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This article was downloaded by: [Canadian Research Knowledge Network]On: 27 November 2009Access details: Access Details: [subscription number 770885181]Publisher Informa HealthcareInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
British Journal of NeurosurgeryPublication details, including instructions for authors and subscription information:http://www.informaworld.com/smpp/title~content=t713407147
The role of advanced MR imaging in understanding brain tumourpathologyStephen J. Price aa Academic Neurosurgery Division, Department of Clinical Neurosciences, Addenbrooke's Hospital,Cambridge, UK
To cite this Article Price, Stephen J.'The role of advanced MR imaging in understanding brain tumour pathology', BritishJournal of Neurosurgery, 21: 6, 562 575To link to this Article: DOI: 10.1080/02688690701700935URL: http://dx.doi.org/10.1080/02688690701700935
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REVIEW ARTICLE
The role of advanced MR imaging in understanding brain tumourpathology
STEPHEN J. PRICE
Academic Neurosurgery Division, Department of Clinical Neurosciences, Addenbrookes Hospital, Cambridge, UK
AbstractAlthough MRI is the imaging modality of choice for brain tumours, the standard clinical sequences cannot tell us aboutcertain features of brain tumours. Improvements in imaging technology now allow advanced sequences, once usedexclusively for research, to be used clinically. Assessment of brain tumours with diffusion weighted MR (a marker ofcellularity), diffusion tensor MR (shows integrity of surrounding white matter tracts), perfusion MR (marker of tumourvascularity and angiogenesis), MR spectroscopy (showing tumour metabolism) and functional MR (to identify eloquentcortex) provide information that is complementary to the structural information. These techniques can be used to improveidentification of the tumour margin, tumour grading, reducing surgical risk and assessing the response to therapy. It isimportant for the neurosurgeon to understand what information can be obtained from these sequences, and that they ensurethey are used to further develop the assessment and management of brain tumours.
Key words: Diffusion MRI, functional MRI, gliomas, magnetic resonance imaging, MR spectroscopy, perfusion MRI,response to therapy, tumour grading.
Introduction
The excellent soft tissue contrast provided by
magnetic resonance imaging (MRI), and the range
of sequences that can explore differences in the
biophysical properties of the brain and tumours, has
made MRI the imaging mode of choice for the
assessment of brain tumours. Although MRI has
improved our visualization of these tumours, there
are a number of areas where it fails to provide us with
sufficient information. In particular, there are four
areas where further information would be very useful.
The first is the inability of conventional imaging to
show tumour infiltration. Infiltrating gliomas cells
extend beyond the limits of both the enhanced T1-
weighted and T2-weighted abnormalities.1 3 Our
inability to detect these cells makes it impossible to
deal with these regions during tumour resection and
results in a margin to be indiscriminately added to
radiotherapy treatment volumes to account for these
infiltrating cells. As this margin includes normal
brain, the total dose used has to be reduced to reduce
the risk of radiation necrosis. As a consequence,
gliomas recur within the treatment volume in the
majority of patients.4,5 Better delineation of the
tumour margin would certainly improve radiotherapy
planning.
A second area where conventional MR is deficient
relates to tumour grading. Although histology is the
gold standard in characterizing brain tumours, even
image-guided biopsies have an appreciable morbidity
and mortality. The heterogeneity of these tumours
also introduces problems with sampling error under
grading the tumour.6 8 For all high grade tumours
MRI can correctly classify them with 65% sensitivity
and 95% specificity.9 For low grade gliomas studies
suggest that only half could be correctly classified
using MRI10 and that one-third of non-enhancing
tumours are in fact high grade gliomas.11
A third problem with conventional MR is that it is
often insensitive to detecting subtle changes in
tumours due to the effects of treatment. There is
now evidence that the early detection of tumour
recurrence at an asymptomatic stage is associated
with improvements in patient survival.12 At present
the methods of assessing response, all based on
structural imaging, rely on changes in tumour size.
This is particularly difficult for the newer cytostatic
therapies where successful treatment may not be
accompanied by much change in tumour size. The
delays in determining that a treatment has failed may
result in the patient being too ill to be considered for
second-line therapies. Manual inspection, the stan-
dard method of neuroradiology reporting, is made
Correspondence: S. J. Price, Academic Neurosurgery Division, Department of Clinical Neurosciences, Box 167, Addenbrookes Hospital, Cambridge, CB2
2QQ, UK. Tel: 01223 336935. Fax: 01223 216926. E-mail: [email protected]
Received for publication 11 September 2007. Accepted 24 September 2007.
British Journal of Neurosurgery, December 2007; 21(6): 562 575
ISSN 0268-8697 print/ISSN 1360-046X online The Neurosurgical FoundationDOI: 10.1080/02688690701700935
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more difficult with the large amount of information
presented to the radiologist in the form of multiple
pulse sequences in different planes. In addition, the
irregular tumour margin and differences in position-
ing can make interpretation even more difficult.
Recent studies suggest that even in the hands of
experts, volume changes up to 59% may not be
appreciated.13 Simple one-dimensional (e.g. the
RECIST criteria)14 or two-dimensional methods
(such as the MacDonald Criteria15) have been used
for a more objective assessment of response, but again
these can be insensitive to subtle changes because
measurements are only made in the imaging axis and
the irregular margin makes measurements difficult. In
addition, changes in volume may only have a slight
effect on tumour diameter; an increase in tumour
volume of 145%, for example, is only associated with
an increase in the tumour radius of 2.3 mm.13 For
brain tumours changes in area do not correlate with
changes in survival,16,17 but do correlate with the time
to progression for enhancing tumours.17 Volumetric
methods can overcome some of these problems, but
are time consuming, subject to bias, and have poor
inter-observer agreement.18 Automated volumetric
methods have problems with defining the tumour
margin. In addition, outcome may not relate to
changes in tumour volume with treatment.17,19,20
The final problem with conventional techniques is
the poor identification of the relationship of eloquent
cortex or white matter tracts to the tumour. Slow
growing tumours frequently distort these regions and
may include functional tissue that is at risk during
surgery.21,22 Structural imaging is not able to identify
these regions with the degree of confidence required
to reduce the risk during surgery.
Until recently, the assessment of brain tumours
with MRI has focused on structural changes. Over
the last few years newer sequences have been
developed that allow us to study pathological and
biological changes within tumours. These techni-
ques, initially used as research tools are now being
transferred to the clinical arena. In this review I will
explore the potential use of five of the most
commonly used methods, namely diffusion weighted
imaging (DWI), diffusion tensor imaging (DTI),
perfusion imaging, MR spectroscopy (MRS) and
functional MRI (fMRI), and explain how these
methods can provide clinicians with more informa-
tion to improve the management of brain tumours.
Diffusion weighted imaging (DWI)
Principles of diffusion weighted imaging
DWI is dependent on the movement of protons in
water. A proton that experiences the same magnetic
field will spin at the same rate. If a pulsed gradient is
applied, the proton will spin at different rates
depending on the strength, duration and direction
of the gradient. If a second pulse gradient is then
applied, the protons will be refocused. Where
protons have not moved this rephasing will be
complete and the signal will be unchanged. Where
protons have moved between the two pulses there
will be a loss of signal that is dependent on the degree
of diffusion weighting, referred to as the b-value, and
the diffusion coefficient. As a result, where there is
free water movement, for example, CSF or in areas of
vasogenic oedema, there is a drop in signal, where
there are regions of water trapping, for example
within swollen cells in areas of cytotoxic oedema,
there is an increase in signal.
Since the signal change with DWI is also depen-
dent on the underlying T2-weighted signal, it is not
possible to use signal changes alone to quantify
diffusion processes. Instead the gradient of the signal
intensity due to different b-values is plotted. This
coefficient, a measure of all motional processes such
as diffusion and flow, is called the apparent diffusion
coefficient (ADC). The ADC measures water diffu-
sion and, therefore, often mirrors changes in DWI
signal. In areas of increased diffusion (e.g. vasogenic
oedema) there is an increase in ADC. In areas of
restricted water diffusion (e.g. cytotoxic oedema),
ADC decreases.
The role of diffusion weighted imaging in tumours
One of the commonest uses of DWI for the
assessment of tumours is differentiating between
cystic lesions. One particular use is distinguishing
between epidermoids (where the thick content of the
cyst restricts the diffusion of water), and arachnoid
cysts where diffusion is free.23 Similarly, the viscous
content of abscesses can be differentiated from cystic
tumours.24 26
For gliomas the DWI changes are more complex.
Experimental studies suggest that the main determi-
nant of the ADC is the extracellular volume
fraction.27 In tumours there are two processes that
can affect the extracellular volume fraction. First,
there is the vasogenic oedema produced due to
defects in the blood brain barrier. As this increases
the volume of the extracellular volume fraction, it
causes a decrease in the DWI signal and an increase
in the ADC. An example is shown in Fig. 1. These
changes occur even in areas of solid tumour. The
second effect is due to the increased cellularity seen in
tumours. Increased cell numbers will restrict diffu-
sion, reduce the extracellular volume fraction and
would increase DWI signal and reduce the ADC.
Most studies have shown that tumours have higher
ADC values compared with normal brain.24,28 36
The regions with the highest ADC values are within
cysts or areas of necrosis. Some studies have shown
that there is an inverse relationship between cellularity
and ADC.31,37 Although other studies have failed to
demonstrate this, there is good evidence that densely
cellular tumours (e.g. lymphomas) have a lower ADC
than other tumour types.31
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Diffusion tensor imaging (DTI)
Principles of diffusion tensor imaging
For DWI it is assumed that water diffuses in all
directions, i.e. diffusion is isotropic. This is not the
case in the brain where water diffuses preferentially
along white matter tracts. This directional diffusion,
called anisotropic diffusion, is due to the presence of
both intracellular barriers (e.g. neurofilaments and
organelles) and extracellular barriers (e.g. myelin
sheaths and glial cells). In this situation a water
molecule could be described as diffusing in a volume
that is described by an ellipse. Mathematically,
ellipses can be described by a tensor. These tensors
can be described by three eigen values that describe
the magnitude of the axes of the ellipsoid direction,
and three eigen vectors that describe their direction.
To quantify the tensor the eigen values are used to
calculate rotationally invariant indices. A number of
these measures exist; the most commonly used in the
literature include the mean diffusivity (D the mean
of the eigen values) as a measure of isotropic
diffusion and the fractional anisotropy (FA) as an
anisotropic measure. An example of this is in a
normal volunteer is shown in Fig. 2. The eigenvec-
tors can be used to provide directional information.
The direction of the largest eigen value, referred to as
the principle axis of diffusion, can be colour-coded to
provide directionally encoded colour maps, so that
fibres passing in the x direction are coloured red,
those in the y direction coloured green and those in
the z direction coloured blue.38 In addition, by
following these eigen vectors on a voxel-by-voxel
basis it is possible to display the direction of the white
matter tracts using tractography.
Diffusion tensor imaging in tumours
In tumours there is a reduction in diffusion
anisotropy, caused by the disruption of normal brain
architecture with loss of tissue organization, destruc-
tion of axonal processes, widening extracellular space
and changes in cell size. The reduction in fractional
anisotropy correlates with cell density39,40 and the
proliferation index of the glioma.39 One of the main
roles of DTI is to study the effects the tumour has on
surrounding white matter tracts.41 Where a tumour
has displaced a white matter tract it has a different
colour hue on the directionally encoded colour maps
with normal FA values compared with contralateral
side. Where the tumour is infiltrated the FA is
reduced compared with the contralateral side and the
colour hues are abnormal. Where tumour has
completely disrupted a tract then it is not identifiable
on either FA or directionally encoded colour maps.
An example is shown in Fig. 3.
Perfusion MR imaging
There are three main methods to study brain
perfusion.
Dynamic susceptibility contrast imaging (DSCI)
This is the most widespread method of perfusion
imaging and is likely to be a standard sequence on
most MR machines. It relies on the T2* signal drop
caused by the passage of a gadolinium-containing
contrast agent through the tissues. The drop in signal
is proportional to the concentration of the contrast
agent and the tissue vascularity. An example of the
FIG. 1. A MR study from a 74 year old woman who presented with a homonymous hemianopia. Contrast enhanced T1-weighted image
shows a left occipital tumour. The diffusion image shows increased in signal centrally with a reduction due to the vasogenic oedema. This is
shown as high signal on the ADC map. On the FA map, the tumour has destroyed the white matter in the occipital region.
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FIG. 3. Differentiating white matter infiltration from compression. In the upper row there is a reduction in the FA within the white matter
tract adjacent to the tumour (arrowed) due to tumour infiltration. In the low row the FA appears normal but the colour map shows a different
colour compared to the contralateral side. This is a feature of white matter compression.
FIG. 2. The diffusion tensor can be measured by using the magnitude of 3 eigenvalues. These can be used in equations to produce maps of
mean diffusivity and fractional anisotropy. These examples are from a normal volunteer.
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signal drop in different regions is shown in Fig. 4.
Simple, semi-quantitative measures such as the peak
height of the T2* relaxivity curve (the reciprocal of
signal drop) or the time to peak are frequently used to
provide a rough measure of perfusion, but cannot be
generalized to other MR machines or sequences. The
time-to-peak in particular is especially insensitive in
brain tumours since, unlike in infarcts, there rarely is
much of a delay in blood flow within tumours. More
quantitative measures involve the calculation of the
relative cerebral blood volume (rCBV)defined as
the volume of blood in the voxel/mass of tissue
within the voxel. This can be calculated by the area
under the relaxivity curve (Fig. 5). An example of an
rCBV map is shown in Fig. 6 in a patient with a
glioblastoma. From this the relative cerebral blood
flow (rCBF) and mean transit time (MTT) can be
calculated.
One of the main problems with DSCI is that it is
not possible to produce absolute values as there is a
FIG. 4. An example of the dynamic susceptibility contrast effect. The graph shows the change in signal intensity taken from regions of interest
in the tumour (red), normal white matter (green) and normal grey matter from the contralateral thalamus (blue). There is a reduction in the
signal intensity after the bolus is given (arrow). For the tumour this drop in signal intensity is greater than that of either of the normal tissues
and this drop last for longer. The drop in signal intensity is greater for grey matter than white matter, and is mirrored by the increased blood
flow to grey matter.
FIG. 5. A graph showing the changes of T2*-relaxivity (reciprocal of signal change) after a bolus of contrast passes through the tissues. Semi-
quantitative markers of this perfusion include the time to peak and the peak height. Calculating the area under the curve provides a more
robust marker, relative cerebral blood volume (rCBV). The difficult in determining rCBV is that due to leakage of contrast into the
extravascular space the curve does not return to baseline levels. Various computational methods have to account for this.
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non-linear relationship between the signal change
and gadolinium contrast. As a result, all values are
usually reported relative to the contralateral white
matter. One assumption made is that all the contrast
remains within the vessel. This is not the case; in
Fig. 5 it can be seen that the baseline relaxivity does
not return to normal. Various computational meth-
ods are employed to account for this.
Dynamic contrast enhancement (DCE)
This method uses a rapid T1 sequence to measure
changes in signal intensity as a bolus of gadolinium
diffuses across the damaged blood-brain barrier into
the extracellular, extravascular space (EES). By
applying a multicompartmental model42 it is possible
to calculate two parameters, the capillary transfer
coefficient (Ktrans) and the volume of the EES (ve)43.
Ktrans has several physiological interpretations de-
pending on the balance between permeability
and blood flow43. In situations of high capillary
permeability, flux across the capillary is flow limited,
so Ktrans reflects the blood plasma flow per unit
volume of tissue. In low permeability situations flux
is permeability limited, so Ktrans reflects the perme-
ability surface area product. In most tumours the
permeability is probably sufficiently high that Ktrans
reflects mainly capillary flow, but also some perme-
ability. This technique may be a useful method of
studying the microcirculation of tumours.
Arterial spin labelling (ASL)
This is a newer technique that uses an endogenous
contrast mechanism, whereby the blood flowing into
the brain is magnetically labelled (arterial spin
labelling). This technique is still largely research-
based and provides truly quantitative values of
cerebral blood flow. It has the advantage that it is
repeatable over a short time frame and it is possible
to label individual vessels to show their involvement
blood flow to the region.
As the development of a blood supply is crucial for
tumour growth, there is much interest in finding
methods that can image tumour vascularity. Evi-
dence suggests that the rCBV of tumours correlates
with tumour vascularity as assessed by non-quanti-
tative scales of histological vascularity,44,45 measures
of microvascular density46 and angiographic vascu-
larity.45
MR spectroscopy
Principles of MR spectroscopy
MR spectroscopy is based on the principle of
chemical shift. If we take two protons as an example,
one bound to an oxygen molecule with another
proton in water and one bound with many others to a
carbon molecule in fat, these protons will be in
different magnetic environments. These differences
are due to different degrees of chemical shielding on
the protons, so the water protons will spin slower
than those in fat. Rather than referring to the
frequencies in absolute terms, the frequency differ-
ences are used and expressed as a ppm scale where
the resonant frequency is related to a reference
frequency.
In theory, MR spectroscopy can be performed on
any nucleus that exhibits nuclear spin. Studies have
reported spectroscopic imaging using deuterium
(2D), 13C, 15N, 7L, 23Na and 19F using either
FIG. 6. An example of an rCBV map in a patient with a glioblastoma. There is obvious increased vascularity on the anterior and medial side of
the tumour. The central part shows very low vascularity suggesting this is either cyst or necrosis.
Advanced MR imaging and brain tumour pathology 567
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endogenous nuclei or exogenous compounds. As
these substances are present in very small quantities,
detection is very difficult on conventional clinical
scanners. Proton spectroscopy (1H) and phosphor-
ous spectroscopy (31P), however, are feasible on most
clinical 1.5 T MR machines. Phosphorous spectro-
scopy is largely limited to research applications
looking at tissue energy production. The low signal-
to-noise ratio means that at present the resolution
only allows assessment of large lesions or global
disorders. Proton spectroscopy acquisition sequences
require water suppression since the concentration of
water protons is over 10,000 times more than other
substances. Signals are localized either using single
voxel techniques, where a single volume of tissue is
excited, or more recently multiple voxel techniques
(also referred to as chemical shift imaging).
The proton spectra produced show different peaks
at particular resonant frequencies. An example of
such a spectrum is shown in Fig. 7. Although up to
30 compounds can be detected in the proton spectra
of the normal brain at 1.5 T, the most commonly
seen peaks are:
. N-acetyl aspartate (NAA): this provides the largestpeak of the spectra at 2.02 ppm. Immunocyto-
chemistry has shown that NAA is predominantly
localized to neurons.47 As it is reduced in
disorders that result in axonal loss it has been
labelled as a neuronal marker and used to
provide a measure of neuronal density and as a
surrogate marker of neuronal integrity.
. Choline (Cho): this signal found at 3.24 ppm, isactually made up from glycerophosphocholine
(GPC), phosphocholine (PC) and a small
amount of free choline. These choline com-
pounds are involved in membrane synthesis and
degradation. Increased choline occurs with dis-
orders causing increased membrane turnover.
. Creatine (Cr and PCr): this signal at 3.02 ppm ismade up from both creatine and phosphocrea-
tine. Both these compounds are involved in ATP
generation and energy metabolism. As the
amount of creatine and phosphocreatine appears
to be relatively constant in the normal brain, it is
used as a reference signal. The concentrations of
other metabolites are often expressed as the ratio
of the peak areas compared with the creatine
peak.
. Lactate: the peak of lactate at 1.33 ppm is usuallybelow the level of detection in the normal brain.
Increased lactate production occurs in disorders
of energy metabolism and an increase in non-
oxidative glycolysis.
. Glutamate and glutamine (Glx): are difficult toseparate at 1.5 T and often appear as a composite
peak at 2.1 2.4 ppm and at 3.6 3.8 ppm.
Elevated glutamate levels lead to excitotoxic cell
damage. Increased glutamine synthesis occurs as
a result of increased blood ammonia levels.
. Mobile lipids are found using short TEs unlessthey are grossly elevated. The produce a double
peak at 0.9 ppm (methyl lipid) and 1.3 ppm (as
methylene lipid). The latter peak can be difficult
to differentiate from lactate and can only be
demonstrated by either inverting the lactate peak
by using long TE MRS or lactate editing.48
Mobile lipids are not seen in normal brain but are
increased when membranes breakdown and lipid
droplets are formed in necrotic/perinecrotic
areas.49
Proton MR spectroscopy in brain tumours
The spectra seen in brain tumours differ greatly from
normal brain. The typical spectrum of a glioma shows:
. NAA: There is a marked reduction in the NAApeak due to the lack of viable neurons within a
tumour. Gliomas demonstrate a reduction in the
NAA/Cr ratio.51 53
. Choline: The choline peak is increased as a resultof increased membrane turnover. Most studies
expresses this increase in total choline as an
increase in the Cho/Cr and Cho/NAA ratios.51 55
. Creatine: The creatine peak has been assumed tobe constant yet in tumours methods that use
absolute quantification have shown that the total
creatine level are lower than the normal
brain.55,56
. Lactate: Within tumours there is disruption of thenormal glucose metabolism and a significant
degree of hypoxia, as a result there are elevated
lactate levels within tumours.52,53
. Mobile lipids: As mentioned above, these are seenwhen lipid droplets form in the perinecrotic/
necrotic areas.57 Lipid is therefore commonly
seen in malignant tumours and is associated with
the degree of malignancy and presence of
necrosis.51,53FIG. 7. An example of a spectrum from a normal brain.
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Studies comparing MRS with tumour pathology
suggest that the choline peak correlates with the cell
density,58,59 and the lipid peak appears to provide the
best marker of proliferation.58 More recent studies
have shown that using the Cho/NAA ratio correlated
with cell density, cell proliferation index and the ratio
of proliferating cells to dying cells.60
Functional MR Imaging (fMRI)
When an area of the brain is active there is a
corresponding increase in blood flow. This coupling
of blood flow and brain metabolism was first
described in 1890 by Sherrington and is the principle
behind fMRI. The increased blood flow is not
matched by an increase in oxygen extraction, so the
concentration of deoxyhaemaglobin is reduced.
Since deoxyhaemaglobin is paramagnetic there is a
change in the T2* signal. This change, referred to as
blood-oxygen level dependent contrast (BOLD), is
detected by the MR sequence.
To produce reproducible results, activation para-
digms are devised to allow specific functions to be
assessed. It is possible to assess any brain activation,
but in practice only assessment of motor function
and language are considered clinically. Motor activa-
tion is stimulated by movement of the relevant part of
the body, interspersed with rest. Studies comparing
motor fMRI to direct cortical stimulation in awake
patients have found excellent correlation.61,62 Lan-
guage activation is more problematic as this is a more
complicated series of processes that involve many
brain regions. The accuracy of fMRI in mapping
language tasks is poorer than motor studies and
usually will require the assessment of multiple
language tasks.63 For bilingual patients there is
evidence that there are different areas responsible
for each language function, and this needs to be
accounted when language is being assessed.64
Problems with fMRI for presurgical planning
There are a number of problems with fMRI for
presurgical planning. The first is that it shows areas
that are activated but not necessarily essential to
avoid causing a neurological deficit. The second is
that the BOLD signal is lost adjacent to gliomas.65,66
This is probably due to the loss of autoregulation in
these vessels and can also be seen following previous
surgery.67 Care also must be taken with lesions that
appear to be adjacent to areas of activation. Lesions
closer than 5 mm to the motor cortex, for example,
are associated with much higher incidence of
neurological deficit.68 This probably reflects injury
to cortical vessels that supply either side of a gyrus. A
further problem is that fMRI provides no information
regarding the relationship of white matter tracts to
the tumour. Studies have shown that it is feasible to
combine fMRI with DTI to accurately identify both
areas of cortical activation and white matter
tracts,69 72 although intraoperative MR studies have
suggested marked shifts in white matter tracts
intraoperatively making accurate detection of these
tracts a problem.73 The final problem relates to the
poor intrasubject repeatability of fMRI. For lan-
guage paradigms this repeatability is, at best, 40%.74
Motor activation has better repeatability with the
centre of the activated area varying by about 4 mm,
but the size of the regions activated by a finger
tapping task varies by about 50%, even when
repeated after only 30 min.75 More work is needed
to improve paradigm design to improve the repeat-
ability of tasks.
Using these techniques to improve the
management of gliomas
It is clear from the previous sections that these new
techniques are sensitive to some of the pathological
processes that occur in gliomas. These new se-
quences can provide information on tumour cellu-
larity (DWI), the effect of tumours on white matter
tracts (DTI), tumour vascularity (perfusion MR) and
tumour metabolism (MRS). Their main use maybe
to overcome some of the problems that conventional
imaging cannot tackle.
Identifying occult tumour infiltration
It has been known for many years that gliomas
preferentially grow along white matter tracts. As DTI
is sensitive to subtle disruption of white matter tracts,
attempts have been made to differentiate them from
non-invasive tumours (e.g. meningiomas and metas-
tases). The results appear mixed with some studies
showing a larger reduction of FA in the peritumoural
region76 78 for gliomas, while other studies only
showing significant increases in mean diffusivity.79,80
These results probably reflect the recent concerns
that FA may not be a particularly sensitive measure
of anisotropic diffusion81,82 and is insensitive to
detecting occult white matter infiltration. Using
other methods of analysing the diffusion tensor,
differences can be identified.82 85
Attempts have been made to correlate DTI
abnormalities with histology. In one study the FA
and mean diffusivity values inversely correlated with
the total number of tumour cells and the ratio of
tumour to normal cells.40 In another study that used
an analysis method that split the diffusion tensor into
pure isotropic and anisotropic components, DTI
could correctly identify tumour infiltration with an
overall sensitivity of 98% and specificity of 81%.86
Using this method it is possible to define a region
that corresponds to the tumour margin.86 A retro-
spective study suggests that the arrangement of this
region can predict the pattern of glioma recurrence.87
This study needs repeating prospectively in a larger,
more homogeneous cohort of patients, but might
allow a degree of individualizing treatment on the
Advanced MR imaging and brain tumour pathology 569
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basis of potential recurrence pattern. Attempts are
being made to plan radiotherapy on the basis of this
data.88
As the MR spectra of tumours differ from normal
brain, studies have attempted to use this to determine
tumour margins. The region of oedema surrounding
a tumour has a similar spectroscopic pattern as the
centre of the tumour with increased choline peaks
and reduced NAA as compared with normal brain.89
Some of these areas of oedema identified on T2-
weighted imaging have Cho/NAA ratios greater than
2, which is within the range seen with tumours.90 In
an image-guided biopsy study the Cho/NAA and
normalized choline ratios correlated with the degree
of tumour infiltration.52 Although spectroscopy was
better than conventional MRI at defining the tumour
margin, they were unable to differentiate between
normal brain and mild tumour infiltration. Using
these techniques it appears that MRS-derived
radiotherapy volumes extended beyond the T2-signal
abnormality in 60% of cases, and that these areas of
abnormality can predict the development of contrast
enhancement.
Combining DTI and MRS shows that where the
anisotropy is loss due to white matter infiltration,
there is a corresponding finding of reduced NAA and
increased Cho ratiosa metabolic profile of tumour
that correlates with the disruption of white matter
tracts.91 Combining these two techniques could
differentiate between white matter disruption due
to tumour and disruption due to oedema alone.
Tumour grading
As a tumour grade increases various pathological
processes occur within the tumour. There is a
marked increase in cellularity that is accompanied
by an increase in vascularity and the development of
necrosis. These tumours will demonstrate higher
metabolic rates, with increased cellular proliferation.
As these new techniques are able to non-invasively
study these processes, attempts have been made use
them to grade tumours.
Attempts have been made to use diffusion imaging,
as a measure of cellularity, to grade gliomas. Studies
have shown that the ADC in high grade gliomas is
significantly lower than the less cellular low grade
gliomas.31,37 In both tumour grades the values for
individual patients overlapped precluding using this
technique for accurate preoperative diagnosis. More
recent studies suggest that an ADC value threshold of
1.06 1073 mm2/s showed that this was a strongprognostic marker with 14% of those with ADC
values below this alive at 2 years compared with 84%
of those with values above this threshold.92
In gliomas, microvascular proliferation is one of
the histological characteristics that can define a
tumour as a glioblastoma.93 Many studies have
attempted to use rCBV to provide a method to
non-invasively grading gliomas.44 46,94,94 98 At-
tempts to find a threshold that can differentiate
between high and low-grade gliomas have been
hampered by the use of different acquisition techni-
ques and methods of reporting the rCBV. The use of
a spin echo (SE) T2* technique tends to give a lower
ratio than using a gradient echo (GE) technique.97,99
Studies using SE sequences suggest that a threshold
of 1.5 can differentiate between high and low grade
gliomas.44,100 Published thresholds for GE sequences
depend on whether the aim is to increase specificity96
(using a ratio of 3.57) or increase sensitivity54 (where
a ratio of 1.75 was suggested). A ratio of 2.93 appears
to maximize both specificity and sensitivity.96
For low grade gliomas, rCBV measurements
appear to provide prognostic information. An rCBV
of 1.75 is a better predictor than histology alone for
predicting outcome and time to progression for low
grade tumours.101,102
There are a number of problems using perfusion
parameters to grade gliomas. The first is that
oligodendrogliomas have higher rCBV values than
astrocytic tumours.100,103 As a result, a low grade
oligodendrogliomas could be falsely graded as a
higher grade tumour. In fact further studies suggest
that a higher rCBV is predictive of chromosome 1p
deletions suggesting that this is associated with
increased neovascularization.104,105 Secondly, all
studies show marked overlap of rCBV with different
tumour grades45,95 97,106 making grading in an
individual patient inaccurate. In particular, it was
difficult to differentiate WHO Grade II vs. WHO
Grade III and WHO Grade III vs. WHO Grade IV
gliomas using these techniques.
A few studies have used T1 dynamic contrast
enhancement techniques to grade gliomas. The
tumour grade strongly correlates with permeability
and weakly correlates with fractional blood vo-
lume.107,108 Although there were significant differ-
ences between the tumour grades, there was still
marked overlap in values of permeability for indivi-
dual patients making exact grading difficult. A later
study in a larger cohort found that the permeability
values correlated well with the MIB-1 index.109 The
fractional vascular volume has been shown to differ
significantly between WHO Grades II and III, and
Grades III and IV.110 Studies that have compared
perfusion techniques have shown that Ktrans is less
sensitive and specific at tumour grading compared
with rCBV.94
Attempts to grade tumours using MR spectro-
scopy suggest that with increasing grade there is an
increase in the Cho/Cr ratio,50,51,53,54,58,89 a reduc-
tion in NAA51,53,54,58,89,90 and increase in lactate/
lipid peak.50,51,53,58 An example of spectra from
gliomas of different grades is shown in Fig. 8.
Murphy et al. found that the MRS findings agreed
with pathological diagnosis in 74% of cases.53
They found that in 17% there was a difference in
grade and in 9% there was a difference in cell of
origin. Overall, it was contributory in a total of
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80% of cases. Computerized automated pattern
recognition algorithms have now been developed
that can differentiate different tumour groups based
on MRS and these have been tested in a multicentre
setting.111 Using a normalized Cho/Cr ratio of over
1.08 had a 97.5% sensitivity, a 77% positive
predictive value and a 62.5% negative predictive
value for differentiating between high and low grade
gliomas.54 The use of such ratios for diagnosis in
individual patients, however, are complicated by
large variation in published values with marked
overlap of values between different tumour
types.29,50,51,54,58
MR spectroscopy is less accurate than perfusion
MR measures, but combination of both techniques
improves diagnosis.54 A similar finding has been
reported with combining MRS with diffusion
weighted MRI.29
Assessing response to therapy
One of the first changes that occur with successful
treatment is tumour cell death. Since it appears that
changes in tumour cellularity and tumour compo-
nents (especially the presence of necrosis) cause
changes in the ADC measurements, this might be a
useful tool to assess the response to therapy. In a
brain tumour model in rats, changes in ADC match
changes in tumour volume112 and cellularity.114
These changes also occur before any change in size
on T2-weighted imaging could be observed. These
techniques have been used to assess the response to
convection-enhanced delivery of taxol in three
patients with recurrent glioblastomas.114 Again,
ADC changes preceded any changes in gadolinium-
enhanced T1 or T2-weighted imaging. By plotting
the differences in ADC in patients undergoing
treatment for high grade gliomas it is possible to
create a functional diffusion map115 that can
predict response to therapy after only 3 weeks.
These techniques also show that there is
marked regional heterogeneity in response to
treatment.115,116
Perfusion imaging has been largely used in the
cancer literature to study the effect of anti-angiogenic
or antivascular drugs. It is now commonly used for
assessing early effects from antivascular drugs in
Phase I or II studies.117,118 It is likely that this will be
the standard method of assessing response to therapy
for these drugs once they are used in routine clinical
FIG. 8. MR spectra taken from normal brain, and different grades of astrocytomas. The normal brain has a large NAA peak that is greater
than the Cho peak. In the low grade gliomas the relative peak height is reversed with increased choline compared to NAA. As the degree of
malignancy increases, there is an increase in the Cho peak, a reduction in NAA and an increase in lactate and lipids. In the glioblastomas, the
presence of lactate and lipids dominates the spectrum. Figure taken with permission from Murphy et al., 2002.53
Advanced MR imaging and brain tumour pathology 571
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practice. Studies have also used perfusion imaging to
study the response to radiotherapy. A reduction of
rCBV during radiotherapy is a predictor for a better
prognosis.119 These changes can even be detected
after only one week of radiotherapy. Dose-related
reduction in perfusion can also be detected in
irradiated normal brain surrounding gliomas.120,121
This potentially may be a marker for normal brain
radiation injury.
MR spectroscopy has also been used to predict the
response to therapy. In a cohort of patients with high-
grade gliomas receiving radical radiotherapy (i.e.
60 Gy in 30 fractions), the lactate/NAA ratio was the
strongest predictor of response to radiotherapy and
overall survival.122 For patients with a ratio above 2,
the 1-year survival was 30% and no patient survived
more than 2 years. For patients with a ratio below 2,
the 1-year survival rate was 80%. In another study
looking to predict the response to tamoxifen che-
motherapy as part of a Phase II study, patients that
responded had a pretreatment increase in creatine
and NAA/Cr, but a lower lactate/creatine and lipid/
creatine ratios.
Conclusions
Improvements in MR technology mean that these
techniques can be performed on 1.5 T clinical
machines with acceptable imaging times. In addition
to the structural information from conventional MR
sequences, advanced MRI provides important in-
formation on tumour pathology and biology. It is
likely that these techniques will become an essential
tool in the assessment of brain tumours.
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