Advanced Mr Imaging in Understanding Brain Tumour Pathology

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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

Advanced MR imaging and brain tumour pathology 563

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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.

564 S. J. Price

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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.

566 S. J. Price

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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.


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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


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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

570 S. J. Price

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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


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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.

References

1 Lunsford LD, Martinez AJ, Latchaw RE. Magnetic resonance

imaging does not define tumor boundaries. Acta Radiol

1986;369(Suppl):154 6.

2 Johnson PC, Hunt SJ, Drayer BP. Human cerebral gliomas:

correlation of postmortem MR imaging and neuropathologic

findings. Radiology 1989;170:211 17.

3 Kelly PJ, Daumas-Duport C, Scheithauer BW, Kall BA,

Kispert DB. Stereotactic histologic correlations of computed

tomographyand magnetic resonance imaging-defined ab-

normalities in patients with glial neoplasms. Mayo Clin Proc

1987;62:450 9.

4 Oppitz U, Maessen D, Zunterer H, Richter S, Flentje M. 3D-

recurrence-patterns of glioblastomas after CT-planned post-

operative irradiation. Radiother Oncol 1999;53:53 7.

5 Chan JL, Lee SW, Fraass BA, et al. Survival and failure

patterns of high-grade gliomas after three-dimensional con-

formal radiotherapy. J Clin Oncol 2002;20:1635 42.

6 Glantz MJ, Burger PC, Herndon JE, et al. Influence of the

type of surgery on the histologic diagnosis in patients with

anaplastic gliomas. Neurology 1991;41:1741 4.

7 Jackson RJ, Fuller GN, bi-Said D, et al. Limitations of

stereotactic biopsy in the initial management of gliomas.

Neuro-oncol 2001;3(3):193 200.

8 McGirt MJ, Villavicencio AT, Bulsara KR, Friedman AH.

MRI-guided stereotactic biopsy in the diagnosis of glioma:

comparison of biopsy and surgical resection specimen. Surg

Neurol 2003;59(4):277 81.

9 Julia-Sape M, Acosta D, Majos C, et al. Comparison between

neuroimaging classifications and histopathological diagnoses

using an international multicenter brain tumor magnetic

resonance imaging database. J Neurosurg 2006;105:6 14.

10 Kondziolka D, Lunsford LD, Martinez AJ. Unreliability of

contemporary neurodiagnostic imaging in evaluating sus-

pected adult supratentorial (low-grade) astrocytoma. J Neu-

rosurg 1993;79:533 6.

11 Scott JN, Brasher PMA, Sevick RJ, Rewcastle NB,

Forsyth PA. How often are nonenhancing supratentorial

gliomas malignant? A population study. Neurology 2002;

59:947 9.

12 Galanis E, Buckner JC, Novotny P, et al. Efficacy of

neuroradiological imaging, neurological examination, and

symptom status in follow-up assessment of patients with

high-grade gliomas. J Neurosurg 2000;93:201 7.

13 Filipek PA, Kennedy DN, Caviness VS, Jr. Volumetric

analyses of central nervous system neoplasm based on MRI.

Pediatr Neurol 1991;7(5):347 51.

14 Therasse P, Arbuck SG, Eisenhauer EA, et al. New guidelines

to evaluate the response to treatment in solid tumors. J Natl

Cancer Inst 2000;92:205 16.

15 Macdonald DR, Cascino TL, Schold SC, Cairncross JG.

Response criteria for phase II studies of supratentorial

malignant glioma. J Clin Oncol 1990;8:1277 80.

16 Chow KL, Gobin YP, Cloughesy T, et al. Prognostic factors

in recurrent glioblastoma multiforme and anaplastic astro-

cytoma treated with selective intra-arterial chemotherapy.

AJNR Am J Neuroradiol 2000;21:471 8.

17 Galanis E, Buckner JC, Maurer MJ, et al. Validation of

neuroradiologic response assessment in gliomas: measure-

ment by RECIST, two-dimensional, computer-assisted

tumor area, and computer-assisted tumor volume methods.

Neuro-oncol 2006;8:156 65.

18 Shah GD, Kesari S, Xu R, et al. Comparison of linear

and volumetric criteria in assessing tumor response in adult

high-grade gliomas. Neuro-oncol 2006;8:38 46.

19 Hammoud MA, Sawaya R, Shi W, Thall PF, Leeds NE.

Prognostic significance of preoperative MRI scans in glio-

blastoma multiforme. J Neurooncol 1996;27:65 73.

20 Kowalczuk A, Macdonald RL, Amidei C, et al.

Quantitative imaging study of extent of surgical resection

and prognosis of malignant astrocytomas. Neurosurgery 1997;

41:1028 36.

21 Ojemann JG, Miller JW, Silbergeld DL. Preserved function in

brain invaded by tumor. Neurosurgery 1996;39:253 8.

22 Skirboll SS, Ojemann GA, Berger MS, Lettich E, Winn HR.

Functional cortex and subcortical white matter located within

gliomas. Neurosurgery 1996;38:678 84.

23 Tsuruda JS, Chew WM, Moseley ME, Norman D. Diffusion-

weighted MR imaging of the brain: value of differentiating

between extraaxial cysts and epidermoid tumors. AJNR Am J

Neuroradiol 1990;11:925 31.

24 Krabbe K, Gideon P, Wagn P, et al. MR diffusion imaging

of human intracranial tumours. Neuroradiology 1997;39(7):

483 9.

25 Kim DG, Yang HJ, Park IA, et al. Gliomatosis cerebri: clinical

features, treatment, and prognosis. Acta Neurochir (Wien)

1998;140:755 62.

26 Lai PH, Ho JT, Chen WL, et al. Brain abscess and necrotic

brain tumor: discrimination with proton MR spectroscopy

and diffusion-weighted imaging. AJNR Am J Neuroradiol

2002;23:1369 77.

27 Latour LL, Svoboda K, Mitra PP, Sotak CH.

Time-dependent diffusion of water in a biological model

system. Proc Natl Acad Sci USA 1994;91:1229 33.

28 Brunberg JA, Chenevert TL, McKeever PE, et al. In vivo MR

determination of water diffusion coefficients and diffusion

anisotropy: correlation with structural alteration in gliomas of

the cerebral hemispheres. AJNR Am J Neuroradiol 1995;16:

361 71.

572 S. J. Price

Downlo

aded

By:

[Ca

nadi

an R

esea

rch

Know

ledg

e Ne

twor

k] A

t: 0

2:22

27 November 2009

29 Bulakbasi N, Kocaoglu M, Ors F, Tayfun C, Ucoz T.

Combination of single-voxel proton MR spectroscopy and

apparent diffusion coefficient calculation in the evaluation of

common brain tumors. AJNR Am J Neuroradiol 2003;

24:225 33.

30 Castillo M, Smith JK, Kwock L, Wilber K. Apparent diffusion

coefficients in the evaluation of high-grade cerebral gliomas.


31 Kono K, Inoue Y, Nakayama K, et al. The role of diffusion-

weighted imaging in patients with brain tumors. AJNR Am J


32 Lam WW, Poon WS, Metreweli C. Diffusion MR imaging

in glioma: does it have any role in the pre-operation

determination of grading of glioma? Clin Radiol 2002;

57:219 25.

33 Maier SE, Bogner P, Bajzik G, et al. Normal brain and brain

tumor: multicomponent apparent diffusion coefficient line

scan imaging. Radiology 2001;219:842 9.

34 Stadnik TW, Chaskis C, Michotte A, et al. Diffusion-

weighted MR imaging of intracerebral masses: comparison

with conventional MR imaging and histologic findings. AJNR

Am J Neuroradiol 2001;22:969 76.

35 Tien RD, Felsberg GJ, Friedman H, Brown M, MacFall J.

MR imaging of high-grade cerebral gliomas: value of

diffusion-weighted echoplanar pulse sequences. AJR Am J

Roentgenol 1994;162:671 7.

36 Guo AC, Cummings TJ, Dash RC, Provenzale JM.

Lymphomas and high-grade astrocytomas: comparison of

water diffusibility and histologic characteristics. Radiology

2002; 224:177 83.

37 Sugahara T, Korogi Y, Kochi M, et al. Usefulness of

diffusion-weighted MRI with echo-planar technique in the

evaluation of cellularity in gliomas. J Magn Reson Imaging

1999;9:53 60.

38 Pajevic S, Pierpaoli C. Color schemes to represent the

orientation of anisotropic tissues from diffusion tensor data:

application to white matter fiber tract mapping in the human

brain. Magn Reson Med 1999;42:526 40.

39 Beppu T, Inoue T, Shibata Y, et al. Fractional anisotropy

value by diffusion tensor magnetic resonance imaging as a

predictor of cell density and proliferation activity of glioblas-

tomas. Surg Neurol 2005;63:56 61.

40 Stadlbauer A, Ganslandt O, Buslei R, et al. Gliomas:

histopathologic evaluation of changes in directionality and

magnitude of water diffusion at diffusion-tensor MR imaging.

Radiology 2006;240:803 10.

41 Witwer BP, Moftakhar R, Hasan KM, et al. Diffusion-tensor

imaging of white matter tracts in patients with cerebral

neoplasm. J Neurosurg 2002;97:568 75.

42 Tofts PS, Kermode AG. Measurement of the blood-brain

barrier permeability and leakage space using dynamic MR

imaging. 1. Fundamental concepts. Magn Reson Med

1991;17:357 67.

43 Tofts PS, Brix G, Buckley DL, et al. Estimating kinetic

parameters from dynamic contrast-enhanced T(1)-weighted

MRI of a diffusable tracer: standardized quantities and

symbols. J Magn Reson Imaging 1999;10:223 32.

44 Aronen HJ, Gazit IE, Louis DN, et al. Cerebral blood volume

maps of gliomas: comparison with tumor grade and histologic

findings. Radiology 1994;191:41 51.

45 Sugahara T, Korogi Y, Kochi M, et al. Correlation of MR

imaging-determined cerebral blood volume maps with histo-

logic and angiographic determination of vascularity of

gliomas. AJR Am J Roentgenol 1998;171:1479 86.

46 Aronen HJ, Pardo FS, Kennedy DN, et al. High micro-

vascular blood volume is associated with high glucose uptake

and tumor angiogenesis in human gliomas. Clin Cancer Res

2000;6:2189 200.

47 Simmons ML, Frondoza CG, Coyle JT. Immunocytochem-

ical localization of N-acetyl-aspartate with monoclonal anti-

bodies. Neuroscience 1991;45:37 45.

48 Star-Lack J, Spielman D, Adalsteinsson E, et al. In vivo lactate

editing with simultaneous detection of choline, creatine,

NAA, and lipid singlets at 1.5 T using PRESS excitation

with applications to the study of brain and head and neck

tumors. J Magn Reson 1998;133:243 54.

49 Remy C, Fouilhe N, Barba I, et al. Evidence that mobile lipids

detected in rat brain glioma by 1H nuclear magnetic resonance

correspond to lipid droplets. Cancer Res 1997;57:407 14.

50 McBride DQ, Miller BL, Nikas DL, et al. Analysis of brain

tumors using 1H magnetic resonance spectroscopy. Surg

Neurol 1995;44:137 44.

51 Negendank WG, Sauter R, Brown TR, et al. Proton magnetic

resonance spectroscopy in patients with glial tumors: a

multicenter study. J Neurosurg 1996;84:449 58.

52 Croteau D, Scarpace L, Hearshen D, et al. Correlation

between magnetic resonance spectroscopy imaging and

image-guided biopsies: semiquantitative and qualitative

histopathological analyses of patients with untreated glioma.

Neurosurgery 2001;49:823 9.

53 Murphy M, Loosemore A, Clifton AG, et al. The contribution of

proton magnetic resonance spectroscopy (1HMRS) to clinical

brain tumour diagnosis. Br J Neurosurg 2002;16:329 34.

54 Law M, Yang S, Wang H, et al. Glioma grading: sensitivity,

specificity, and predictive values of perfusion MR imaging

and proton MR spectroscopic imaging compared with

conventional MR imaging. AJNR Am J Neuroradiol

2003;24:1989 98.

55 Howe FA, Barton SJ, Cudlip SA, et al. Metabolic profiles of

human brain tumors using quantitative in vivo 1H magnetic

resonance spectroscopy. Magn Reson Med 2003;49:223 32.

56 Meyerand ME, Pipas JM, Mamourian A, Tosteson TD,

Dunn JF. Classification of biopsy-confirmed brain tumors

using single-voxel MR spectroscopy. AJNR Am J Neuroradiol

1999;20:117 23.

57 Remy C, Fouilhe N, Barba I, et al. Evidence that mobile lipids

detected in rat brain glioma by 1H nuclear magnetic

resonance correspond to lipid droplets. Cancer Res 1997;

57:407 14.

58 Nafe R, Herminghaus S, Raab P, et al. Preoperative proton-

MR spectroscopy of gliomas correlation with quantitative

nuclear morphology in surgical specimen. J Neurooncol

2003;63:233 45.

59 Gupta RK, Cloughesy TF, Sinha U, et al. Relationships

between choline magnetic resonance spectroscopy, apparent

diffusion coefficient and quantitative histopathology in human

glioma. J Neurooncol 2000;50:215 26.

60 McKnight TR, Lamborn KR, Love TD, et al. Correlation of

magnetic resonance spectroscopic and growth characteristics

within Grades II and III gliomas. J Neurosurg 2007;106:660

6.

61 Achten E, Jackson GD, Cameron JA, et al. Presurgical

evaluation of the motor hand area with functional MR

imaging in patients with tumors and dysplastic lesions.

Radiology 1999;210:529 38.

62 Fandino J, Kollias SS, Wieser HG, Valavanis A, Yonekawa Y.

Intraoperative validation of functional magnetic resonance

imaging and cortical reorganization patterns in patients with

brain tumors involving the primary motor cortex. J Neurosurg

1999;91:238 50.

63 Roux FE, Boulanouar K, Lotterie JA, et al. Language

functional magnetic resonance imaging in preoperative

assessment of language areas: correlation with direct cortical

stimulation. Neurosurgery 2003;52:1335 47.

64 Roux FE, Tremoulet M. Organization of language areas in

bilingual patients: a cortical stimulation study. J Neurosurg

2002;97:857 64.

65 Holodny AI, Schulder M, Liu WC, Maldjian JA, Kalnin AJ.

Decreased BOLD functional MR activation of the motor and

sensory cortices adjacent to a glioblastoma multiforme:

implications for image-guided neurosurgery. AJNR Am J



Downlo

aded

By:

[Ca

nadi

an R

esea

rch

Know

ledg

e Ne

twor

k] A

t: 0

2:22

27 November 2009

66 Ulmer JL, Krouwer HG, Mueller WM, et al. Pseudo-

reorganization of language cortical function at fMR imaging:

a consequence of tumor-induced neurovascular uncoupling.

Am J Neuroradiol 2003;24:213 17.

67 Kim MJJ, Holodny AI, Hou BL, et al. The effect of prior

surgery on blood oxygen level-dependent functional MR

imaging in the preoperative assessment of brain tumors.


68 Krishnan R, Raabe A, Hattingen E, et al. Functional magnetic

resonance imaging-integrated neuronavigation: correlation

between lesion-to-motor cortex distance and outcome.

Neurosurgery 2004;55:904 15.

69 Berman JI, Berger MS, Mukherjee P, Henry RG. Diffusion-

tensor imaging-guided tracking of fibers of the pyramidal tract

combined with intraoperative cortical stimulation mapping in

patients with gliomas. J Neurosurg 2004;101:66 72.

70 Clark CA, Barrick TR, Murphy MM, Bell BA. White matter

fiber tracking in patients with space-occupying lesions of

the brain: a new technique for neurosurgical planning?

Neuroimage 2003;20:1601 8.

71 Coenen VA, Krings T, Axer H, et al. Intraoperative

three-dimensional visualization of the pyramidal tract in

a neuronavigation system (PTV) reliably predicts true

position of principal motor pathways. Surg Neurol 2003;

60(5):381 90.

72 Tummala RP, Chu RM, Liu H, Truwit CL, Hall WA.

Application of diffusion tensor imaging to magnetic-resonance-

guided brain tumor resection. Pediatr Neurosurg 2003;39:

39 43.

73 Nimsky C, Ganslandt O, Hastreiter P, et al. Intraoperative

diffusion-tensor MR imaging: shifting of white matter tracts

during neurosurgical proceduresinitial experience. Radiol-

ogy 2005;234:218 25.

74 Rutten GJ, Ramsey NF, van Rijen PC, van Veelen CW.

Reproducibility of fMRI-determined language lateralization

in individual subjects. Brain Lang 2002;80:421 37.

75 Ramsey NF, Tallent K, Van Gelderen P, et al. Reproducibility

of human 3D fMRI brain maps acquired during a motor task.

Hum Brain Mapping 1996;4:113 21.

76 Price SJ, Burnet NG, Donovan T, et al. Diffusion tensor

imaging of brain tumours at 3T: a potential tool for assess-

ing white matter tract invasion? Clin Radiol 2003;58(6):

455 62.

77 Provenzale JM, McGraw P, Mhatre P, Guo AC, Delong D.

Peritumoral brain regions in gliomas and meningiomas:

investigation with isotropic diffusion-weighted MR imaging

and diffusion-tensor MR imaging. Radiology 2004;232:451

60.

78 Tropine A, Vucurevic G, Delani P, et al. Contribution of

diffusion tensor imaging to delineation of gliomas and

glioblastomas. J Magn Reson Imaging 2004;20:905 12.

79 Lu S, Ahn D, Johnson G, Cha S. Peritumoral diffusion tensor

imaging of high-grade gliomas and metastatic brain tumors.


80 van Westen D, Latt J, Englund E, Brockstedt S, Larsson

E-M. Tumor extension in high-grade gliomas assessed with

diffusion magnetic resonance imaging: values and lesion-to-

brain ratios of apparent diffusion coefficient and fractional

anisotropy. Acta Radiol 2006;47(3):311 19.

81 Green HA, Pena A, Price CJ, et al. Increased anisotropy in

acute stroke: a possible explanation. Stroke 2002;33:1517

21.

82 Price SJ, Pena A, Burnet NG, et al. Tissue signature

characterisation of diffusion tensor abnormalities in cerebral

gliomas. Eur Radiol 2004;14:1909 17.

83 Lu S, Ahn D, Johnson G, et al. Diffusion-tensor MR imaging

of intracranial neoplasia and associated peritumoral edema:

introduction of the Tumor Infiltration Index. Radiology

2004;232:221 8.

84 Zhou XJ, Leeds NE. Assessing glioma cell infiltration using a

fiber coherence index: a DTI study. Proc Int Soc Mag Reson

Med 2005;13:365.

85 Morita K, Matsuzawa H, Fujii Y, et al. Diffusion tensor

analysis of peritumoral edema using lambda chart analysis

indicative of the heterogeneity of the microstructure within

edema. J Neurosurg 2005;102:336 41.

86 Price SJ, Jena R, Burnet NG, et al. Improved delineation of

glioma margins and regions of infiltration with the use of

diffusion tensor imaging: an image-guided biopsy study.


87 Price SJ, Jena R, Burnet NG, et al. Predicting patterns of

glioma recurrence using diffusion tensor imaging. Eur Radiol

2007;17:1675 84.

88 Jena R, Price SJ, Baker C, et al. Diffusion tensor imaging:

possible implications for radiotherapy treatment planning of

patients with high-grade glioma. Clin Oncol 2005;17:

581 90.

89 Sijens PE, Oudkerk M. 1H chemical shift imaging character-

ization of human brain tumor and edema. Eur Radiol

2002;12:2056 61.

90 McKnight TR, dem Bussche MH, Vigneron DB, et al.

Histopathological validation of a three-dimensional magnetic

resonance spectroscopy index as a predictor of tumor

presence. J Neurosurg 2002;97:794 802.

91 Stadlbauer A, Nimsky C, Gruber S, et al. Changes in fiber

integrity, diffusivity, and metabolism of the pyramidal tract

adjacent to gliomas: a quantitative diffusion tensor fiber

tracking and MR spectroscopic imaging study. AJNR Am J


92 Murakami R, Sugahara T, Nakamura H, et al. Malignant

supratentorial astrocytoma treated with postoperative radia-

tion therapy: prognostic value of pretreatment quantitative

diffusion-weighted MR imaging. Radiology 2007;243:493 9.

93 Kleihues P, Cavenee WK. Pathology and genetics of tumours of

the nervous system. Lyon: IARC Press, 2000.

94 Law M, Yang S, Babb JS, et al. Comparison of cerebral blood

volume and vascular permeability from dynamic susceptibility

contrast-enhanced perfusion MR imaging with glioma grade.


95 Knopp EA, Cha S, Johnson G, et al. Glial neoplasms:

dynamic contrast-enhanced T2*-weighted MR imaging.

Radiology 1999;211:791 8.

96 Shin JH, Lee HK, Kwun BD, et al. Using relative cerebral

blood flow and volume to evaluate the histopathologic grade

of cerebral gliomas: preliminary results. AJR Am J Roentgenol

2002;179:783 9.

97 Sugahara T, Korogi Y, Kochi M, Ushio Y, Takahashi M.

Perfusion-sensitive MR imaging of gliomas: comparison

between gradient-echo and spin-echo echo-planar imaging

techniques. AJNR Am J Neuroradiol 2001;22:1306 15.

98 Boxerman JL, Schmainda KM, Weisskoff RM. Relative

cerebral blood volume maps corrected for contrast agent

extravasation significantly correlate with glioma tumor grade,

whereas uncorrected maps do not. AJNR Am J Neuroradiol

2006;27:859 67.

99 Donahue KM, Krouwer HG, Rand SD, et al. Utility of

simultaneously acquired gradient-echo and spin-echo cerebral

blood volume and morphology maps in brain tumor patients.

Magn Reson Med 2000;43:845 53.

100 Lev MH, Ozsunar Y, Henson JW, et al. Glial tumor grading

and outcome prediction using dynamic spin-echo MR

susceptibility mapping compared with conventional con-

trast-enhanced MR: confounding effect of elevated rCBV of

oligodendrogliomas. AJNR Am J Neuroradiol 2004;25:214

21.

101 Law M, Oh S, Johnson G, et al. Perfusion magnetic resonance

imaging predicts patient outcome as an adjunct to histo-

pathology: a second reference standard in the surgical and

nonsurgical treatment of low-grade gliomas. Neurosurgery

2006;58:1099 107.

102 Law M, Oh S, Babb JS, et al. Low-grade gliomas: dynamic

susceptibility-weighted contrast-enhanced perfusion MR

imagingprediction of patient clinical response. Radiology

2006;238:658 67.

574 S. J. Price

Downlo

aded

By:

[Ca

nadi

an R

esea

rch

Know

ledg

e Ne

twor

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t: 0

2:22

27 November 2009

103 Cha S, Tihan T, Crawford F, et al. Differentiation of low-

grade oligodendrogliomas from low-grade astrocytomas by

using quantitative blood-volume measurements derived from

dynamic susceptibility contrast-enhanced MR imaging.


104 Jenkinson MD, Smith TS, Joyce KA, et al. Cerebral blood

volume, genotype and chemosensitivity in oligodendroglial

tumours. Neuroradiology 2006;48(10):703 13.

105 Law M, Brodsky JE, Babb J, et al. High cerebral blood volume

in human gliomas predicts deletion of chromosome 1p:

preliminary results of molecular studies in gliomas with

elevated perfusion. J Magn Reson Imaging 2007;25:1113 19.

106 Donahue KM, Krouwer HG, Rand SD, et al. Utility of

simultaneously acquired gradient-echo and spin-echo cere-

bral blood volume and morphology maps in brain tumor

patients. Magn Reson Med 2000;43:845 53.

107 Roberts HC, Roberts TP, Brasch RC, Dillon WP. Quanti-

tative measurement of microvascular permeability in human

brain tumors achieved using dynamic contrast-enhanced MR

imaging: correlation with histologic grade. AJNR Am J


108 Provenzale JM, Wang GR, Brenner T, Petrella JR,

Sorensen AG. Comparison of permeability in high-

grade and low-grade brain tumors using dynamic suscept-

ibility contrast MR imaging. AJR Am J Roentgenol

2002;178:711 16.

109 Roberts HC, Roberts TP, Bollen AW, et al. Correlation of

microvascular permeability derived from dynamic contrast-

enhanced MR imaging with histologic grade and tumor

labeling index: a study in human brain tumors. Acad Radiol

2001;8(5):384 91.

110 Ludemann L, Hamm B, Zimmer C. Pharmacokinetic analysis

of glioma compartments with dynamic Gd-DTPA-enhanced

magnetic resonance imaging. Magn Reson Imaging 2000;

18:1201 14.

111 Tate AR, Majos C, Moreno A, et al. Automated classification

of short echo time in in vivo 1H brain tumor spectra: a

multicenter study. Magn Reson Med 2003;49:29 36.

112 Chenevert TL, McKeever PE, Ross BD. Monitoring early

response of experimental brain tumors to therapy using

diffusion magnetic resonance imaging. Clin Cancer Res

1997;3:1457 66.

113 Chenevert TL, Stegman LD, Taylor JM, et al. Diffusion

magnetic resonance imaging: an early surrogate marker of

therapeutic efficacy in brain tumors. J Natl Cancer Inst

2000;92:2029 36.

114 Mardor Y, Roth Y, Lidar Z, et al. Monitoring response to

convection-enhanced taxol delivery in brain tumor patients

using diffusion-weighted magnetic resonance imaging. Cancer

Res 2001;61:4971 3.

115 Moffat BA, Chenevert TL, Lawrence TS, et al. Functional

diffusion map: a noninvasive MRI biomarker for early

stratification of clinical brain tumor response. PNAS

2005;102:5524 9.

116 Hall DE, Moffat BA, Stojanovska J, et al. Therapeutic efficacy

of DTI-015 using diffusion magnetic resonance imaging as an

early surrogate marker. Clin Cancer Res 2004;10:7852 9.

117 Akella NS, Twieg DB, Mikkelsen T, et al. Assessment of

brain tumor angiogenesis inhibitors using perfusion magnetic

resonance imaging: quality and analysis results of a phase I

trial. J Magn Reson Imaging 2004;20:913 22.

118 Nabors LB, Mikkelsen T, Rosenfeld SS, et al. Phase I and

correlative biology study of cilengitide in patients with

recurrent malignant glioma. J Clin Oncol 2007;25:1651 7.

119 Cao Y, Tsien CI, Nagesh V, et al. Survival prediction in high-

grade gliomas by MRI perfusion before and during early stage

of RT. Int J Radiat Oncol Biol Phys 2006;64:876 85.

120 Lee MC, Cha S, Chang SM, Nelson SJ. Dynamic suscept-

ibility contrast perfusion imaging of radiation effects in

normal-appearing brain tissue: changes in the first-pass and

recirculation phases. J Magn Reson Imaging 2005;21:683 93.

121 Price SJ, Jena R, Green HA, et al. Early radiotherapy dose

response and lack of hypersensitivity effect in normal brain

tissue: a sequential dynamic susceptibility imaging study of

cerebral perfusion. Clin Oncol 2007. Published online, Jul 11:

doi:10.1016/j.clon.2007.04.010.

122 Tarnawski R, Sokol M, Pieniazek P, et al. 1H-MRS in vivo

predicts the early treatment outcome of postoperative radio-

therapy for malignant gliomas. Int J Radiat Oncol Biol Phys

2002;52:1271 6.


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Advanced Mr Imaging in Understanding Brain Tumour Pathology

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