1. health and fitness
2. disease
3. cancer

The British Journal of Radiology, 82 (2009), 148–156
Dynamic contrast-enhanced MRI for prostate cancer localization
1
A S N JACKSON, FRCR, 2S A REINSBERG, PhD, 3S A SOHAIB, FRCR, 2E M CHARLES-EDWARDS, MSc,
S JHAVAR, MD, DMRT, 5T J CHRISTMAS, MD, FRCS, 5A C THOMPSON, FRCS, 6M J BAILEY, MS, FRCS,
7
C M CORBISHLEY, FRCPath, 8C FISHER, MD, FRCPath, 2M O LEACH, PhD and 1D P DEARNALEY, MD, FRCR
4
1
Academic Department of Radiotherapy and Oncology, 2CRUK Magnetic Resonance Imaging Group and, 3Department of
Radiology, Royal Marsden NHS Foundation Trust and Institute of Cancer Research, Downs Rd, Sutton SM2 5PT, 4Cancer
Genetics and Molecular Carcinogenesis, The Institute of Cancer Research and the Royal Marsden Hospital NHS
Foundation Trust, Cotswold Road, Sutton SM2 5NG, 5Department of Urology, Royal Marsden NHS Foundation Trust,
Fulham Road, London SW3 6JJ, Departments of 6Urology and 7Cellular Pathology, St Georges Hospital, Blackshaw Road,
London SW17 0QT, and 8Department of Histopathology, Royal Marsden NHS Foundation Trust, Fulham Road, London
SW3 6JJ, UK
ABSTRACT. Radiotherapy dose escalation improves tumour control in prostate cancer
but with increased toxicity. Boosting focal tumour only may allow dose escalation with
acceptable toxicity. Intensity-modulated radiotherapy can deliver this, but visualization
of the tumour remains limiting. CT or conventional MRI techniques are poor at
localizing tumour, but dynamic contrast-enhanced MRI (DCE-MRI) may be superior. 18
patients with prostate cancer had T2 weighted (T2W) and DCE-MRI prior to
prostatectomy. The prostate was sectioned meticulously so as to achieve accurate
correlation between imaging and pathology. The accuracy of DCE-MRI for cancer
detection was calculated by a pixel-by-pixel correlation of quantitative DCE-MRI
parameter maps and pathology. In addition, a radiologist interpreted the DCE-MRI and
T2W images. The location of tumour on imaging was compared with histology, and the
accuracy of DCE-MRI and T2W images was then compared. Pixel-by-pixel comparison of
quantitative parameter maps showed a significant difference between the benign
peripheral zone and tumour for the parameters Ktrans, ve and kep. Calculation of areas
under the receiver operating characteristic curve showed that the pharmacokinetic
parameters were only ‘‘fair’’ discriminators between cancer and benign gland.
Interpretation of DCE-MRI and T2W images by a radiologist showed DCE-MRI to be
more sensitive than T2W images for tumour localization (50% vs 21%; p50.006) and
similarly specific (85% vs 81%; p50.593). The superior sensitivity of DCE-MRI compared
with T2W images, together with its high specificity, is arguably sufficient for its use in
guiding radiotherapy boosts in prostate cancer.
Radiotherapy dose escalation improves tumour control rates in prostate cancer, particularly in less favourable tumours [1–5], but this is at the expense of greater
rectal side effects. One strategy to improve the therapeutic ratio of increased radiotherapy would be to dose
escalate only discrete tumour foci within the prostate,
and contemporary radiotherapy planning techniques are
able to create the necessary dose distributions to do this
[6, 7]. The major limitation here is difficulty in delineating tumour using available imaging techniques. CT, the
standard method for radiotherapy target volume localization, provides poor soft-tissue contrast, and focal
tumour in the prostate is often not seen. MRI has
superior soft-tissue contrast resolution compared with
CT, but conventional techniques still lack the necessary
accuracy to delineate tumour satisfactorily.
Dynamic contrast-enhanced MRI (DCE-MRI) uses
differences in the time course of enhancement following
Address correspondence to: A S N Jackson, Academic Department
of Radiation Oncology, Christie Hospital NHS Trust, Wilmslow Rd,
Manchester M20 4BX, UK. E-mail: Andrew.Jackson@manchester.
ac.uk
148
Received 12 December
2007
Revised 12 February 2008
Accepted 7 March 2008
DOI: 10.1259/bjr/89518905
’ 2009 The British Institute of
Radiology
intravenous administration of contrast in order to
distinguish
benign
from
malignant
tissues.
Enhancement can be expressed semi-quantitatively
using parameters derived from signal intensity changes
associated with the passage of contrast agent.
Enhancement can also be described qualitatively, based
on calibrated contrast agent concentration–time curves
[8] and pharmacokinetic models used to express the
passage of contrast agent between tissue compartments
in terms of physiologically relevant parameters.
DCE-MRI has been investigated in the prostate;
prostate cancer is often found to enhance more quickly
and to a greater degree, and to show more washout than
the benign peripheral zone [9–13]. Studies that derived
concentration–time curves described similar differences
between cancerous and benign prostate according to
either descriptive parameters relating to the concentration–time curve [12] or pharmacokinetic models [14, 15].
In terms of clinical application, some studies have
suggested that DCE-MRI may be of use in characterizing
hypointense lesions on conventional T2 weighted (T2W)
MRI sequences [10, 11]. Some studies selected a single
The British Journal of Radiology, February 2009
Dynamic contrast-enhanced MRI in prostate cancer
slice for DCE-MRI analysis [10], one thought likely to
contain tumour on the basis of T2W sequences [9, 11].
Later studies produced more clinically relevant results
by comparing the ability of radiologists to correctly
localize tumour to a region of the prostate using DCEMRI and T2W images, and suggesting superiority for
DCE-MRI [16, 17]
This work describes two approaches to evaluate the
ability of DCE-MRI to correctly localize tumour within
the prostate, as assessed against histological analysis of
whole-mount prostatectomy specimens. Firstly, quantitative pharmacokinetic parameter maps derived from
DCE-MRI images were compared on a pixel-by-pixel
basis with the distribution of tumour within the prostate,
in effect assessing the accuracy of automated tumour
delineation. Secondly, a radiologist viewed standard
T2W images and raw DCE-MRI data to determine the
presence or absence of tumour within specific regions of
the prostate.
Table 1. Summary of patient characteristics
Parameter
Mean
Range
Age (years)
PSA (ng l–1)
No. of core biopsies
Cores involved (%)
Interval biopsy to MRI (weeks)
59
10.76
8
35.7
31
42–73
4.3–18.9
6–12
12.5–58
8–151
Parameter
Clinical T stage:
T1c
T2a
T2b
T3a
Unavailable
Gleason score:
3+3
3+4
4+4
Number
10
2
1
1
4
12
5
1
PSA, prostate-specific antigen.
Methods and materials
The research protocol was approved by the Royal
Marsden NHS Trust and Institute of Cancer Research
Committee for Clinical Research and Regional Ethics
Committee,
and
Wandsworth
Regional Ethics
Committee.
Patients
The 19 patients included in this study were undergoing radical prostatectomy as primary treatment for
histologically confirmed adenocarcinoma of the prostate.
The patients were required to have no contra-indication
to MRI scanning and were ineligible if they had received
prior hormonal treatment or if prostatectomy was a
salvage procedure following radiotherapy. Histological
diagnosis of prostate cancer had been reached by
transrectal ultrasound-guided biopsy in all cases.
Remaining patient characteristics can be found in
Table 1. In one patient, the size of the prostate precluded
its removal as a whole gland; therefore, correlation
between histology and imaging was not feasible for this
patient. In two patients, satisfactory calibration data
were not acquired at the time of MRI scanning and,
consequently, quantitative pharmacokinetic analysis
could not take place. Pixel-by-pixel analysis is therefore
restricted to 16 patients and radiologist interpretation to
18 patients.
examination. The DCE-MRI volume was placed such
that its slices interrogated the same position and
angulations as those of the T2W scan, although the
DCE-MRI volume was limited to eight slices to ensure an
acquisition time of ,3 s per image volume. Owing to
restrictions on gradient slew-rate, the minimum repetition time achievable with the gradient echo sequence
varied with patient-dependent imaging volume angulation. The DCE-MRI scan therefore comprised 150
consecutive imaging time-points of duration 2.9–3.2 s,
during which contrast agent (0.5 mmol ml–1 gadodiamide (Omniscan; Amersham Health AS, Oslo, Norway))
was administered intravenously after the 10th time-point
–1
to a total dose of 0.4 ml kg at a rate of 3 ml s–1.
Parameters associated with the imaging sequences are
detailed in Table 2.
Surgery
Three consultant urologists collaborated on the project,
two of whom perform radical prostatectomy via a
retropubic approach, and the other via a transperineal
approach. Three patients therefore underwent transperineal prostatectomy and the remainder retropubic
prostatectomy. The aim was to remove the gland whole
with the seminal vesicles attached. 14 patients underwent surgery within 24 h of MRI, with the remaining 5
patients having surgery 2 days, 4 days, 9 days and 21
days (2 patients) after MRI.
Imaging
Imaging was performed using a 1.5 T Philips Intera
Gyroscan scanner (Philips Medical Systems, Reigate, UK)
with a phased-array flexible pelvic coil. A T2W sagittal
scan was performed to identify the posterior surface of
the prostate. Axial T2W scans were aligned such that 18
6 4 mm contiguous slices were acquired perpendicular
to the posterior surface of the gland. A three-dimensional
T1 weighted gradient echo sequence comprising eight
4 mm contiguous slices was used in the DCE-MRI
The British Journal of Radiology, February 2009
Histopathological analysis
The left and right sides (¡ the anterior surface) of the
prostate were inked to allow subsequent orientation. The
seminal vesicles were detached and thin sections from
each urethral extremity of the gland were taken with a
scalpel to assess margin status. The ex vivo gland was
sectioned perpendicularly to its posterior surface using a
multi-bladed knife and cradle developed within the
institution (UK patent number 2404607) (Figure 1). This
149
A S N Jackson, S A Reinsberg, S A Sohaib et al
Table 2. Details of MRI sequences used
Sequence
TE (ms)
TR (ms)
Flip angle
FOV (mm)
Acquired matrix
Reconstructed
matrix
Slice thickness/
gap (mm)
T2W coronal scout
T2W sagittal
T2W axial small FOV
DCE-MRI
80
132
100
Shortest
Shortest
5080
Shortest
3.7–4.1
90
90
90
10
3756262.5
3706259
1706170
3006210
2566179
3206224
2566256
1766123
2566179
5126358
2566256
2566179
5/0
3/0.4
4/0
4/0
TR, repetition time; TE, echo time; FOV, field of view; T2W, T2 weghted; DCE, dynamic contrast-enhanced.
apparatus ensured that the prostate was held in a known
and stable orientation for slicing, and resulted in tissue
slices of 4 mm thickness, i.e. the same slice thickness and
orientation as the images derived from the DCE-MRI and
axial T2W scans. The resulting slices were put into
cassettes in a known orientation, i.e. cranial or caudal
face of the slice downward in the cassette. One slice was
selected by another investigator for subsequent RNA
extraction [18], placed in a buffer solution and refrigerated. All of the slices of the main specimen were
photographed using a Nikon D100 digital camera
(Nikon UK Limited, Kingston upon Thames, UK). All
slices except that used for RNA extraction were fixed in
10% neutral buffered formaldehyde solution for a
minimum of 24 h before being processed using an
automated processor (Tissue-TekH VIP 5 series, Sakura,
Torrance CA) and embedded in paraffin wax. The wax
blocks were sectioned with a rotary microtome to a
thickness of 3 mm, mounted as whole sections on slides
and stained with haemotoxylin and eosin.
Image co-registration
Stained slices were photographed, and each image
(with the tumour indicated by a consultant histopathologist (C.F or C.C)) was matched with the image of its
fresh unstained counterpart and, in turn, its corresponding MRI image. Assignment of MRI with the corresponding pathology was achieved by an overall ‘‘best fit’’ of
T2W images to pathology, based on morphology of the
fresh unstained slices. T2W images with their superior
anatomical detail were used for this initial co-registration. The stained slice was orientated using the inked
margins for left and right orientation, and anatomical
Figure 1. Prostate slicing using a multi-bladed knife and
holder developed in-house.
150
features ¡ the inked anterior surface for anteroposterior
orientation.
To compensate for shrinkage, which in other series has
been estimated as between 10% and 33% [19–22], and
distortion during processing of fresh tissue, image
morphing software was used to apply transformations
to the final stained slice in order to reshape it to its
original corresponding MRI image (gtkMorph; GNU,
general public licence). One control mesh was created
between the image of the fresh slice and its T2W MRI
counterpart, and another between the fresh slice and the
stained section. Landmarks such as the capsule, urethra
and ejaculatory ducts, but not features thought to
represent tumour, were used as reference points for the
control mesh. Using the approach illustrated in Figure 2,
each image of a stained slice with the tumour outlined
was morphed to the shape of the corresponding T2W
MRI slice via the intermediate step of the image of the
unfixed (fresh) corresponding prostate slice. The area of
each histologically demonstrated individual tumour
focus was also recorded, and the total tumour volume
per patient was estimated by multiplying the sum of
tumour areas per patient by the slice thickness (4 mm).
From the slice coordinates of the T2W slice, the
coincident DCE-MRI image was identified. An additional visual comparison between corresponding T2W
and dynamic images was made to control for any patient
and/or prostate motion taking place between the
imaging sequences.
Generation of pharmacokinetic parameter maps
The previously calibrated DCE-MRI data were corrected for anteroposterior prostate motion occurring
during the dynamic series acquisition using software
developed in-house. The correction was then manually
checked by sequentially scrolling through the sequence
of images for each imaging slice.
The motion-corrected dynamic sequences were then
transferred to a separate workstation running in-house
software (Magnetic Resonance Imaging Workbench
(MRIW)) [23]. The software converts intensity changes
observed in the images to changes in T1 values, and
hence contrast agent concentration, using the method
described by Parker et al [8] and subsequently generates
pharmacokinetic parameter maps using a model
described by Tofts et al [24]. In this model, the movement
of contrast agent between tissue compartments is related
to three physiologically based parameters: Ktrans (the
volume transfer constant between blood plasma and
extracellular extravascular space (EES)), ve (the EES
fractional volume) and kep (the flux rate constant
between the EES and plasma). A standardized arterial
The British Journal of Radiology, February 2009
Dynamic contrast-enhanced MRI in prostate cancer
Figure 2. The process of correcting for shrinkage and distortion of the specimen using gtkMorph software.
input was used in the pharmacokinetic model (derived
in a manner similar to that described by Walker-Samuel
et al [25]) using data from arterial sampling reported by
Fritz-Hansen et al [26]. Typical output parameter maps
from the MRIW software are shown in Figure 3.
Pixel-by-pixel analysis
A pixel-by-pixel comparison between the pharmacokinetic maps and the corresponding histopathology
sections was performed using software developed inhouse. The prostate and its peripheral and central zones
were defined on the corresponding T2W images. These
regions along with those defined as being tumour
(morphed as described earlier) were then transferred
automatically to the DCE-MRI image data. Mean
pharmacokinetic enhancement parameters for the
tumour, benign central gland and benign peripheral
zone were calculated for each patient. The sensitivity and
specificity on a pixel-by-pixel basis for varying thresholds of each parameter were calculated for each slice in
order to generate receiver operating characteristic (ROC)
curves for each patient and for each parameter, and also
a ‘‘summary’’ ROC for all pixels for all patients.
Analysis by qualitative radiological assessment
Our second approach involved an experienced radiologist (S.A.S) identifying tumour based on an overall
impression of the raw dynamic images viewed using the
manufacturer’s analysis software (Easyvision; Philips).
Specifically, focal areas of rapid enhancement and/or
Table 3. Mean values of pharmacokinetic parameters for 16 patients analysed
Benign peripheral zone
Benign central gland
Tumour
kep (min21)
Ktrans (min21)
ve
0.502 (0.002)
0.513 (0.026)
0.685
0.219 (0.001)
0.264 (0.109)
0.328
0.318 (0.003)
0.382 (0.179)
0.412
Parenthesized values indicate p-values derived from Wilcoxon signed ranks tests between parameters for the benign gland and
tumour.
The British Journal of Radiology, February 2009
151
A S N Jackson, S A Reinsberg, S A Sohaib et al
Figure 3. Dynamic contrast-enhanced MRI images of the pelvis with overlayed quantitative parameter maps for (a) Ktrans, (b) ve
and (c) kep. (d) The contrast time vs concentration curve for an individual voxel.
washout were interpreted as representing tumour. The
prostate was first considered in the lower, middle and
upper thirds based on the number of pathology slices
obtained from the gland. Each axial section was then
divided into 4 quadrants, resulting in 12 zones in total.
The assignment between histopathology slices and
imaging was the same as that used in the pixel-by-pixel
analysis. Blinded to the results of pathology, the
radiologist examined each slice and identified probable
tumour from the qualitative parameter maps and from
the raw dynamic images. Each region of interest was
designated as being within one of the twelve zones. e.g.
‘‘inferior, posterior, left’’. The same way of describing the
152
location of tumour was used for the histopathology
sections. On a different occasion, the radiologist used
only the T2W images to identify the presence or absence
of disease in a particular prostate zone, with focal areas
of low signal intensity taken to represent tumour. To
determine the effect of lesion size on the radiologist’s
ability to identify malignant lesions, regions of interest
were divided into quartiles according to area, and
separately analysed. As there are data to suggest that
lesions ,0.5 cm3 in size may not be prognostically
significant [27, 28], further analysis was carried out after
subdividing patients according to whether their total
tumour volume was .0.5 cm3 or .1.0 cm3.
The British Journal of Radiology, February 2009
Dynamic contrast-enhanced MRI in prostate cancer
Statistical analysis
Discussion
Statistical analysis was carried out using SPSS (SPSS
Inc, Chicago, IL). The Wilcoxon signed rank test was
used to compare means of paired datasets, and the
Mann–Whitney U test was used for non-paired data. A pvalue of ,0.05 was taken to represent statistical
significance.
Results from the automated pixel-by-pixel analysis
indicate a statistically significant difference in pharmacokinetic parameters between cancer and the benign
peripheral zone, but the discriminatory value of the test,
as judged by the area under the ROC curve, is only fair.
There was no significant difference in the enhancement
parameters between cancer and the benign central gland.
The qualitative radiological assessment method of
analysis has shown that DCE-MRI is superior to T2W
scans in terms of the ability of a radiologist to correctly
localize tumour to geographical regions of the prostate.
We have not clearly demonstrated a relationship
between tumour size and imaging accuracy, but sample
sizes were small when patients and/or lesions were
divided according to size.
Other studies have examined the use of DCE-MRI in
prostate cancer. Initial work tended to map retrospectively histological tumour to corresponding DCE-MRI
imaging or parameter maps in order to explore which
parameters appeared to be most discriminatory between
tumour and benign gland. Some have used simple
descriptive parameters relating to the time enhancement
or time–concentration curve, whereas others have
applied pharmacokinetic models. Turnbull et al [14]
correlated pharmacokinetic parameters with histopathology findings in 12 subjects and demonstrated that
neoplastic tissue had a greater amplitude and higher
contrast exchange rate than fibromuscular benign prostatic hypertrophy (BPH). Using a two-compartment
pharmacokinetic model, Kiessling et al [13] found that
prostate cancer has a higher amplitude of enhancement,
kep and area under the time–signal intensity curve than
the benign gland.
In the publication by Jager et al [9], a dynamic
subtraction turbo-FLASH (fast low-angle shot) sequence
with an endorectal coil was used in addition to standard
MRI images of the prostate. The patient group comprised
57 men who were about to undergo radical prostatectomy for prostate cancer. A single slice thought to contain
both tumour and benign tissue was chosen for the
dynamic sequence from the T2W images. For correct
tumour localization, the sensitivity was 57.5% vs 73.5%
and specificity 80.5% vs 81% for T2W and turbo-FLASH
sequences, respectively. For the detection of extracapsular spread on a quadrant by quadrant basis, sensitivity
was 18.4% vs 50% and specificity 98.3% vs 99.2% for T2W
and turbo-FLASH images, respectively.
Engelbrecht et al [12] analysed 36 men with prostate
cancer prior to radical retropubic prostatectomy and
performed DCE-MRI with an endorectal coil, in addition
to standard MRI sequences. Concentration–time curves
Results
Pixel-by-pixel analysis
Table 3 shows the mean and the range of values for
each of the three pharmacokinetic parameters for the 16
patients analysed in this way. Data are presented for the
benign central gland, benign peripheral zone and
tumour. A significant difference was seen between the
benign peripheral zone and tumour for the values of
Ktrans, ve, and kep. When comparing benign central gland
to tumour, a significant difference was seen only for kep.
Table 4 summarizes the area under the ROC curve for
each parameter, i.e. the ability of a specific parameter to
discriminate benign from malignant tissue. Data are
presented for the whole gland and are restricted to the
peripheral zone.
Analysis by qualitative radiological assessment
Out of a possible 12 zones, the average number of
zones per patient thought to be suspicious for containing
tumour was 2.9 (range, 1–6) on the basis of DCE-MRI
alone, and 1.7 (range, 0–4) on the basis of T2W images
alone. The average number of zones per patient
confirmed by histology to contain tumour was 4.6
(range, 1–11). The median estimated tumour volume
per patient was 0.676 cm3 for all 18 patients and 0.83 cm3
for the 16 patients included in the pixel-by-pixel analysis;
the range for both groups was 0.01–4.76 cm3.
Table 5 shows the accuracy of the technique as
determined by the qualitative radiological assessment
method. The sensitivity of DCE-MRI was significantly
greater than that for T2W scans for all sizes of lesion,
with a similar specificity. No significant sensitivity or
specificity difference was seen between patients with a
tumour volume greater or smaller than 0.5 cm3, or
greater or smaller than 1 cm3 for DCE-MRI or T2W
images. There was no significant difference in the
sensitivity of DCE-MRI when all lesions were considered
compared with the largest 25% of lesions only (50.5% vs
69.6%; p50.25) (data not shown).
Table 4. Mean area under the receiver operating characteristic (ROC) curve for each parameter
Ktrans
kep
Whole gland
Area under summary ROC 0.61
curve (all patients)
Range of areas under curve 0.33–0.74
for individual patients
ve
Peripheral zone
Whole gland
Peripheral
zone
Whole gland
Peripheral zone
0.6
0.6
0.6
0.55
0.56
0.46–0.77
0.33–0.73
0.46–0.76
0.33–0.74
0.32–0.75
The British Journal of Radiology, February 2009
153
A S N Jackson, S A Reinsberg, S A Sohaib et al
Table 5. Accuracy as assessed by qualitative radiological assessment for DCE-MRI and T2W images
DCE MRI
Sensitivity
All patients (n518)
0.5 (0.006)
0.49
Total tumour volume .0.5 cm3
(n511)
Total tumour volume .1.0 cm3 (n56) 0.63
T2W MRI
Specificity
Sensitivity
Specificity
0.85 (0.593)
0.85
0.21
0.25
0.89
0.90
0.93
0.34
1.0
Parenthesized values indicate p-values derived from Wilcoxon signed ranks tests comparing DCE-MRI and T2W images. DCE,
dynamic contrast-enhanced; T2W, T2 weighted.
were calculated and a four-parameter model applied,
with onset time, time to peak, peak enhancement and
washout being chosen as the parameters of interest. The
optimal parameter for the discrimination of tumour
within the peripheral zone or central gland was found to
be relative peak enhancement. In this study, the
exclusion of small lesions and the characterization of
each lesion by selected pixels, demonstrating the maximal value of the parameter of interest, could lead to bias
and an overestimation of the association between that
parameter and the presence of tumour.
Rouviere et al [11] imaged 39 patients with a DCE-MRI
T1 weighted FLASH sequence prior to radical prostatectomy. A single slice thought to contain tumour was
chosen for the dynamic images. Area under the ROC
curve varied from 0.602–0.698 for the identification of
tumour within adenoma, and 0.614–0.827 for the depiction of tumour within the peripheral zone.
Preziosi et al [10] studied 11 patients who were
scanned with conventional sequences and a dynamic
contrast sequence. A single slice containing a focal area
of enhancement was selected for analysis. All 13 of the
enhancing regions of interest corresponded to tumour, as
seen on histopathology, and a further 4 tumour foci were
identified that had not been identified on imaging. This
resulted in a sensitivity of 13/17 (76%) and a positive
predictive value of 100% for DCE-MRI. In a similar way
to the qualitative radiological assessment in our methodology, the values for sensitivity and positive predictive
value given here relate to correct identification of a
tumour focus, but not necessarily to correct assessment
of the size of a given lesion.
The most comparable studies to our qualitative radiological assessment method are those by Girouin et al [17]
and Futterer et al [16]. In 46 patients and using a similar
method to ours, Girouin et al [17] divided the prostate into
20 geographical zones, also distinguishing the central
gland from the peripheral zone and seminal vesicles.
Three observers interpreted DCE-MRI images by visual
assessment of the enhancement pattern. In general terms,
malignant lesions were those considered to enhance earlier
than the surrounding gland. In similar results to ours, a
sensitivity of 46–60% and specificity of 91–94% was found
for DCE-MRI images when compared with 17.6–24%
sensitivity and 94–98% specificity for T2W images.
Futterer et al [16] divided the prostate into 14 zones, and
two radiologists scored T2W images and DCE-MRI
parameter maps for Ktrans, ve and kep on a five-point scale.
DCE-MRI parameter maps scored 69–95% sensitivity and
80–96% specificity compared with 52–67% sensitivity and
73–74% specificity for T2W images. The parameter ve
resulted in the greatest accuracy for both readers.
154
Our study, as well as others, has shown that prostate
tumour is associated with a higher ve, i.e. the fractional
EES, than surrounding gland. Diffusion-weighted MRI,
however, has shown prostate tumours to have a lower
apparent diffusion coefficient than the surrounding
gland [29–32], suggesting a more compact cellular tissue
with less EES. The reason for this apparent contradiction
is not clear, but it is possible that the pharmacokinetic
model used in DCE-MRI, when assuming that the
intravascular volume and contrast within it are negligible, fails in prostate cancer which may have a nonnegligible vascular volume component.
In keeping with other studies, we have demonstrated
that there are differences in the enhancement properties
of prostate cancer when compared with the benign
peripheral gland. This study does, however, have several
limitations. The ‘‘pixel-by-pixel’’ analysis is reliant on
specialist software and is therefore not widely available.
Pixels are considered independently, and patterns within
groups of pixels are not taken into account. In reality, a
group of adjacent pixels of a parameter above a given
threshold might be considered more suspicious for
malignancy than single pixels, a property not considered
by this method of analysis, and which may reduce the
specificity of this method of analysis. Additionally, this
point-by point technique is highly susceptible to small
differences in orientation of the prostate between
imaging and sectioning. The effect of any such misalignment would be particularly marked for small tumours.
For radiologist-reviewed images, errors in orientation
and subsequent slice assignment would produce a less
marked effect than in pixel-by-pixel analysis, as larger
regions of the prostate were considered. A criticism of
this method of analysis is that it represents the ability of
one individual radiologist to identify tumour. In addition, the numerical description of accuracy described
refers only to the ability of the imaging technique to
correctly identify tumour in a particular region of the
prostate, as defined in the study. It takes no account of
the ability to correctly identify the size of lesions.
Not all slices of the prostate were fully analysed for six
patients because the slice taken for RNA extraction was
not subject to routine histopathological processing.
However, even if the least favourable scenario for DCEMRI and the best for T2W images were assumed —
namely that tumour was present in all quadrants of the
non-analysed slice and that DCE-MRI images failed to
demonstrate tumour but T2W images did — the calculated sensitivity of DCE-MRI still remained superior to
that of T2W images (0.48 vs 0.23, respectively; p50.02).
In this study, we took meticulous care to acquire
images at an angulation and slice thickness that would
The British Journal of Radiology, February 2009
Dynamic contrast-enhanced MRI in prostate cancer
allow a direct comparison with histology. Indeed, the
method of sectioning the unfixed prostate has led to the
granting of a UK patent. In addition, we used novel
software to correct for size and shape distortions of
pathology specimens to allow as accurate a correspondence of pixels as possible for the quantitative analysis.
This study was carried out to determine whether DCEMRI would prove a useful tool for radiotherapy target
localization. Ideally, such a technique would have
sensitivity and specificity values of 100% in terms of
correct spatial and volumetric estimation of tumour. The
consequences in terms of improved tumour control vs
normal tissue toxicity for a hypothetical prostate lesion
localized with DCE-MRI and radiation dose escalated
would depend upon the balance between sensitivity and
specificity, and also on the anatomical location of the
lesion within the prostate. Dose-escalating true- or falsepositive lesions would have different normal tissue
implications for, say, the rectum or urethra depending
upon whether they were located in the peripheral zone
or central gland. In this study, the diagnostic accuracy of
dynamic images by a radiologist’s impression are
arguably sufficient to make a case for boosting ‘‘regions’’
of the prostate, as defined here. With these levels of
sensitivity and specificity, approximately half of
involved regions would be detected, and less than 10%
of regions would be boosted needlessly. These approximations may under- rather than over-estimate the
potential value of DCE-MRI, as patients treated with
external beam radiotherapy will, in general, have larger,
more detectable cancers than those studied here, who
were treated with prostatectomy. Further work will
model tumour control probability and the normal tissue
complication probability based on dose-escalating
tumour nodules localized by the qualitative radiological
assessment method of analysis. Other functional MRI
techniques such as diffusion-weighted MRI [31–35] and
MR spectroscopic imaging [36–38] show promise in
prostate cancer imaging, and future work should also
seek to assess the value of these techniques as tools for
target localization in prostate cancer.
Conclusions
We have analysed the ability of DCE-MRI to correctly
localize prostate tumour in two ways. We have shown
that, for one radiologist using a commercial software
package, DCE-MRI results in superior tumour localization compared with T2W scans. We have demonstrated
that tumour appears to have different enhancement
properties to benign peripheral zone and that, according
to ROC analysis, this results in a diagnostic test which
would be considered of fair discriminatory value by
conventional criteria. Further work will establish
whether a clinical benefit would be derived by using
this method of tumour localization for prostate cancer
radiotherapy dose escalation.
Acknowledgments
This work was undertaken in The Royal Marsden NHS
Foundation Trust, which received a proportion of its
The British Journal of Radiology, February 2009
funding from the NHS Executive; the views expressed in
this publication are those of the authors and not
necessarily those of the NHS Executive. This work was
supported by The Department of Health New and
Emerging Applications of Technology (NEAT) program
grant number B132, the Institute of Cancer Research, the
Bob Champion Cancer Trust and Cancer Research UK
Section of Radiotherapy [CUK] grant number C46/
A2131.
References
1. Dearnaley DP, Sydes MR, Graham JD, Aird EG, Bottomley
D, Cowan RA, et al. Escalated-dose versus standard-dose
conformal radiotherapy in prostate cancer: first results from
the MRC RT01 randomised controlled trial. Lancet Oncol
2007;8:475–87.
2. Peeters ST, Heemsbergen WD, Koper PC, van Putten WL,
Slot A, Dielwart MF, et al. Dose-response in radiotherapy
for localized prostate cancer: results of the Dutch multicenter randomized phase III trial comparing 68 Gy of
radiotherapy with 78 Gy. J Clin Oncol 2006;24:1990–6.
3. Pollack A, Zagars GK, Starkschall G, Antolak JA, Lee JJ,
Huang E, et al. Prostate cancer radiation dose response:
results of the M. D. Anderson phase III randomized trial.
Int J Radiat Oncol Biol Phys 2002;53:1097–105.
4. Shipley WU, Verhey LJ, Munzenrider JE, Suit HD, Urie
MM, McManus PL, et al. Advanced prostate cancer: the
results of a randomized comparative trial of high dose
irradiation boosting with conformal protons compared with
conventional dose irradiation using photons alone.
Int J Radiat Oncol Biol Phys 1995;32:3–12.
5. Zietman AL, DeSilvio ML, Slater JD, Rossi CJ Jr, Miller DW,
Adams JA, et al. Comparison of conventional-dose vs highdose conformal radiation therapy in clinically localized
adenocarcinoma of the prostate: a randomized controlled
trial. JAMA 2005;294:1233–9.
6. Nutting CM, Corbishley CM, Sanchez-Nieto B, Cosgrove
VP, Webb S, Dearnaley DP. Potential improvements in the
therapeutic ratio of prostate cancer irradiation: dose
escalation of pathologically identified tumour nodules
using intensity modulated radiotherapy. Br J Radiol
2002;75:151–61.
7. Pickett B, Vigneault E, Kurhanewicz J, Verhey L, Roach M.
Static field intensity modulation to treat a dominant intraprostatic lesion to 90 Gy compared to seven field 3dimensional radiotherapy. Int J Radiat Oncol Biol Phys
1999;44:921–9.
8. Parker GJ, Suckling J, Tanner SF, Padhani AR, Revell PB,
Husband JE, et al. Probing tumor microvascularity by
measurement, analysis and display of contrast agent uptake
kinetics. J Magn Reson Imaging 1997;7:564–74.
9. Jager GJ, Ruijter ET, van de Kaa CA, de la Rosette JJ,
Oosterhof GO, Thornbury JR, et al. Dynamic TurboFLASH
subtraction technique for contrast-enhanced MR imaging of
the prostate: correlation with histopathologic results.
Radiology 1997;203:645–52.
10. Preziosi P, Orlacchio A, Di Giambattista G, Di Renzi P,
Bortolotti L, Fabiano A, et al. Enhancement patterns of
prostate cancer in dynamic MRI. Eur Radiol 2003;13:925–30.
11. Rouviere O, Raudrant A, Ecochard R, Colin-Pangaud C,
Pasquiou C, Bouvier R, et al. Characterization of timeenhancement curves of benign and malignant prostate
tissue at dynamic MR imaging. Eur Radiol 2003;13:931–42.
12. Engelbrecht MR, Huisman HJ, Laheij RJ, Jager GJ, van
Leenders GJ, Hulsbergen-Van de Kaa CA, et al.
Discrimination of prostate cancer from normal peripheral
zone and central gland tissue by using dynamic contrastenhanced MR imaging. Radiology 2003;229:248–54.
155
A S N Jackson, S A Reinsberg, S A Sohaib et al
13. Kiessling F, Lichy M, Grobholz R, Heilmann M, Farhan N,
Michel MS, et al. Simple models improve the discrimination
of prostate cancers from the peripheral gland by T1weighted dynamic MRI. Eur Radiol 2004;14:1793–801.
14. Turnbull LW, Buckley DL, Turnbull LS, Liney GP, Knowles
AJ. Differentiation of prostatic carcinoma and benign
prostatic hyperplasia: correlation between dynamic GdDTPA-enhanced MR imaging and histopathology. J Magn
Reson Imaging 1999;9:311–6.
15. van Dorsten FA, van der Graaf M, Engelbrecht MR, van
Leenders GJ, Verhofstad A, Rijpkema M, et al. Combined
quantitative dynamic contrast-enhanced MR imaging and
(1)H MR spectroscopic imaging of human prostate cancer.
J Magn Reson Imaging 2004;20:279–87.
16. Futterer JJ, Heijmink SW, Scheenen TW, Veltman J,
Huisman HJ, Vos P, et al. Prostate cancer localization with
dynamic contrast-enhanced MR imaging and proton MR
spectroscopic imaging. Radiology 2006;241:449–58.
17. Girouin N, Mege-Lechevallier F, Tonina Senes A, Bissery A,
Rabilloud M, Marechal JM, et al. Prostate dynamic contrastenhanced MRI with simple visual diagnostic criteria: is it
reasonable? Eur Radiol 2007;17:1498–509.
18. Jhavar SG, Fisher C, Jackson A, Reinsberg SA, Dennis N,
Falconer A, et al. Processing of radical prostatectomy
specimens for correlation of data from histopathological,
molecular biological, and radiological studies: a new whole
organ technique. J Clin Pathol 2005;58:504–8.
19. Jager GJ, Ruijter ET, van de Kaa CA, de la Rosette JJ,
Oosterhof GO, Thornbury JR, et al. Local staging of prostate
cancer with endorectal MR imaging: correlation with
histopathology. AJR Am J Roentgenol 1996;166:845–52.
20. Schiebler ML, Tomaszewski JE, Bezzi M, Pollack HM,
Kressel HY, Cohen EK, et al. Prostatic carcinoma and
benign prostatic hyperplasia: correlation of high-resolution
MR
and
histopathologic
findings.
Radiology
1989;172:131–7.
21. Sommer FG, Nghiem HV, Herfkens R, McNeal J, Low RN.
Determining the volume of prostatic carcinoma: value of
MR imaging with an external-array coil. AJR
Am J Roentgenol 1993;161:81–6.
22. Lencioni R, Menchi I, Paolicchi A, Carini M, Amorosi A,
Bartolozzi C. Prediction of pathological tumor volume in
clinically localized prostate cancer: value of endorectal coil
magnetic resonance imaging. Magma 1997;5:117–21.
23. d’Arcy JA, Collins DJ, Padhani AR, Walker-Samuel S,
Suckling J, Leach MO. Informatics in Radiology (infoRAD):
Magnetic Resonance Imaging Workbench: analysis and
visualization of dynamic contrast-enhanced MR imaging
data. Radiographics 2006;26:621–32.
24. Tofts PS, Brix G, Buckley DL, Evelhoch JL, Henderson E,
Knopp MV, 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.
156
25. Walker-Samuel S, Leach MO, Collins DJ. Evaluation of
response to treatment using DCE-MRI: the relationship
between initial area under the gadolinium curve (IAUGC)
and quantitative pharmacokinetic analysis. Phys Med Biol
2006;51:3593–602.
26. Fritz-Hansen T, Rostrup E, Larsson HB, Sondergaard L,
Ring P, Henriksen O. Measurement of the arterial concentration of Gd-DTPA using MRI: a step toward quantitative
perfusion imaging. Magn Reson Med 1996;36:225–31.
27. McNeal JE, Bostwick DG, Kindrachuk RA, Redwine EA,
Freiha FS, Stamey TA. Patterns of progression in prostate
cancer. Lancet 1986;1:60–3.
28. Stamey TA, Yang N, Hay AR, McNeal JE, Freiha FS,
Redwine E. Prostate-specific antigen as a serum marker for
adenocarcinoma of the prostate. N Engl J Med
1987;317:909–16.
29. Choi YJ, Kim JK, Kim N, Kim KW, Choi EK, Cho KS.
Functional MR imaging of prostate cancer. Radiographics
2007;27:63–75; discussion 75–7.
30. Reinsberg SA, Payne GS, Riches SF, Ashley S, Brewster JM,
Morgan VA, et al. Combined use of diffusion-weighted
MRI and 1H MR spectroscopy to increase accuracy in
prostate cancer detection. AJR Am J Roentgenol
2007;188:91–8.
31. Hosseinzadeh K, Schwarz SD. Endorectal diffusionweighted imaging in prostate cancer to differentiate
malignant and benign peripheral zone tissue. J Magn
Reson Imaging 2004;20:654–61.
32. Sato C, Naganawa S, Nakamura T, Kumada H, Miura S,
Takizawa O, et al. Differentiation of noncancerous tissue
and cancer lesions by apparent diffusion coefficient values
in transition and peripheral zones of the prostate. J Magn
Reson Imaging 2005;21:258–62.
33. Charles-Edwards EM, deSouza NM. Diffusion-weighted
magnetic resonance imaging and its application to cancer.
Cancer Imaging 2006;6:135–43.
34. Shimofusa R, Fujimoto H, Akamata H, Motoori K,
Yamamoto S, Ueda T, et al. Diffusion-weighted imaging
of prostate cancer. J Comput Assist Tomogr 2005;29:149–53.
35. Haider MA, van der Kwast TH, Tanguay J, Evans AJ,
Hashmi AT, Lockwood G, et al. Combined T2-weighted and
diffusion-weighted MRI for localization of prostate cancer.
AJR Am J Roentgenol 2007;189:323–8.
36. Kurhanewicz J, Swanson MG, Nelson SJ, Vigneron DB.
Combined magnetic resonance imaging and spectroscopic
imaging approach to molecular imaging of prostate cancer.
J Magn Reson Imaging 2002;16:451–63.
37. Casciani E, Polettini E, Bertini L, Masselli G, Emiliozzi P,
Amini M, et al. Contribution of the MR spectroscopic
imaging in the diagnosis of prostate cancer in the peripheral
zone. Abdom Imaging 2007, in press.
38. Squillaci E, Manenti G, Mancino S, Carlani M, Di Roma M,
Colangelo V, et al. MR spectroscopy of prostate cancer.
Initial clinical experience. J Exp Clin Cancer Res
2005;24:523–30.
The British Journal of Radiology, February 2009