaging methods of choice. While being excellent for determining position
and size,
these techniques convey little information about the functional status of the
tumor tissue
or adjacent tissue (e.g. degree of angiogenesis, tissue viability, malignancy,
etc). Although
tumor malignancy to some extent may be suggested indirectly by contrast
enhanced
imaging, several studies have shown that the degree of contrast enhancement is
by no
means a reliable indicator of tumor grade. Based on these shortcomings,
dynamic
susceptibility perfusion imaging is becoming increasingly important due to its
usefulness in
physiological imaging.
Perfusion imaging of tumors is used to demonstrate the vascular growth
(angiogenesis and
neovascularization) associated with tumor growth by imaging the Blood Volume
(BV) or
Blood Flow (BF) in a tumor. Since BV values correlate with the grade of
vascularity; high-
grade (malign) tumors tend to have higher BV values than low-grade (less
malign) tumors.
Perfusion imaging is therefore helpful in the grading of tumors. Imaging
methods for
mapping the cellular metabolism may correlate with the grade of vascularity in
a similar
way, and may therefore also be used.
Several studies have shown to differentiate between high- and low-grade
gliomas based on
relative cerebral blood volume (rCBV) maps obtained by perfusion MRI. The
general way to
characterize glioma malignancy is by measuring the ratio between the most
elevated rCBV
area within the glioma ("hot- spot"), and an unaffected contra-lateral white
matter rCBV
value. Although several notations are used, this ratio is often referred to as
normalized
CBV (nCBV), and high-grade gliomas tend to have a higher nCBV ratio than low-
grade
gliomas. This method is described in e.g. Wetzel et al., Radiology 2002: 224:
797-803.
However, there are several limitations to this method. First, the selection of
giioma hot-
spot is highly user-dependent and differentiation between vessels and tumor
region of true
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blood volume elevation can be challenging and a source of error. Secondly,
since only a
few image pixels are typically used to determine the rCBV hot-spot, the method
is
inherently sensitive to image noise and other sources of spurious pixel values
(e.g. spikes
introduced by the algorithms used to generate the nCBV maps). Thirdly,
unaffected white
matter rCBV values are generally used to derive the nCBV value. This is based
on the
assumption that most gliomas are located in white matter. However, incorrect
selection of
reference rCBV values might result in either under- or overestimation of nCBV
values.
Finally, oligodendrogliomas tend to give high nCBV values irrespective of
glioma grade.
As a result, cut-off nCBV values between high-grade and low-grade giiomas
might be
harder to establish if oligodendrogliomas are included. As an alternative to
the hot-spot
method, Schmainda et al, Am 3 Neuroradiol 2004: 25: 1524-1532, suggests to use
the
mean nCBV for the tumor as the basis for grading (referred to as the WT
method), but this
method has not been consistently compared to the hot-spot method. Also, a mean
nCBV
has the disadvantage of not reflecting the diversity of values in the tumor.
Summary of the invention
It is an object of the present invention to provide a new and more precise
approach to
tumor grading from BV or cellular metabolism maps.
The invention thus provides a method and a system for grading tumors based on
BV or
cellular metabolism maps, and a method for preparing a correlation data set to
be used in
such grading. An overall advantage of the invention is that it is much less
dependent on
subjective user interaction.
According to a first embodiment, the invention provides a method for grading a
tumor on
the basis of a map of values representing blood volume or cellular metabolism
of a tumor
region, the method comprising
- selecting regions of the tumor whose corresponding values in the map are to
be
applied in the grading, excluding large blood vessels and areas of necrosis;
- defining a plurality of value intervals and counting the number of values of
the selected
regions within each interval to determine a frequency distribution of values
in the
selected regions;
- assessing a tumor grade based on the frequency distribution, where the more
heterogeneous distribution corresponds to a higher grade.
Similarly, in a second embodiment, the invention provides a system for
providing
parameters to be used in grading tumors, the system comprising
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- software means for co-registering a map of values representing blood volume
or
cellular metabolism of a tumor region with image data representing tissue type
of the
tumor region;
- a data selection tool for assisting an operator in selecting regions of the
tumor whose
corresponding values in the map are to be applied in the grading and excluding
regions
representing large blood vessels and areas of necrosis;
- software means for counting the number of values of the selected regions
within each
of a plurality of value intervals to determine a frequency distribution of
values in the
selected regions; and
- means for determining one or more parameters from the frequency distribution
related
to heterogeneity of the frequency distribution.
The determination of a tumor grade may be carried out by a human operator
based on the
provided parameters, or may be automated by the system. In the latter case,
the means
for determining one or more parameters preferably comprises means for
correlating said
one or more parameters with a grade of the tumor, where the more heterogeneous
frequency distribution corresponds to a higher grade.
Also, in a third embodiment, the invention provides a method for preparing a
correlation
data set for use in assessment of a grade for a tumor by the above method or
system, the
method comprising:
- selecting a set of tumors of similar type so that the set comprises tumors
of all grades,
- providing a histologically determined grade of each tumor in the set;
- for each tumor in the set:
= selecting regions of the tumor whose corresponding values in the map are to
be
applied in the grading, excluding large blood vessels and areas of necrosis;
= defining a plurality of value intervals and counting the number of values of
the
selected regions within each interval to determine a frequency distribution of
values
in the selected regions;
= determining one or more parameters characterizing the heterogeneity of the
frequency distribution;
- correlating the determined parameters with the histologically determined
grades to
prepare a correlation data set from which a grade can be estimated for another
tumor
of similar type using corresponding parameters obtained from this other tumor.
A BV map is preferably obtained by perfusion imaging whereby images are
acquired
before, during and after injection of a contrast agent. When determining BV,
the contrast
agent is ideally restricted to the vascular system in the tissue of interest
in order for its
distribution to give a true representation of the blood volume. If the
contrast agent
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distributes throughout the extra cellular fluid space, the determined BV
values does not
provide a precise estimate of vascularity. All existing MR and CT contrast
agents are
restricted to the vascuclar space in the brain due to the presence of a blood-
brain barrier
(BBB). This means that all existing agents are intravascular agents in the
brain as long as
the BBB is intact. The BBB may be compromised by pathology such as tumours
causing
contrast agent leakage into the extracellular space. Such leakage may
introduce errors in
the resulting BV maps. Several methods have been suggested to overcome the
errors
introduced by such leakage. One method is to fit the first-pass curve to a
parametric
model like a gamma variate curve (see e.g. Knopp EA et al. Radiology 1999: 21:
791-
798). Another method is to apply a pharmacokinetic model to the observed
dynamic
curves in each voxel and correct for any leakage directly (see e.g. Boxerman
JL et al. AJNR
2006: 27: 859-867). Another approach is to use contrast agents with a larger
molecular
weight so that little leakage occurs even in regions of disrupted BBB.
Intravascular
contrast agents remain confined to the intravascular space, typically as a
result of having a
molecular weight of approximately 70,000 and above. Typical types of
intravascular
contrast agents are Gd-DTPA labelled albumin, Gd-DTPA labelled dextran, and
chromium-
labelled red blood cells.
Blood volume measurements are meant to determine the volume of blood in a
region of
tissue. The blood volume (BV) is used to evaluate the micro-vascular density
or
vascularity, in other words, the density of small blood vessels (capillaries)
in a tissue
region. Due to relatively small voxel sizes (typically tens of mmZ), large
vessels in the
region could result in a misleading shift of the BV frequency distribution
towards higher BV
values. Therefore, vessels having dimensions of the order of or larger than
the typical
spatial resolution of the applied perfusion imaging technique are preferably
excluded from
the regions whose BV values are used in the data analysis. Similarly, regions
consisting of
necrotic tissue, if included, shift the BV frequency distribution towards
lower BV values,
and such regions are preferably also excluded from the regions applied in the
analysis.
A cellular metabolism map correlating with the vascularity of the tumor is
preferably a
glucose metabolism map obtained by positron emission tomography (PET).
The map of values may be a 2D or 3D map resulting from a scanning technique
such as
perfusion MRI or CT or PET. A 3D map typically consists of several 2D maps
representing
slices through the tumor region.
The formation of a frequency distribution is also referred to as a histogram
analysis, i.e. a
mapping that counts the number of observations that fall into various disjoint
categories
(referred to as bins or value intervals). A histogram (graph or chart) is a
typical graphical
CA 02581904 2007-03-08
representation of the histogram analysis or frequency distribution.
The more heterogeneous distribution is the distribution where the values of
the map are
distributed over more value intervals, thereby 'representing more tumor volume
5 heterogeneity. The prominence of this characteristic, i.e. the heterogeneity
or diversity,
however, depends on the number of value intervals and the total number of
values, and
the more heterogeneous distribution may be described as:
- the distribution having the lower maximum value, as this indicates that the
values of
the distribution (when comparing distributions with the same number of values)
are
mainly distributed into a broader selection of value intervals and is
therefore less
peaked.
- the distribution having the larger FWHM or similar parameter characterizing
the width
of the distribution, the wider the distribution, the more heterogeneous it is.
- the number of values over a.predetermined threshold value interval, as many
values in
the tail corresponding to large values indicates a spread out and thereby
heterogeneous distribution.
The heterogeneity of the frequency distribution or tumor volume, and thereby
the grading,
may be assessed using several different approaches. Thus, in a preferred
embodiment,
one or more parameters characterizing the heterogeneity of the frequency
distribution,
that are to be used in the tumor grade assessment, are on one or more of the
following:
- a shape of the frequency distribution,
- variables in a parametric model applied to the frequency distribution, e.g.
probability
density function or a gamma variate function,
- the highest relative fraction of values in one interval, corresponding to a
peak height in
the histogram,
- whether a significant number of values in the distribution exceed a
predetermined
threshold or cut-off value (e.g. with p<0,05), or
- a FWHM or similar value of the frequency distribution.
As implied by the third embodiment, the assessment of a tumor grade based in
the
determined parameters may require having a correlation data set specific to
the relevant
type of tumors, e.g. brain tumors. This may also allow the grading of the
tumor by
embodiments of the present invention to be compared with grades obtained by
other
methods. Thus, the assessment of a tumor grade may comprise comparing the
determined
parameters with previously determined parameters of equivalent frequency
distributions
from tumors with a known grading, and using the correlation data set to assess
a tumor
grade. The system according to the second embodiment may thus comprise data
comparison software for this purpose.
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As will be described in greater detail in the detailed description, the
formation of the
frequency distribution may preferably comprise normalizing values of at least
the selected
regions to a reference value, and normalizing the frequency distribution. Such
normalization provides the advantage of enabling comparison between frequency
distributions of different tumors obtained at different locations.
A challenge in the method of the invention is to determine the tumor regions
from which
values are applied in the histogram analysis, this is also referred to as
tissue
segmentation. The selection of regions is preferably performed in one or more
other
images where the boundaries of the tumor are more easily determined and
different tissue
types such as large blood vessels and areas of necrosis more easily
segregated. By
coregistering such images with the map, values to be applied in the histogram
analysis can
be selected. Large blood vessels traversing the tumor may give rise to a
distorted
frequency distribution if included in the selected region. Here, by large is
meant larger
than voxel size or veins detectable in anatomic (e.g. T2-weighted) images. To
provide an
as good as possible statistical basis for the parameters determined from the
frequency
distribution, it would be advantageous to select the entire tumor, less the
excluded
regions, to be applied in the grading. Hence, this is a preferred feature of
the selection.
In one embodiment, the applied regions are selected manually on a slide-by-
slide basis by
marking up or selecting the regions on appropriate images of the tumor
(typically not the
BV or cellular metabolism map). In another embodiment, the applied regions are
selected
by an automated or semi-automated method configured to be carried out by
computer
software. This provides the advantages of reducing both user dependence and
workload.
An analysis of blood volume frequency distributions for high grade tumors has
been
applied to study the effect of radiotherapy in Cao et al., Int. J. Radiation
Oncology Biol.
Phys. 2006: 64: 876-885 (doi:10.1016/j.ijrobp.2005.09.001). Here, the temporal
changes
in CBV frequency distributions of high grade gliomas were studied during
radiotherapy to
be used as a predictor for survival. The data analysis in this publication
does not relate to
tumor grading. Only high grade gliomas (grade 3 and 4) are studied Cao et al.,
which is
therefore not considered to be relevant prior art for grading of tumors.
In summary the basic idea of an embodiment of the invention is to make
possible a non-
invasive grading of a tumor based on parameters determined from a frequency
distribution
of vaiues in a map representing blood volume or cellular metabolism in the
tumor. The
grading based on this basic idea is more precise and consistent grading since
it relies on
values selected from the whole tumor (not just from small specific regions,
hot spots);
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since it takes the diversity of the values into account by analyzing the
frequency
distribution (instead of simply a mean value); and since it involves and
allows for a more
automated procedure wherein any subjective contributions from human operators
is not
critical to the resulting grading.
In the present description, each preferred feature or element may be combined
or used by
itself and applied to each embodiment, where appropriate. These and other
embodiments
of the invention will be apparent from and elucidated with reference to the
embodiment(s)
described hereinafter.
Brief description of the figures
The present invention will now be explained, by way of example only, with
reference to the
accompanying Figures, where
Figure 1 is a flow diagram illustrating the method and software architecture
according to
embodiments of the invention.
Figure 2 is an illustration of a layout of a system for grading tumors, for
performing
histogram analysis, for preparing a correlation data set, or for performing
automated
segmentation of tissue to selected tumor regions in accordance with various
embodiments
of the invention.
Figures 3A-D show a sample case of a patient with a grade II diffuse
astrocytoma (Subject
120, Table 1) demonstrating the use of nCBV overlay maps to identify vessels
within the
tumor region.
Figures 4A and B show rCBV maps of a glioblastoma (A) and low grade
oligoastrocytoma
(B), the white circles indicate the tumor region in which regions to be
applied are selected.
Figures 5A and B show the glioma delineation marked by a thin grey line 30 on
different
images of a grade II oligodendroglioma.
Figures 6A and B are 25-bin histograms illustrating the distribution of nCBV
values in total
glioma volume of (A) grade II diffuse astrocytoma and (B) grade IV
glioblastoma. Note the
low maximum peak height and wide distribution in (B) compared to (A).
Figure 7 shows the histogram distribution of nCBV values in a number of
investigated
tumors.
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Figures 8A and B show confidence-intervals for the sensitivity (A) and
specificity (B) of the
hot-spot method (HS_1 and HS_2), the histogram method with different number of
bins
(H5-H100), and the WT method (WT).
Figure 9 shows the receiver operator characteristic (ROC) for the histogram
vs. the hotspot
method.
Figure 10 shows the result of a cluster analysis as an overlay on a T2-w
image.
Figure 11 shows mean nCBV peak values with standard deviations for low-grade
(black)
and high-grade (light grey) gliomas obtained by observers (OBS) and cluster
analysis.
Figures 12A and B show examples of nCBV maps of low-geade oligodendroglial
tumors
(indicated by the white circles) with and without the -lp/-19q genotype.
Figure 13 shows the distribution of normalized nCBV values from total glioma
volume in
low-grade oligodendroglial tumors without (curves 1-4) and with (curves 5-8) -
lp/-19q
genotype.
Detailed description of the invention
Figure 1 is a flow diagram 1 used in the following to embody various
embodiments of the
invention. The flow diagram 1 outlines and embodies the process steps
comprised in the
method for grading a tumor according to an embodiment of the invention. The
flow
diagram 1 outlines and embodies the system architecture, such as software
architecture,
of the system for providing parameters to be used in grading tumors according
to an
embodiment of the invention. Also, the flow diagram 1 outlines and embodies
the process
steps comprised in the method for preparing a correlation data set for use in
grading a
tumor according to an embodiment of the invention. Although described in
relation to the
example of CBV maps of brain tumors obtained by perfusion MRI, the
corresponding
process or architecture can be applied to techniques applied by other
embodiments of the
invention.
First, the data used to form maps representing blood volume or cellular
metabolism and to
perform tissue segmentation is acquired in box 1, typically a medical scanning
technique
such as MRI, CT or PET. The acquired data is used to form images (box 4) used
to
generate the maps (box 8) and images (box 5 and 6) to be coregistered with the
maps to
enable selection of regions to be applied in the grading. Other images may be
formed if
needed (box 3 and box 7).
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Obtaining BV data for a tumor by perfusion imaging is a well established
technique within
the art and has been extensively described. In short, perfusion imaging can be
performed
by MRI or multi-detector CT scanning by following an intravenously injected
bolus of
contrast agent. During the first pass of the contrast agent through the
vascular system
(typically of the order 5 to 15 seconds), it remains in the intravascular
space. In MRI
perfusion imaging, the intravascular paramagnetic contrast molecules cause a
shortening
of T2* relaxation, which results in signal loss. Relevant image types include
dynamic
contrast enhanced (DCE) images, T2-weighted images, T1-weighted images and
diffusion
weighted (DW) images (boxes 4-7). DCE image are used to generate regional
cerebral
blood volume (rCBV) maps based on the analysis of the dynamic signal response
following
bolus injection of the contrast agent (box 8). Apparent diffusion coefficient
(ADC) maps
(box 9) are generated by analysis of the signal change as a function of
diffusion weighting
obtained from the DW images. In CT perfusion imaging, the high concentration
of the
intravascular contrast agent during the first pass causes a higher density.
From the
changes in signal loss (MRI) or the increase in density (CT), the
concentration of the
contrast agent in each pixel can be calculated, and a pixel by pixel relative
estimate of
blood volume can be inferred. Maps of blood volume (BV) and blood flow (BF)
can be
generated.
Having the required images and maps, the selection of regions of the tumor
whose
corresponding values in the map are to be applied in the grading can be
performed either
by an operator (box 10), automatically (box 11) as will be described later, or
in a semi-
automatic procedure where an operator is assisted in the selection, e.g. by
software
providing educated guesswork. Large blood vessels and areas of necrosis are
sorted out in
the tissue segmentation.
Having selected the regions to be applied in the grading, the corresponding
values from
the maps are used to form the frequency distribution, or histogram, by
defining a plurality
of value intervals or bins, and counting the number of values of the selected
regions within
each bin (box 12). When forming the histogram, an appropriate number of
intervals or
bins should be selected. Having too many would result in too "flat
distribution" with very
few or none values in each bin. Having too few would result in a very peaked
distribution
regardless of the heterogeneity of the values. Often, one the following rules
may be used
to determine the number of bins, N, from the number of data points, n, here
the number
of BV values (voxels) in the tumor regions selected to be applied in the
analysis:
N=A~_n_
N =101og n
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Here A is a constant that may be determined for one sample to give a suitable
frequency
distribution for the purpose of determining the grading, whereafter the
equation can be
used to scale the number of bins for different data sets.
5 Further in box 12, the individual values are normalized against values from
normal tissue
in the same subject, or against a standard reference value. Reference tissue
can be
obtained by several means. First, it can be obtained by manual selection by a
trained user.
Alternatively it can be obtained by automated methods whereby the unaffected
white
matter of the brain is automatically segmented out using established
segmentation
10 techniques. Finally, standardization can be achieved by relating the rCBV
to the arterial
input function; e.g. the first-pass response in an artery feeding the relevant
parts of the
brain tissue. The arterial input function can be selected manually or
automatically using
appropriate segmentation techniques. This will be descried later in relation
to a more
detailed example. Also the frequency distribution itself can be normalized so
that the area
under the resulting histogram curve equals 1.
The resulting histogram can be evaluated in different ways to estimate a grade
of the
tumor. An experienced radiologist that has evaluated a large number of such
histograms
can estimate a tumor grade directly from the normalized histogram. This is
indicated by
the arrow between boxes 12 and 14. Hence, in one embodiment, the system may
simply
present the histogram to the operator.
In another embodiment, one or more parameters characterizing the heterogeneity
of the
frequency distribution, e.g. by the shape, peak height, width, etc. of the
histogram, can be
determined. Hence, in another embodiment, the system may present a parameter
characterizing the vascularization heterogeneity of the tumor to the operator.
Examples of
parametric functions that could be used include the gamma variate function:
F(rCBV) = rCBV exp(-rCBV /b)
where a and b are model parameters and rCBV is the regional blood volume (or
normalized
blood volume). Another example could be a Gaussian function of the form:
F(rCBV) = exp - (rCBV -K)Z
aZ
where a and K are model parameters. For both these examples the peak height
and the
FWHH of the distribution can be expressed analytically in terms of the model
parameters.
According to one embodiment of the invention, histograms as described in the
above are
formed for a set of tumors that has also been graded histologically, which can
serve as a
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reference database (box 13) for the grading of tumors. By correlating the
distributions,
typically through the determined parameters, with the histologically
determined grades, a
correlation data set can be prepared. Using the correlation data set, a grade
can be
estimated for a new tumor of similar type using corresponding parameters
obtained from
this new tumor. Hence, in yet another embodiment, the system may present an
estimated
grade to the operator.
Figure 2 illustrates a hardware layout of a system 20 for grading tumors, for
performing
histogram analysis, for preparing a correlation data set, or for performing
automated
segmentation of tissue to selected tumor regions in accordance with various
embodiments
of the invention. The system 20 has means 21 for receiving or accessing image
data to be
processed from an image recording apparatus such as a CT, MR, or PET scanner
24.
Alternatively, 24 may represent an internal or external storage holding images
recorded by
such apparatus. The means 21 may e.g. be a data bus allowing access to a
memory, an
internet connection, or a cable or wireless connection. The system comprises a
computer
or a similar processing apparatus holding an electronic processor 26 and
memory 27 for
holding and executing computer programs applying algorithms for tissue
segmentation,
histogram analysis and/or grading using the received image data, such as BV
maps
containing BV values and other contrast images for identifying and selection
relevant
20 tumor regions. A possible architecture for such software is described in
relation to Figure 1
in the above. After processing the received image data, the resulting
histogram,
parameter, or tumor grade could be applied in further (post) processing or
displayed,
printed etc. The system therefore also has means 22 for transmitting the
result to a
display 28, a printer, or to a further processing 29, e.g. a cable, data bus,
internet
25 connection or similar.
In the following, an application of the method according to an embodiment of
the invention
is described in relation to a method for glioma grading based on MR-derived
cerebral blood
volume (CBV) maps. The method is directly compared to the hot-spot method
applied in
the prior art. The description provides further details of the image recording
and the
histogram analysis and serves to further enable the invention.
Fifty patients (mean age 46 years, range 6-76 years, 28 males, 22 females)
with
histologically confirmed gliomas were imaged using dynamic contrast-agent
enhanced
magnetic resonance (MR) imaging. Imaging was performed at 1.5 Tesla (Siemens
Sonata,
Symphony or Avanto, Siemens AG, Erlangen, Germany). Imaging was performed
using a
8-channel- (Symphony/Sonata) or a 12-channel (Avanto) head-coil. The protocol
included
axial T2-w fast spin-echo (FSE) (TR/TE, 4000 ms /104 ms) and axial T1-w spin-
echo (SE)
(TR/TE 500 ms /7.7 ms) obtained before and after i.v. administration of
contrast agent.
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The voxel size was 0.45x0.45x5mm3 with 19 slices in both sequences. Dynamic
contrast-
enhanced perfusion MRI was performed using a gradient-echo echo-planar imaging
(GRE-
EPI) sequence acquired during contrast agent administration. The imaging
parameters
were: TR/TE 1430 ms /46 ms, bandwidth 1345Hz/pixel (12 axial slices) or 1720
ms /48
ms, bandwidth 1500Hz/pixel (14 axial slices), voxel size 1.80x1.80x5mm3 and
inter-slice
gap of 1.5mm. The number of slices was adjusted to cover the entire lesion.
For each slice,
50 images were recorded at intervals equal to TR. After approximately 8 time-
points, 0.1
mmol/kg of Gadovist (Schering AG, Berlin, Germany) was injected at a rate of
5mL/sec,
immediately followed by a 20mL bolus of saline (B.Braun Melsungen AG,
Melsungen,
Germany) also at 5mL/sec.
The images were transferred to a workstation and post processed using a
dedicated
software package (nordicICET'", NordicImagingLab, Bergen, Norway). The rCBV
maps were
generated using established tracer kinetic models applied to the first-pass
data. To reduce
the effects of recirculation, the OR2* curves were fitted to a gamma-variate
function which
is an approximation of a AR2* curve as it would appear in the absence of
recirculation or
leakage. nCBV maps were calculated on a pixel-by-pixel basis by dividing every
rCBV value
in a specific slice with an unaffected white matter rCBV value defined by a
neuroradiologist. The nCBV maps were displayed as color overlays on the
structural
images. Coregistration between the conventional MR images and the nCBV maps
was
performed based on the geometric information stored in the respective
datasets. If
needed, the nCBV overlay map was interactively adjusted to optimally match the
two
datasets.
All evaluations were performed independently by four experienced
neuroradiologist familiar
with perfusion MRI. A transparency slider for the overlay was interactively
adjusted to
identify large vessels as well as regions of contrast enhancement, necrosis or
edema from
the T1-w and T2-w underlay images. Figures 3A-D show a sample case of a
patient with a
grade II diffuse astrocytoma (Subject 120, Table 1) demonstrating the use of
nCBV
overlay maps to identify vessels within the tumor region; 3A shows a nCBV map.
3B shows
coregistered nCBV map overlaid on a T2-w FSE image (TR=4000/TE=104). 3C shows
a T2-
w FSE image. 3D shows a post-contrast T1-w SE image. The red arrow in 3B
indicates a
potential hot-spot area as seen on the nCBV map. However, the underlying
"vessel-like
structure" identified in both the T2-w image (3C) and the post-contrast T1-w
image (3D)
might suggest that this is not a true hot-spot. Figures 4A and B show rCBV
maps of a
glioblastoma (A) and low grade oligoastrocytoma (B), the white circles
indicate the tumor
region in which regions to be applied are selected. The higher heterogeneity
and larger
peak nCBV values are reflected in the corresponding histograms shown later in
Figure 7.
CA 02581904 2007-03-08
13
Region of interests (ROIs) were drawn on the nCBV overlays according to the
combined
overlay/underlay information. Three methods for glioma grading were tested in
each
subject; the method according to an embodiment of the invention (referred to
as the
histogram method in the following), the WT method described in Schmainda et al
(Am J
Neuroradiol 2004: 25: 1524-1532) and the hot-spot method described in Wetzel
et al.
(Radiology 2002: 224: 797-803).
The histogram method in accordance with an embodiment of the invention was
carried out
as described in the following. Using the available information in the
different image sets,
the observers selected regions of the tumor whose corresponding values in the
map are to
be applied in the grading, excluding large blood vessels and areas of
necrosis. The
selection was made by drawing freehand ROIs of what was considered to be the
complete
tumor area in each slice. Figures 5A and B demonstrate the glioma delineation,
here
marked by a thin grey line 30 on a grade II oligodendroglioma. Figure 5A shows
it on a
coregistered rCBV map overlaid on a T2-w FSE image (TR=4000/TE=104), Figure 5B
shows it on a T2-w FSE image (TR=4000/TE=104). As seen in both images, the
observer
has taken care to avoid areas within the glioma region with low signal on the
T2-w image
and high signal on the nCBV map which was thought to represent blood vessels.
Frequency distributions in the form of histograms were generated by
classifying the nCBV
values in the selected regions into a given number of bins. The area under the
resulting
histogram curve was normalized to 1. The range of the nCBV values along the x-
axis was
kept constant (0-15). The histogram method was tested using 5, 15, 25, 35, 50
or 100
bins. Glioma malignancy was assessed by measuring the maximum normalized peak -
height of the distribution (i.e. relative frequency of nCBV values in a given
histogram bin),
under the hypothesis that the peak height of the histogram distribution is
inversely
proportional to CBV heterogeneity, and hence tumor malignancy.
In the hot-spot method, each observer selected a minimum of four ROIs which
was
believed to represent high nCBV regions and the maximum value was used. In the
case of
multiple lesions, the largest lesion was chosen. In compliance with the
reference method,
the size of the tumor ROIs were kept constant (circular ROI with radius
1.8mm). Finally, a
mean nCBV was.generated based on the total tumor volume as defined by the four
observers (WT method).
Mann-Whitney tests were used to evaluate the glioma grading capability of each
method. A
significance level of P=.05 was used for all tests. Sensitivity and
specificity, based on
optimal cut-off values, were derived using binary logistic regression. A
glioma classified as
high/low -grade by both observer data and histology was considered as a true-
positive/
CA 02581904 2007-03-08
14
true-negative findings, respectively. To compare our results with previous
studies, the
sensitivity and specificity of the hot-spot method was also calculated using a
previously
published cut-off nCBV value of 1.75 (15). The ability of each method to
differentiate
between grade II oligodendroglial tumors (oligodendrogliomas or
oligoastrocytomas) and
grade II diffuse astrocytomas, or between grade III gliomas (anaplastic) and
grade IV
gliomas (glioblastoma) were investigated. Inter-observer reproducibility with
respect to
glioma grading was tested by assessing the percentage of patients in which the
data from
all observers gave the same glioma grade. This was tested for each method
using the
optimal cut-off values estimated by binary logistic regression. For the hot-
spot method,
this percentage was also estimated using the proposed 1.75 cut-off value.
Statistical
analysis was performed using SPSS 13 (Apache Software Foundation, Chicago,
US).
Of the 50 gliomas investigated, 27 were histologically confirmed to be low-
grade (WHO
grade I-II) and 23 were high-grade (WHO grade III-IV). A summary of patient
demographics, histological diagnosis, surgical procedure and conventional MR
findings are
shown in Table 1 in the appendix. On average, the four observers reported
using 7
minutes and 11 minutes per patient when using the hot-spot method and tumor
delineation (WT and histogram method), respectively. The methods were reported
equally
difficult to perform. Figures 6A and B are 25-bin histograms illustrating the
distribution of
nCBV values in total glioma volume of (A) grade II diffuse astrocytoma and (B)
grade IV
glioblastoma. Note the low maximum peak height and wide distribution in (B)
compared to
(A). Figure 7 shows the histogram of the frequency distribution of nCBV values
in a
selection of investigated tumors. A cut-off or threshold nCBV value of 2.0
(p<0.05) as
shown by the vertical punctured line was found to differentiate high-grade
tumors from
low-grade. Note the distinct shape difference between the high-grade and low-
grade
tumors.
As shown in Table 2 in the appendix, all methods tested could correctly
identify high-grade
(grade III and IV) gliomas (P<.001, P<.006 and P<.001 for histogram-, hot-spot-
and WT-
methods, respectively). Regardless of bin numbers, only the histogram method
was able to
differentiate between grade III (n=5) and grade IV gliomas (n=18) in all
observers (Table
3 in the appendix).
For all methods and all histogram bin numbers, there was an overlap between
the 95%
confidence-intervals for both specificity and sensitivity in all observers.
Hence, the
confidence-intervals were considered similar, and the data could be pooled,
giving 200
data points for each method (Table 3). Using optimal cut-off values, the
sensitivity of the
histogram method (100-bins) was borderline significantly higher than the
sensitivity of the
hot-spot method (74-90% vs. 55-75%, respectively). The sensitivity,using a
nCBV cut-off
CA 02581904 2007-03-08
value of 1.75 was significantly higher than any other method (92-99%), and the
specificity
was significantly lower (14-30%). Figures 8A and B show the resulting
confidence-intervals
for the sensitivity (A) and specificity (B) of the hot-spot method using an
optimal cut-off
value (HS_1), the hot-spot method using a published cut-off value of 1.75
(HS_2), the
5 histogram method with different number of bins (H5-H100), and the WT method
(WT).
Agreement in glioma grade based on data from all observers was obtained in
68%, 82%
and 88% of the patient population when using the hot-spot, WT and histogram
method,
respectively (Table 3).
10 Using optimal cut-off values, the results show that the histogram method
according to an
embodiment of the invention is less user-dependent than the hot-spot method
and
provides significantly higher sensitivity and equal specificity. Further, the
histogram
method may be made independent of choice of reference tissue. The effect of
changing the
reference tissue from e.g. white to gray matter is simply a shift of the
position of the peak
15 distribution bin without changing the actual peak value. In contrast, the
hot-spot method
is critically dependent on correct selection of reference tissue since the
determination of
nCBV is based on this parameter alone. Although the number of histologically
confirmed
grade III gliomas was low in this study (n=5), only the histogram method was
able to
differentiate between grade III and grade IV gliomas (Table 3). The consistent
results of
the histogram method suggests that grade IV gliomas are generally more
heterogeneous
than grade III gliomas, whereas both grade III and IV gliomas might have
similar hyper-
vascular regions. As described in previous studies (18), necrosis was a
specific marker for
distinguishing grade IV gliomas from grade III, but not a sensitive one (Table
1).
Figure 9 shows the receiver operator characteristic (ROC) for the histogram
vs. the hotspot
method. A ROC is a standard method to assess how sensitivity is affected by
changes is
specificity. As shown in Figures 8A and B, the sensitivity of the hotspot
method can be
made very high but at the expense of a very low specificity (e.g. many false
positives).
The histogram method is shown to be a better predictor for glioma malignancy
than the
hotspot method since the curve is closer to the ideal curve (which would be a
single point
in the upper left corner; i.e. 100% true positives with no false negatives).
When comparing the use of different histogram bin numbers, the 95% confidence-
interval
for sensitivity generally improved with increasing number of histogram bins,
whereas the
specificity remained relatively unchanged (Table 3). The reduced sensitivity
at lower bin
numbers can be explained by the large range of rCBV values contained in each
of the
resulting bins which will tend to mask out small hyper-vascular regions in
high-grade
gliomas.
CA 02581904 2007-03-08
16
In the embodiment of the histogram method applied in the above, the peak
height of the
normalized histogram distribution of nCBV values in the tumor was used as the
parameter
characteristic for the heterogeneity of the frequency distribution. This
approach was
chosen because the resulting height is directly dependent on the underlying
heterogeneity
of the nCBV distribution. As suggested by other embodiments of the invention,
the
histogram based analysis can be further improved by parametric analysis of the
total
frequency distribution rather than just the peak value.
According to an embodiment of the invention, the tumor regions from which
values are
applied in the histogram analysis can be selected by an automated or semi-
automated
method configured to be carried out by computer software. This provides a more
user
independent and automated method for selecting regions of the tumor whose
corresponding values in the map are to be applied in the grading and for
excluding large
blood vessels and areas of necrosis. In the below, an automated method that
can aid in
the selection of tumor regions is described and evaluated. The automated
method analyses
regions of interest (ROIs) using k-means cluster analysis of multiple MR
images taken from
a standard CNS tumor image protocol, including first-pass perfusion imaging.
Thirty-five patients with histologically confirmed gliomas, (aged 6-76 yrs,
mean age 46; 22
males, 13 females) were included in the study. Imaging was performed at 1.5 T
(Siemens
Sonata or Avanto, Germany) prior to surgery. rCBV maps were generated using
established tracer kinetic models applied to the first pass data obtained by
i.v. bolus
injection of 0.lmmol/kg of Gadovist (Schering AG, Germany). The time
resolution of the
first-pass gradient echo (GRE)-EPI sequence was 1.5s and the voxel size was
1.8x1.8x6.5mm3. An experienced neuroradiologist created normalized (n)CBV maps
by
dividing each rCBV value in each slice with an unaffected white matter rCBV
value. The
nCBV maps were coregistered with conventional T2-w FSE, T1-w SE pre-contrast,
T1-w SE
post-contrast and MR diffusion (b-values = 0, 500, 1000) images. K-means
cluster
analysis was performed in Matlab 2006a by minimizing the squared Euclidean
distance
between cluster members. The cluster analysis was performed in three steps.
Initially,
vessels infiltrating the glioma volume were identified by clustering of
composite images
generated from T2-w and diffusion (DW) images (b=1000, T2-corrected). The
resulting
images were then used as a binary mask to exclude vessels from further
analysis. Edema
and cystic components were identified from cluster analysis of the DW images
alone. The
result of these two clustering steps were then used as a mask input to a
processed
difference image generated from the pre- vs. post contrast enhanced T1-w
images to
obtain a final estimation of the glioma volume. In Figure 10, dark grey region
50 is the
result of a cluster analysis as an overlay on a T2-w image. The patient was
diagnosed with
CA 02581904 2007-03-08
17
a low-grade astrocytoma. Note the exclusion of both the middle cerebral artery
and the
cystic components in the centre of the glioma.
The glioma volumes as identified by the process described above were compared
to the
glioma volumes independently measured by three experienced neuroradiologists
blinded to
the histopathologic diagnosis. The glioma ROI's were determined from rCBV maps
as
overlays on the anatomical MR images. Glioma grading was then performed using
the
histogram method according to an embodiment of the invention, which assesses
the
maximum normalized peak height of nCBV distribution from the total glioma
volume,
under the hypothesis that a low peak implies a wide distribution of nCBV
values illustrating
the heterogeneity of a high-grade glioma. To determine the level of
interobserver
reproducibility, the results from the independent observers were compared to
the results
from the cluster analysis using a Mann-Whitney test and a coefficient of
variation test. All
image analysis was performed using nICETM (NordicImagingLab, Norway).
Of the thirty-five gliomas investigated, fourteen were histologically
confirmed to be high-
grade (eleven glioblastoma multiforme [grade IV] and three anaplastic
astrocytomas or
oligodendrogliomas [grade III]). Of the twenty-one low-grade gliomas, three
were pilocytic
astrocytomas [grade I] and eighteen were astrocytomas, oligodendrogliomas or
mixed
oligoastrocytomas [grade II]. The peak nCBV distribution values of the
oligodendrogliomas
did not differ from the astrocytomas. All three observers obtained
statistically significant
higher histogram peak values for the low-grade gliomas compared to the high-
grade
gliomas (Mann-Whitney; p=0.002, p=0.004 and p=0.003). The cluster analysis
method
gave a more significant difference between the two cohorts (p=0.001).
Figure 11 shows mean nCBV peak values with standard deviations for low-grade
(black)
and high-grade (light grey) gliomas obtained by observers (OBS) and cluster
analysis.
Note the reduced relative standard deviation obtained by using cluster
analysis compared
to the manual selection of total glioma volume (High-grade cohort: 0.34, 0.40,
0.37 for
the observers and 0.30 for cluster analysis. Low-grade cohort: 0.38, 0.37,
0.44 for the
observers and 0.32 for cluster analysis).
The above study enables the embodiment applying an automated, user independent
method to improve delineation of true glioma volume. The method utilizes all
available MR
data generated in a standard CNS tumor protocol, thereby increasing the
likelihood of
correct tumor identification in the selection of tumor regions to be applied
in the histogram
analysis. Although the proposed method has so far been tested in a limited
number of
patients only, the preliminary results suggest that this method provides a
more objective
and robust approach compared manual identification of glioma volume.
CA 02581904 2007-03-08
18
In the following, one application of present invention is described in
relation to evaluating
whether oligodendroglial tumors with combined loss of short arm of chromosome
1p (-1p)
and long arm of chromosome 19q (-19q) influences the result in glioma grading
from MR-
derived cerebral blood volume maps. It is a well known problem with the hot-
spot method
of the prior art that most oligodendroglial tumors exhibit a higher hot spot
value than
astrocytomas irrespective of WHO grade. It has been suggested that the -lp/-
19q
genotype might be the reason for this, consequently leading to an inconclusive
hot spot
grading result. In the study presented in the below, the -lp/-19q genotype
influence on
grading using the hot-spot method and grading using an embodiment of the
invention are
compared.
Twenty-two patients with histologically confirmed oligodendrogliomas and
oligoastrocytomas (aged 9-62 yrs, mean age 43; 10 males, 12 females) have been
included. Loss of heterozygosity (LOH) at lp and 19q were analyzed using a
standard
polymerase chain reaction (PCR) technique. Imaging was performed at 1.5 T
(Siemens
Sonata or Avanto, Germany) prior to surgery. rCBV maps were generated using
established tracer kinetic models applied to the first-pass data obtained by
i.v. bolus
injection of 0.1 mmol/kg of Gadovist (Schering AG, Germany). The time
resolution of the
first pass gradient echo (GRE)-EPI sequence was 1.5s and the voxel size was
1.8x1.8x6.5mm3. Normalized (n)CBV maps were created by dividing each rCBV
value in
each slice with a white matter rCBV value obtained from an contra-lateral
unaffected
region. An experienced neuroradiologist was blinded to the histopathological
diagnosis and
defined the glioma areas based on the anatomical images (combined with rCBV
maps) by
drawing freehand regions of interest (ROI's) in each slice. Large tumor
vessels were not
included in the ROI's. The histogram analysis method according to an
embodiment of the
invention (histogram method) was used to assess the maximum normalized peak
height of
nCBV distribution from the obtained total glioma volumes, under the hypothesis
that a low
peak implies a wide distribution of nCBV values illustrating the heterogeneity
of a high-
grade glioma. For each glioma, a hot spot (rCBV max) value was also selected
using a 16
pixel ROI. All image analysis was performed using nICETM (NordicImagingLab,
Norway).
The results from the histogram method were compared to the results from the
hot spot
method using a Mann-Whitney test and a coefficient of variation test.
The -lp/-19q genotype was found in 9 of the twenty-two included tumors. Four
gliomas
were histologically confirmed as high-grade (grade III), which included two of
the nine -
lp/-19q genotypes. Figures 12A and B show examples of nCBV maps of low-grade
oligodendroglial tumors (indicated by the white circles) with and without the -
lp/-19q
genotype. The rCBV maps are overlaid on T2-w SE images; Figure 12A shows a low
grade
CA 02581904 2007-03-08
19
oligodendroglioma with -lp/-19q genotype. Figure 12B shows a low-grade
oligodendroglioma without -lp/-19q genotype. Note the low rCBV values in B,
typical of
gliomas without -lp/-19q genotype.
Both the histogram method and the hot spot method were able to differentiate
between
the low-grade oligodendroglial tumors with and without the -lp/-19q genotype
(p=0,003
[histogram] vs. p=0,02 [hot spot]). Figure 13 shows examples of this when
using the
histogram method. Figure 13 shows the distribution of normalized nCBV values
from total
glioma volume in four low-grade oligodendroglial tumors without -lp/-19q
genotype
(curves 1-4) and four with -lp/-19q genotype (curves 5-8). Note the lower peak
values of
the gliomas with the -lp/-19q genotype attributed to increased vascular
heterogeneity.
Neither method showed any difference between the four high-grade gliomas, of
which two
had -lp/-19q genotype. Both methods achieved a statistical significant
difference between
the low-grade gliomas without the -lp/-19q genotype and the high-grade gliomas
(p=0,008 [histogram] vs. p=0,05 [hot spot]), whereas neither could
differentiate between
the low-grade gliomas with the -lp/-19q genotype and the high-grade gliomas.
Including
both oligodendroglial tumors with and without the -lp/-19q genotype in the low-
grade
cohort, only the histogram method achieved a statistically significant
difference between
the high- and low-grade gliomas (p=0,04). The coefficient of variation was
lower for the
two low-grade cohorts using the histogram method compared to the hot spot
method (with
-lp/-19q genotype: 0,33 [histogram] vs. 0,44 [hot spot], without -lp/-19q
genotype:
0,16 [histogram] vs. 1,31 [hot spot]).
The results suggest that the presence of -lp/-19q genotype in oligodendroglial
tumors
strongly influence the results of giioma grading from MR-derived nCBV maps.
The hot spot
method was less specific than the histogram method for grading high- and low-
grade
gliomas in the presence of -lp/-19q genotypes, A reason for this might be that
even
though the low-grade -lp/-19q genotypes show signs of increased vascularity,
the
distribution of nCBV values in the total glioma volume is still relatively
homogeneous.
These preliminary results suggest that the histogram method according to an
embodiment
of the invention provides a more robust approach to glioma grading than the
traditional
hot spot method.
CA 02581904 2007-03-08
Appendix
Table 1: Patent demographics, histological diagnosis, surgical procedure and
MR findings.
(D U
0
U-
OC
C
C
0- E U N f-- C
U ~ a1 V t0 u C
x 0 ~ L ~ O ~ C
_ (0 N N 4)
~n Q in 0 ~n c 0 C 2: z o 0
4 41 M GB IV Biopsy extensive extensive yes yes diffuse
5 34 M DA II Biopsy moderate extensive yes no diffuse
6 41 F OA II Resection none minimal no no diffuse
8 61 F OD II Resection none moderate no no diffuse
9 35 F OA II Resection none moderate no no diffuse
12 27 M OA II Biopsy none moderate no no evident
17 75 F GB IV Resection extensive moderate yes no diffuse
18 65 F GB IV Resection extensive extensive yes no diffuse
20 42 M OD II Biopsy moderate extensive no no diffuse
22 50 M DA II Resection none moderate no no diffuse
24 F DA II Biopsy none extensive no no evident
27 62 M AOD III Resection moderate moderate no no diffuse
28 48 M AOA III Resection extensive moderate no no diffuse
29 53 F GB IV Resection extensive moderate yes no diffuse
31 67 M GB IV Resection extensive moderate yes no evident
33 64 M DA II Biopsy none moderate no no diffuse
39 17 M DA II Resection none extensive yes no diffuse
42 18 F AA III Resection none moderate no no diffuse
45 53 M OD II Biopsy none minimal no no diffuse
46 62 M GB IV Resection extensive moderate no no evident
50 43 F OA II Resection none extensive no no diffuse
52 58 F GB IV Resection extensive moderate yes no evident
54 43 F AOA III Resection extensive extensive no no diffuse
60* 54 F GB IV Biopsy moderate extensive yes no diffuse
63 9 M OA II Resection none minimal no no diffuse
64 57 F OA II Resection moderate moderate no no diffuse
66 43 M OA II Resection none moderate no no evident
67 51 M DA II Resection extensive extensive yes yes diffuse
CA 02581904 2007-03-08
21
68 25 M PA I Resection extensive extensive yes no diffuse
70 14 M PA I Resection extensive extensive yes no diffuse
74 52 M GB IV Resection extensive extensive yes no diffuse
81 39 F OA II Resection extensive moderate no no evident
84 60 F GB IV Resection extensive extensive yes no diffuse
85 57 M GB IV Resection extensive extensive yes yes diffuse
88 13 M OD II Resection moderate moderate no no diffuse
89 76 F AA III Biopsy none moderate no no diffuse
91 55 M GB IV Resection extensive extensive yes yes diffuse
92 53 M GB IV Resection extensive extensive yes yes diffuse
95 68 M GB IV Resection extensive moderate yes no diffuse
96 36 F OD II Biopsy none moderate no no diffuse
97* 6 F PA I Resection extensive extensive no no evident
98 36 F OA II Resection none extensive no no diffuse
99 61 M GB IV Resection extensive extensive yes no diffuse
101 49 F GG II Resection extensive minimal yes no evident
102 68 M GB IV Resection extensive moderate yes no diffuse
108 59 M GB IV Resection extensive extensive yes no diffuse
109* 44 F OA II Resection none minimal no no diffuse
117* 40 M OD II Resection extensive minimal no no diffuse
120 28 M DA II Resection moderate extensive no no diffuse
122 66 F GB IV Resection moderate moderate no no diffuse
HD=Histopathologicai Diagnosis, GB=Glioblastoma, DA=Diffuse Astrocytoma,
OA=Oligoastrocytoma, OD=Oligodendroglioma, AOD=Anaplastic
Oligodendroglioma, AOA=Anaplastic Oligoastrocytoma, AA=Anaplastic
Astrocytoma, PA=Pilocytic Astrocytoma, GG=Gangliogiioma
* HD was obtained prior to MR exam
CA 02581904 2007-03-08
22
Table 2: Mean values with standard deviations for high- and low-grade gliomas,
optimal
cut-off values and results of the Mann-Whitney test for the hot-spot method,
the WT
method and the histogram method.
~-
o ~
.-i .. N ~-. M .. CY .. ~ U' C:
= (D
~ ~ " " ~
n) c a) c Q) c ~ c E(7 c ~ C7
~ U) ~ a~i O rcn >
O 0 2: 0 2: 0 2: O_j
LGG; 2.85 3.21 3.84 3.08
Hot-spot (1.55) (1.90) (1.48) (1.63)
4.37-5.53 <.006
HGG; 7.04 5.72 7.37 7.49
Hot-spot (3.61) (3.64) (3.37) (4.04)
1.85 1.79 1.79 1.70
LGG; WT (0.53) (0.61) (0.53) (0.53)
2.20-2.44 <.001
2.98 3.06 2.94 2.70
HGG; WT (0.93) (1.30) (0.96) (0.92)
LGG; Hist. 0.86 0.87 0.87 0.90
5-bins (0.10) (0.12) (0.11) (0.10)
0.74-0.80 <.001
HGG; Hist. 0.62 0.65 0.66 0.39
5-bins (0.17) (0.18) (0.17) (0.10)
LGG; Hist. 0.59 0.63 0.63 0.65
10-bins (0.13) (0.16) (0.15) (0.15)
0.47-0.51 <.001
HGG; Hist. 0.39 0.39 0.41 0.43
10-bins (0.10) (0.12) (0.12) (0.14)
LGG; Hist. 0.49 0.50 0.50 0.52
15-bins (0.11) (0.13) (0.12) (0.11)
0.37-0.41 <.001
HGG; Hist. 0.29 0.29 0.31 0.31
15-bins (0.09) (0.10) (0.11) (0.12)
LGG; Hist. 0.34 0.36 0.37 0.39
25-bins (0.10) (0.13) (0.13) (0.13)
0.24-0.28 <.001
HGG; Hist. 0.19 0.19 0.20 0.21
25-bins (0.07) (0.07) (0.08) (0.09)
CA 02581904 2007-03-08
23
LGG; Hist. 0.28 0.29 0.30 0.31
35-bins (0.10) (0.12) (0.13) (0.12)
0.19-0.26 <.001
HGG; Hist. 0.14 0.14 0.15 0.15
35-bins (0.05) (0.05) (0.06) (0.07)
LGG; Hist. 0.21 0.22 0.23 0.24
50-bins (0.08) (0.09) (0.10) (0.10)
0.14-0.15 <.001
HGG; Hist. 0.10 0.11 0.11 0.12
50-bins (0.04) (0.04) (0.04) (0.05)
LGG; Hist. 0.13 0.13 0.14 0.15
100-bins (0.07) (0.07) (0.08) (0.08)
0.08-0.09 <.001
HGG; Hist. 0.06 0.06 0.06 0.06
100-bins (0.02) (0.02) (0.03) (0.03)
LGG=Low-Grade Gliomas, HGG=High-Grade Gliomas, SD=Standard deviation
* Range of values derived from binary logistic regression
CA 02581904 2007-03-08
24
Table 3: Sensitivity ancl specificity values, inter-observer agreement
percentage and Mann-
Whitney test results for the hot-spot method, the WT method and the histogram
method.
* c > c -~
>-
~_ 4~
E Q
:cn ~ v
Cl O C C
C ~ 4J ~ ~ ~ C ~ C Q_1
Q) \ {1 \ Q1 \ i
l!) CD O
Hot-spot method .010 < P >
55-75 81-94 68
(opt. cut-off value) .457
WT method 61 - 80 72 - 88 82 .036 < P > .422 < P >
.054 .842
.010<P> .592<P>
Histogram 5-bins 57 - 77 76 - 90 80
.022 .895
.008 < P > .737 < P >
Histogram 10-bins 68 - 86 63 - 84 70
.038 .895
.003 < P > .255 < P >
Histogram 15-bins 63 - 82 71 - 87 84
.008 .948
.001 < P > .180 < P >
Histogram 25-bins 69 - 87 68 - 85 82
.008 .842
.000<P> .141<P>
Histogram 35-bins 70 - 87 72 - 88 72
008 .463
.000 < P > .203 < P >
Histogram 50-bins 70 - 87 73 - 89 80
.008 .641
.000<P> .141<P>
Histogram 100-bins 74 - 90 77 - 91 88
.001 .505
Hot-spot
92-99 14-30 78 - -
(1.75 cut-off value)
* Range defined by 95 percent confidence intervalls
** Percent of total patient population in which all observers reported the
same
grading result
*** Grade II oligodendroglial tumors vs grade II astrocytomas
