Workforce Planning – Nurses and Midwives — 9/27/07
Ocak et al.
MRI Diagnosis of Prostate
Cancer Based on
Genitourinar y Imaging • Original Research
Dynamic Contrast-Enhanced MRI
of Prostate Cancer at 3 T: A Study
of Pharmacokinetic Parameters
Iclal Ocak1,2
Marcelino Bernardo3
Greg Metzger4
Tristan Barrett1
Peter Pinto5
Paul S. Albert6
Peter L. Choyke1
Ocak I, Bernardo M, Metzger G, et al.
Keywords: dynamic contrast-enhanced MRI, genitourinary
imaging, MRI, pharmacokinetics, prostate cancer
Received October 18, 2006; accepted after revision
April 23, 2007.
Funded in part with federal funds from the National Cancer
Institute, National Institutes of Health, under contract N01CO-12400; and by the Intramural Research Program of the
National Institutes of Health, National Cancer Institute,
Center for Cancer Research.
The content of this publication does not necessarily reflect
the views or policies of the Department of Health and
Human Services, nor does mention of trade names,
commercial products, or organizations imply endorsement
of the U.S. government.
1Molecular Imaging Program, Center for Cancer Research,
National Cancer Institute, Bethesda, MD 20892.
2Present address: Department
of Radiology, University of
Pittsburgh Medical Center, 200 Lothrop St., Pittsburgh, PA
15213-2582. Address correspondence to I. Ocak
([email protected]).
3NCI Molecular Imaging Program and Research
Technology Program, SAIC-Frederick, Inc., Frederick, MD.
4Center for Magnetic Resonance Research, University of
Minnesota, Minneapolis, MN.
5Urologic Oncology Branch, National Cancer Institute,
Bethesda, MD.
6Biometric Research Branch, Division of Cancer Treatment
and Diagnosis, National Cancer Institute, Bethesda, MD.
This is a Web exclusive article.
AJR 2007; 189:W192–W201
В© American Roentgen Ray Society
OBJECTIVE. The objectives of our study were to determine whether dynamic contrast-enhanced MRI performed at 3 T and analyzed using a pharmacokinetic model improves the diagnostic performance of MRI for the detection of prostate cancer compared with conventional
T2-weighted imaging, and to determine which pharmacokinetic parameters are useful in diagnosing prostate cancer.
SUBJECTS AND METHODS. This prospective study included 50 consecutive patients
with biopsy-proven prostate cancer who underwent imaging of the prostate on a 3-T scanner with
a combination of a sensitivity-encoding (SENSE) cardiac coil and an endorectal coil. Scans were
obtained at least 5 weeks after biopsy. T2-weighted turbo spin-echo images were obtained in three
planes, and dynamic contrast-enhanced images were acquired during a single-dose bolus injection of gadopentetate dimeglumine (0.1 mmol/kg). Sensitivity, specificity, positive predictive
value (PPV), and negative predictive value (NPV) were estimated for T2-weighted and dynamic
contrast-enhanced MRI. The following pharmacokinetic modeling parameters were determined
and compared for cancer, inflammation, and healthy peripheral zone: Ktrans (forward volume
transfer constant), kep (reverse reflux rate constant between extracellular space and plasma), ve
(the fractional volume of extracellular space per unit volume of tissue), and the area under the gadolinium concentration curve (AUGC) in the first 90 seconds after injection.
RESULTS. Pathologically confirmed cancers in the peripheral zone of the prostate were
characterized by their low signal intensity on T2-weighted scans and by their early enhancement, early washout, or both on dynamic contrast-enhanced MR images. The overall sensitivity, specificity, PPV, and NPV of T2-weighted imaging were 94%, 37%, 50%, and 89%, respectively. The sensitivity, specificity, PPV, and NPV of dynamic contrast-enhanced MRI were
73%, 88%, 75%, and 75%, respectively. Ktrans, kep, and AUGC were significantly higher
(p < 0.001) in cancer than in normal peripheral zone. The ve parameter was not significantly
associated with prostate cancer.
CONCLUSION. MRI of the prostate performed at 3 T using an endorectal coil produces
high-quality T2-weighted images; however, specificity for prostate cancer is improved by also
performing dynamic contrast-enhanced MRI and using pharmacokinetic parameters, particularly Ktrans and kep, for analysis. These results are comparable to published results at 1.5 T.
rostate cancer is the most prevalent noncutaneous cancer in men
and is the second leading cause of
cancer-related deaths in American
men. In 2006, the American Cancer Society
estimated that 234,460 American men would
be diagnosed with prostate cancer and that
27,350 men would die from the disease [1].
The incidence of prostate cancer has been increasing because of improved diagnosis,
higher prevalence in an aging population, and
increases in potential environmental carcinogens [2]. Most of these newly detected tumors
are confined to the prostate gland; however,
existing imaging methods fail to localize the
site of prostate cancer in 25–30% of the cases
[1, 3]. Moreover, endorectal sonography and
MRI using a conventional endorectal coil lack
specificity for prostate cancer [4]. The ability
of MRI to localize and stage prostate cancer
could assist in the management of newly diagnosed prostate cancer.
There are several methods that can improve
the localization of prostate cancer using T2weighted MRI. One method is to perform
MRI at higher field strengths, thus improving
the signal-to-noise ratio (SNR). Another
method is to use additional imaging tech-
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MRI Diagnosis of Prostate Cancer Based on Pharmacokinetic Parameters
niques. Dynamic contrast-enhanced MRI is a
well-established method for detecting and
quantifying tumor angiogenesis and is independent of T2 relaxation and MR spectroscopic characteristics. The results of dynamic
contrast-enhanced MRI are usually interpreted quantitatively using pharmacokinetic
modeling that allows transfer rate constants,
such as Ktrans (forward volume transfer constant) and kep (reverse reflux rate constant between extracellular space and plasma), to be
determined. These constants are known to be
elevated in many cancers [4, 5]; therefore,
quantitative dynamic contrast-enhanced MRI
based on pharmacokinetic analysis is a potential method for improving the localization of
prostate cancers on MRI. However, angiogenesis is not a constant feature of all tumors, especially small ones, and not all angiogenesis
is due to cancer but can also be caused by inflammatory conditions. Therefore, the specificity of dynamic contrast-enhanced MRI
must be determined in clinical trials. The results of previous studies performed at 1.5 T
suggest that dynamic contrast-enhanced MRI
can improve the specificity of MRI over conventional T2-weighted MRI for the detection
of prostate cancer [6–8]. Few studies of dynamic contrast-enhanced MRI have been conducted at 3 T, which offers a higher SNR and
improved temporal and spatial resolution that
would be expected to improve the localization
of prostate cancer relative to imaging at lower
field strengths [9, 10].
In this study, we investigated the additional
value of dynamic contrast-enhanced MRI in
identifying prostate cancers compared with
T2-weighted MRI at 3 T. Furthermore, we investigated which pharmacokinetic parameters are predictive of prostate cancer.
Subjects and Methods
Study Design
This study was approved by the local institutional
review board and was compliant with the Health Insurance Portability and Accountability Act, informed
consent was obtained from each patient. Fifty consecutive patients were enrolled in the study between February 2005 and May 2006. All patients had biopsyproven prostate cancer and underwent imaging of the
prostate using T2-weighted MRI and dynamic contrast-enhanced MRI at 3 T (Intera, Philips Medical
Systems). The mean age of the patients was 61 years
(range, 53–77 years), and the median prostate-specific
antigen (PSA) level was 15 ng/mL (range, 0.6–270
ng/mL). The Gleason score ranged from 4 to 10.
Inclusion criteria included MRI performed 5
weeks–6 months after endorectal biopsy from
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which at least one of six biopsy samples was positive for prostate cancer and the location of each
specimen was known. If the specimens and mapping were adequate, biopsies performed at outside
institutions were accepted, but if not, they were repeated at our institution. All biopsy specimens were
assessed by a board-certified genitourinary pathologist with extensive experience in prostate cancer.
Patients underwent MRI at least 5 weeks after the
biopsy was performed.
Exclusion criteria included a history of distant metastatic disease, previous prostate radiation therapy,
and a prior or concurrent history of androgen ablative
hormonal therapy (i.e., orchidectomy, antiandrogens).
Patients with contraindications to endorectal coil insertion (e.g., anorectal surgery, severe hemorrhoids)
and those with contraindications to MRI (e.g., patients
with cardiac pacemakers, cerebral aneurysm clips,
shrapnel injury) were also excluded.
MRI Technique
All imaging examinations were performed using
an endorectal coil (BPX-15, Medrad) tuned to
127.8 MHz and combined with a 6-channel cardiac
coil (sensitivity-encoding [SENSE], Philips Medical Systems). Prior bowel preparation was not a requirement. After digital rectal examination was
performed, the endorectal coil was inserted while
the patient was lying in the lateral decubitus position and was inflated using perfluorocarbon (3
mol/L [Fluorinert, 3M]) to a volume of approximately 60 mL to reduce susceptibility artifacts induced by air in the coil’s balloon. Three-plane scout
views of the prostate were obtained to confirm correct positioning of the endorectal coil.
Multisection T2-weighted turbo spin-echo
(TSE) images of the entire prostate were obtained
in three planes (axial, coronal, and sagittal) at a resolution of 0.46 Г— 0.6 Г— 3.0 mm (field of view, 140
mm; matrix, 234 Г— 304; TR/TE, 8,852/120; flip angle, 90В°; slice thickness, 3 mm). The axial images
were positioned perpendicular to the rectal wall
guided by the sagittal scout images, and the phase
direction was left to right.
Dynamic Contrast-Enhanced MRI Acquisition
Dynamic contrast-enhanced images were acquired perpendicular to the long axis of the prostate,
which approximates the axial plane, before, during,
and after a single-dose injection of gadopentetate
dimeglumine (Magnevist, Berlex Laboratories) at a
dose of 0.1 mmol/kg through a peripheral vein at a
rate of 3 mL/s via a mechanical injector (Spectris
MR Injection System, Medrad). An unenhanced T1
map based on a dual flip angle was obtained before
injection by acquiring an identical 3D acquisition
with a flip angle of 5В° to use along with data from the
dynamic acquisition obtained at a flip angle of 15В°
[11]. The unenhanced T1 map allows subsequent
changes in MR signal intensity during passage of the
contrast agent to be converted into changes in gadopentetate dimeglumine concentration.
The dynamic contrast-enhanced acquisition
consisted of a 10-slice, 3D fast-field echo sequence
(TR/TE, 5.5/2.1; 15В° flip angle; 260-mm field of
view; effective SENSE factor, 3; and resolution,
0.86 Г— 1.18 Г— 6.0 mm with a temporal resolution of
3.1 seconds). The phase direction was left to right
and no fat saturation was applied. Six unenhanced
sets (19 seconds) and approximately 94 contrastenhanced sets of images were acquired over the 5
minutes of scanning to monitor the time course of
contrast agent uptake and clearance within the
prostate. There was no delay between acquisitions.
A total of approximately 1,000 images were obtained during dynamic contrast-enhanced MRI.
The entire scanning protocol, combining conventional T2-weighted MRI with dynamic contrast-enhanced MRI, was performed in less than 1 hour.
Dynamic Contrast-Enhanced MRI Analysis with a
Two-Compartment Pharmacokinetic Model
A pharmacokinetic model allows the quantitation
of dynamic contrast-enhanced MRI data in terms of
parameters that relate to the underlying vascular
physiology [12]. The two-compartment pharmacokinetic model we use is based on the principles of
Kety in which a low-molecular-weight contrast
agent is presumed to diffuse from the vascular space
into the extravascular, extracellular space and then
leak slowly back into the vascular space [5, 12–14].
The rate of forward leakage, the rate of backward
leakage, and the fractional volume of the extracellular space can be calculated from the two-compartment pharmacokinetic model. The archetype of this
family of pharmacokinetic models is the two-compartment kinetic model described by Tofts et al. [15],
which uses an arterial input function from a nearby
artery to adjust for the injection rate and cardiac output and an unenhanced T1 map to generate time–gadolinium concentration plots. This general kinetic
model was implemented in a proprietary programming environment (PRIDE software, Philips Medical Systems) using the interface data language (IDL,
RSI). We normalized measurements of the gadolinium concentration–time curves to the arterial input
function, which was obtained from the adjacent femoral artery [16], and acquired unenhanced T1 maps.
To ensure standardization, we first measured individual arterial input function to minimize variations among patients caused by cardiac output and
circulation times. Then, we produced the following
dynamic contrast-enhanced MRI parameters:
Ktrans; kep; ve, the fractional volume of extracellular
space per unit volume of tissue; and the area under
the gadolinium concentration curve (AUGC) in the
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Ocak et al.
Fig. 1—Utility of color map in identifying tumor in 52-year-old man with diffuse chronic prostatitis (prostate-specific antigen level = 4.7 ng/mL, Gleason score = 7).
A, Transverse high-resolution T2-weighted turbo spin-echo MR image shows diffuse low signal intensity within entire peripheral zone.
B and C, Transverse 3D fast-field echo T1-weighted images before (B) and after (C) contrast injection show early signal enhancement in tumor on left anterior peripheral zone.
D, Pixel-by-pixel pharmacokinetic analysis was performed in region of interest (ROI) enclosed by white curve. kep (reverse reflux rate constant between extracellular space
and plasma) map overlaid on T2-weighted image localizes lesion to left anterior peripheral zone.
E and F, Comparison of gadopentetate dimeglumine concentration–versus–time curves obtained from right (E) and left (F) sides of peripheral zone, specified by green and
red ROIs in (C), respectively, indicate that wash-in and washout processes were more rapid in tumor.
first 90 seconds after injection. This last parameter
is not strictly derived from pharmacokinetic analysis but can be readily obtained from the same software. Analysis was performed on a pixel-by-pixel
basis and color maps based on these parameters
were generated separately and overlaid on the T2weighted images.
Image Assessment
All scans were assessed by a uroradiologist with
6 years of experience in endorectal prostate MRI and
two postdoctoral fellows with less than 1 year of experience. The reviewers were aware that patients had
biopsy-proven prostate cancer but were blinded to
the exact sextant involved in each case. On the basis
of anatomic landmarks on the axial T2-weighted images and corresponding biopsy sites, the prostate
gland was divided into the following sextants: left
and right bases, middle gland, and apex. T2weighted images were evaluated blindly without
knowledge of dynamic contrast-enhanced MR images to verify location, and the results were recorded.
Prostate cancer was diagnosed if a low-signal-intensity area in the peripheral zone had at least one of the
following characteristics: irregular shape; inhomogeneous signal; or extension outside the capsule on
T2-weighted imaging, except areas of postbiopsy
hemorrhage, as defined by high signal intensity on
unenhanced T1-weighted images, and postbiopsy
changes in the gland. After interpretation of the T2weighted MR images, the dynamic contrast-enhanced MR images, obtained in cine mode and with
kinetic mapping, were evaluated, and the results of
the combined studies were recorded.
Pharmacokinetic analysis was performed by a
second radiologist who identified the regions of interest (ROIs) and performed the analysis. For pharmacokinetic analysis of dynamic contrast-enhanced
MRI, arterial input function curves were obtained by
drawing an ROI over the femoral artery included in
the image. Another ROI was drawn that included the
entire prostate gland, and pharmacokinetic analysis
of the prostate gland was performed on a pixel-bypixel basis. After that analysis, each individual sex-
tant ROI was drawn to obtain the pharmacokinetic
parameters (Fig. 1). T2-weighted images and dynamic contrast-enhanced MRI findings in each sextant of the prostate gland were evaluated to determine the area most suspicious for prostate cancer
before placement of ROIs. The mean В± 1 SD of each
quantitative parameter was determined in these regions. The size and location of the ROIs were determined by one author using the T2-weighted and dynamic contrast-enhanced MRI scans for each
sextant. Dynamic images were converted into color
maps reflecting Ktrans, kep, ve, and AUGC, respectively, and the mean values of each parameter were
calculated in each sextant. Approximately 45–60
minutes was required to fully analyze the dynamic
contrast-enhanced MRI data in each case.
Statistical Analysis
Measures of diagnostic performance were estimated on a sextant-specific basis. We estimated sensitivity, specificity, positive predictive value (PPV),
and negative predictive value (NPV). Ninety-five
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MRI Diagnosis of Prostate Cancer Based on Pharmacokinetic Parameters
TABLE 1: Statistical Analysis Results of T2-Weighted Imaging, Dynamic Contrast-Enhanced MRI, and Both Sequences
Combined for the Detection of Prostate Cancer
Sensitivity (%)
Specificity (%)
Positive Predictive
Value (%)
Negative Predictive
Value (%)
94 (89–99)
37 (26–47)
50 (43–57)
89 (78–97)
Dynamic contrast-enhanced
73 (62–82)
88 (80–95)
75 (65–85)
75 (68–82)
Combined T2-weighted and dynamic contrast-enhanced
70 (59–80)
88 (80–95)
75 (64–84)
74 (67–81)
MRI Sequence Evaluated
Note—Data in parentheses are estimated 95% CIs using the bootstrap percentile method with 5,000 replicate samples.
Fig. 2—Prostatitis and hemorrhage masking prostate
cancer in right apex in 59-year-old man with Gleason
score of 6 and prostate-specific antigen level of 4.5
ng/mL. Biopsy was performed 8 weeks before MRI.
A, Transverse T2-weighted image shows diffuse low
signal intensity in entire peripheral zone due to
prostatitis and hemorrhage.
B, Unenhanced transverse 3D fast-field echo
T1-weighted image shows diffuse hemorrhage in
peripheral zone.
C, On this MR image obtained after contrast agent
injection, tumor cannot be differentiated from
hemorrhagic regions.
D, kep (reverse reflux rate constant between
extracellular space and plasma [minв€’1]) map also fails
to delineate tumor.
percent CIs were estimated using the bootstrap percentile method to account for potential correlation
between sextants on the same individual.
Ktrans, kep, ve, and AUGC were log-transformed.
We performed two types of analyses. First, we compared the distribution of the continuous MRI variables
(Ktrans, kep, ve, and AUGC) by cancer status at biopsy;
this was done descriptively using box plots. Differences in mean levels were tested using a linear mixed
model (with a random subject effect); this method accounted for the potential correlation in sextant-specific measurements across individuals. We also examined whether these measurements were influenced by
hemorrhage and by PSA using linear mixed models.
In the second analysis, we developed a predictor
of cancer status based on the MRI variables. A logistic regression modeling approach, which accounts for
correlation between sextant measurements on the
same subject, was used. This technique is called
“generalized estimating equations” (GEE) [17]. In
developing this predictor, we included only MRI
variables that have a univariate association with cancer with a significance of p < 0.10. We estimated the
diagnostic performance of the predictor by estimating the receiver operating characteristic (ROC) curve.
To avoid the inherent bias in validating a predictor on
the data used to develop the predictor, we used a
leave-one-out cross-validation method. In this case,
we omitted an individual’s six measurements when
predicting that individual’s cancer status. Probability
values were adjusted for multiple comparisons.
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The overall sensitivity, specificity, PPV, and
NPV of T2-weighted images in patients with
prostate cancer were 94%, 37%, 50%, and
89%, respectively, and the results for dynamic
contrast-enhanced MRI were 73%, 88%, 75%,
and 75%, respectively. Dynamic contrast-enhanced MR images interpreted in conjunction
with T2-weighted MR images resulted in 70%
sensitivity, 88% specificity, 75% PPV, and 74%
NPV (Table 1). Dynamic contrast-enhanced
MRI and color maps produced with quantitative analysis of dynamic contrast-enhanced
MRI were found to be particularly useful in
cases in which there was diffuse low signal intensity on T2-weighted images on peripheral
zone (Fig. 1). Furthermore, in some cases, postbiopsy hemorrhage also led to a diagnostic
challenge on T2-weighted scans and dynamic
contrast-enhanced MR images, even though a
5- to 6-week waiting period was part of the protocol (Fig. 2). Hemorrhagic areas were low in
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Ocak et al.
gression including all variables that were significant in univariate models. The estimated
predictor is shown in Appendix 1.
Figure 5 provides the ROC curve (appropriately cross-validated) for the multivariate
predictor. We see that for a threshold corresponding to a specificity of 80%, the sensitivity is approximately 75%.
Fig. 3—Prostate cancer depicted on T2-weighted and dynamic contrast-enhanced MRI in 56-year-old man with
prostate-specific antigen level of 4.8 ng/mL and histologically proven prostate cancer with Gleason score of 7.
A, Transverse T2-weighted turbo spin-echo image shows low signal intensity in right apical peripheral zone.
B and C, Three-dimensional fast-field echo T1-weighted images before (B) and after (C) contrast agent injection
show earlier signal enhancement in tumor.
D, Fusion of transverse T2-weighted image with color-encoded Ktrans (forward volume transfer constant) map
delineates tumor area.
signal on T2-weighted scans and tended to enhance abnormally, at least in part, on dynamic
contrast-enhanced MRI, most likely due to the
formation of granulation tissue.
The pharmacokinetic variable Ktrans was significantly larger for those sextants that had cancer than for those with a healthy peripheral zone
(mean difference on log scale = 0.236,
p < 0.001). In Figure 3, a typical example of a
prostate dynamic contrast-enhanced MR image,
a high-resolution TSE T2-weighted MR image,
and an image resulting from fusion of a transverse T2-weighted image with color-encoded
Ktrans parametric map are illustrated. The variables kep and AUGC were also significantly
larger for those sextants with cancer than those
with a healthy peripheral zone (mean difference
= 0.211, p < 0.001; mean difference = 0.04;
p < 0.001, respectively). Figure 4 shows dynamic contrast-enhanced MRI, high-resolution
TSE T2-weighted MRI, and a superimposed kep
parametric map, along with the whole analysis
of data by a pharmacokinetic analytic tool. The
variable ve did not differ significantly by cancer
status (mean difference = –0.03, p = 0.33).
Table 2 presents untransformed mean values by
There was no significant association between elevations in PSA and any of the MRI
variables. Furthermore, the differences in
these variables by cancer status were not altered by baseline PSA measurements—that
is, there was no interaction between cancer
status and PSA in this series.
We developed a predictor of cancer status
based on multiple MRI variables. First, using
GEE/logistic regression, we examined which
variables had a univariate influence on the
probability of cancer. Significant variables
were T2-weighted images (p < 0.001), dynamic contrast-enhanced images (p < 0.001),
log(Ktrans) (p = 0.007), log(AUGC) (p = 0.002),
and log(kep) (p = 0.01). We developed a predictor based on using all four variables in a logistic regression model. Second, a prediction
model was developed using GEE/logistic re-
In this study we sought to determine
whether dynamic contrast-enhanced MRI at 3
T improves diagnostic performance for the localization of prostate cancer compared with
high-resolution T2-weighted MRI. Conventional MRI methods are generally limited in
their ability to differentiate prostate cancer
from other abnormalities within the peripheral zone. The results of our study show that
the addition of dynamic contrast-enhanced
MRI to conventional imaging methods improves the localization of prostate cancer in
most cases and may improve diagnostic performance, with the pharmacokinetic parameters Ktrans, kep, and AUGC being significantly
higher in prostate cancer than normal-appearing peripheral zone.
In theory, 3-T MRI could have significant
diagnostic advantages over imaging using
lower-field-strength magnets. Contemporary
fast acquisition techniques can give sufficiently high temporal resolution to sample the
fast dynamics observed during contrast enhancement of the prostate gland; however,
there may be compromises in SNR as the acquisition time decreases [18]. The higher field
strength of 3-T MRI produces an approximately two times higher SNR compared with
1.5 T. This higher field strength allows
higher-resolution T2-weighted images and
faster dynamic images to be obtained with an
SNR comparable to 1.5 T [19]. Some authors
have even suggested that the higher field
strength of 3-T MRI means that the endorectal coil is no longer necessary. Sosna et al.
[20] found image quality at 3 T without an endorectal coil to be comparable to that at 1.5 T
with an endorectal coil [20]. We sought to
take advantage of the higher SNR by including the endorectal coil. We used Fluorinert to
reduce susceptibility artifacts.
Conventional MRI examinations of the
prostate gland have long included T2weighted MR images for diagnosing prostate
cancer. However, the reliability of T2weighted MRI protocols in discriminating
prostate cancer from other causes of low signal intensity, such as inflammation, hemor-
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MRI Diagnosis of Prostate Cancer Based on Pharmacokinetic Parameters
Fig. 4—Prostate cancer depicted on both T2-weighted and dynamic contrast-enhanced MR images of 60-year-old
man with prostate-specific antigen level of 7 ng/mL and tumor on left peripheral zone with Gleason score of 7.
A, Transverse high-resolution turbo spin-echo T2-weighted image shows homogeneous low signal intensity on left
peripheral zone.
B and C, Three-dimensional fast-field echo T1-weighted images before (B) and after (C) contrast agent injection
show earlier signal enhancement in tumor on left peripheral zone.
D, Fusion of transverse T2-weighted image with color-encoded kep (reverse reflux rate constant between
extracellular space and plasma) map detects tumor.
TABLE 2: Quantitative Dynamic Contrast-Enhanced MRI Parameters of
Tumor, Inflammation, and Peripheral Zone of Prostate
Κtrans (min–1)a
kep (min–1)a
(mM Gd Г— min)
0.47 В± 0.57
1.40 В± 0.99
0.33 В± 0.20
0.19 В± 0.13
0.52 В± 0.43
1.36 В± 0.80
0.36 В± 0.20
0.19 В± 0.14
Normal peripheral zone
0.23 В± 0.25
0.80 В± 0.62
0.30 В± 0.18
0.12 В± 0.09
Prostatic Tissue
Note—Data are mean ± SD. K trans = forward volume transfer constant, kep = reverse reflux rate constant
between extracellular space and plasma, ve = the fractional volume of extracellular space per unit volume of
tissue, AUGC = area under the gadolinium concentration curve in the first 90 seconds after injection, mM Gd =
millimolar gadolinium.
a K trans and k values were significantly larger for those sextants that had cancer than for normal peripheral
rhage, or hyperplasia, is low [21–25]. In our
study population, the sensitivity of high-resolution T2-weighted images was 94% in the
peripheral zone, but postbiopsy changes and
chronic prostatitis resulted in a high falsepositive rate and that resulted in a specificity
of only 37%. However, T2-weighted images
used in conjunction with dynamic contrast-
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enhanced MRI resulted in higher diagnostic
performance, with specificity increasing from
37% to 88%, although sensitivity decreased.
T2-weighted MRI is very sensitive for
prostate cancer but is not specific. There are
many false-positives, as the specificity of
37% implies. Dynamic contrast-enhanced
MRI adds specificity because lesions that en-
hance and washout rapidly are likely to be
prostate cancer. It is not surprising, however,
that dynamic contrast-enhanced MRI did not
improve the sensitivity of T2-weighted MRI
because the latter’s sensitivity was already
very high (94%) and many prostate cancers
do not enhance sufficiently. Therefore, dynamic contrast-enhanced MRI is mainly useful in determining whether a T2-weighted
finding is more likely to be a tumor or a benign process. It was not useful in finding new
lesions that were not detectable on T2weighted scans.
Dynamic contrast-enhanced MRI is widely
used for the diagnosis and staging of various
kind of cancers, such as breast carcinoma and
liver metastases, and is emerging as a promising method for monitoring tumor response to
antiangiogenic treatment [26]. Dynamic contrast-enhanced MRI studies show potential
for discriminating between cancer and normal prostatic tissue [27–30]. However, there
still appears to be some overlap in the enhancement patterns between tumors and benign areas of the peripheral zone such as those
affected by prostatitis, postbiopsy hemorrhage, and benign prostatic hyperplasia.
Thus, clinical results of dynamic contrast-enhanced MRI at 1.5 T have been inconsistent,
with sensitivities and specificities of detection or staging (or both) varying from 51% to
89% and from 67% to 87%, respectively [31].
The challenge for MRI is to find a means of
overcoming these limitations. In our study,
sensitivity and specificity of dynamic contrast-enhanced MRI were 73% and 88%, respectively, which fall within the mid range of
the reported sensitivities and specificities at
1.5 T [32] (Table 3); however, comparison of
our results with those of other studies is difficult because the severity of disease varies
with the study population and both estimates
of sensitivity and specificity are subject to
sampling variation. Nevertheless, the increased specificity provided by performing
dynamic contrast-enhanced MRI compared
with T2-weighted imaging may be helpful in
directing biopsies to high-risk lesions in patients with elevated PSA values but repeatedly negative biopsies.
Although all imaging was performed at 3 T
with an endorectal coil and a temporal resolution of 3.1 seconds was achieved, dynamic contrast-enhanced MRI still has diagnostic limitations because of the inherently heterogeneous
nature of prostate cancer and the potential for
multiple microscopic tumor foci within the
gland that are beyond the resolution of MRI
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Ocak et al.
1 в€’ Specificity
Fig. 5—Diagram shows receiver operating characteristic (ROC) curve obtained by combining T2-weighted images
with Ktrans (forward volume transfer constant), kep (reverse reflux rate constant between extracellular space and
plasma), and AUGC (area under the gadolinium concentration curve in the first 90 seconds after injection)
parameters computed from dynamic contrast-enhanced MRI. ROC curve shows diagnostic performance of
generalized estimating equations and logistic regression predictor. Analysis of combination of these parameters
yielded 80% specificity and 75% sensitivity; this corresponds to single point on estimated ROC curve.
TABLE 3: Reported Prostate Cancer Detection Rates of T2-Weighted and
Dynamic Contrast-Enhanced MRI at 1.5 and 3 T
T2-Weighted MRI
[reference no.]
No. of
Schlemmer et al. [38]
Futterer et al. [39]
Girouin et al. [40]
Hara et al. [41]
Ito et al. [42]
Kim et al. [9]
Futterer et al. [39]
Current study
Miao et al. [43]
(Fig. 6). In our study population, patients underwent MRI after at least one positive transrectal sonographically guided biopsy regardless of tumor size. Trauma from this biopsy
most likely produced significant morphologic
changes in the prostate gland in certain patients.
In addition, postbiopsy hemorrhage (even
though imaging was delayed 5–6 weeks after
the last biopsy) and benign prostatic hyperplasia were challenging issues in the differential
diagnosis. Both of these benign entities resulted in an early enhancement pattern (high
Ktrans), early washout (high kep), or both, thus
Dynamic ContrastEnhanced MRI
mimicking prostate cancer. In some cases,
postbiopsy hemorrhage rendered dynamic
contrast-enhanced MRI studies nondiagnostic
because the high signal intensity of subacute
chronic hemorrhage on unenhanced T1weighted images made it difficult to observe
enhancement differences within a tumor.
Moreover, in some cases, hemorrhagic prostate regions showed hyperenhancement that
was likely related to reparative granulation tissue. Thus, for some patients in our study population, the minimum 5- to 6-week waiting period after biopsy was insufficient for the
resolution of either hemorrhage or angiogenesis related to tissue repair. Most patients, however, are unwilling to wait more than 6 weeks
because their treatment must be delayed.
Quantitative pharmacokinetic analysis of
dynamic contrast-enhanced MRI provides information about a tumor’s vascular physiology (i.e., blood flow and permeability of
leaky tumor microvessels), which reflects the
characteristic rapid wash-in and washout in
tumor. It has previously been shown that microvessel density, a marker of angiogenesis,
correlates with pathologic staging of prostate
cancer better than Gleason score of tumors
[33]. Engelbrecht et al. [25] reported that the
optimal parameter for discrimination of prostate cancer in the peripheral zone and central
gland was relative peak enhancement. Noworolski et al. [34] showed significantly
higher peak enhancement, faster enhancement slope, and faster washout slopes in abnormal, as compared with normal, peripheral
zone. Barentsz et al. [28] showed that fast dynamic MRI of bladder cancer and most prostate cancer resulted in early enhancement and
in some cases a steeper slope, higher maximal
signal intensity, and washout as compared
with normal tissue. They found differences in
enhancement between malignant and other
tissues, best seen on the early dynamic images—that is, the first-pass phase of contrast
agent administration.
Our results are similar to prior findings and
show only a modest benefit to using dynamic
contrast-enhanced MRI at 3 T versus 1.5 T.
Differences between these studies are more
likely to be related to patient selection and
technical factors rather than to real differences caused by magnetic field strength.
However, the results of our study clearly show
the benefit of dynamic contrast-enhanced
MRI when combined with T2-weighted MRI
for the detection of prostate cancer.
We also investigated the diagnostic value
of quantitative analysis of dynamic contrastenhanced MRI as a means of improving diagnostic performance in prostate cancer. The
mean values of quantitative parameters were
higher in biopsy-proven cancer areas and
there were statistically significant differences
between normal tissue and prostate cancer for
the parameters Ktrans, kep, and AUGC in concordance with these studies.
Multivariate analysis showed that a combination of all parameters improved the overall
sensitivity and specificity of the T2-weighted
and dynamic contrast-enhanced MRI studies.
Color maps produced from this analysis have
AJR:189, October 2007 — 9/27/07
MRI Diagnosis of Prostate Cancer Based on Pharmacokinetic Parameters
Fig. 6—65-year-old man with prostate-specific antigen level of 8 ng/mL and histologically proven prostate cancer
in left base with Gleason score of 6. Normal enhancement within peripheral zone masks small prostate cancer.
A and B, Coronal (A) and transverse (B) T2-weighted turbo spin-echo images show normal-appearing peripheral
zone and no evidence of hemorrhage.
C, Three-dimensional fast-field echo T1-weighted image obtained after contrast agent injection shows
homogeneous enhancement in entire peripheral zone.
D, Fusion of T2-weighted transverse image with color-encoded Ktrans (forward volume transfer constant) map is
also normal.
also been useful in the localization of tumors in
the peripheral zone in cases with barely discernible contrast enhancement on dynamic
contrast-enhanced MRI. Nevertheless, kinetic
parameters in both malignant and benign peripheral zones still showed considerable overlap, which points to a high intrinsic interpatient
variability in the vascular permeability and surface area, blood volume, and blood flow for
both malignant and benign prostatic tissues.
However, because the blood flow within the tumor tissue is heterogeneous, particularly in
prostate cancer in which there is a high ratio of
immature vessels, it is hard to determine an absolute cutoff value for these parameters.
We developed a multivariate predictor based
on T2-weighted and dynamic contrast-enhanced MRI and pharmacokinetic modeling
parameters. A cross-validated assessment of diagnostic performance suggests that the predictor performs well in predicting sextant-specific
AJR:189, October 2007
prostate cancer. The next step in validating this
predictor is to assess its performance prospectively on an independent cohort of patients.
The dynamic contrast-enhanced MRI appearances of chronic and acute prostatitis are
yet to be fully documented in the literature. In
this study, there were a total of 17 sextants
with chronic or acute prostatitis. All showed
diffuse low signal intensity on T2-weighted
images. Nine (53%) of these 17 sextants
showed early enhancement and early washout
on dynamic contrast-enhanced MRI, resulting in false-positive interpretations.
We also looked at the differences in the
pharmacokinetic parameters of chronic and
acute prostatitis. In both cases, values of
Ktrans, kep, ve, and AUGC were very close to
the results for prostate cancer, which means
these benign prostate entities still remain a
challenge for the differential diagnosis and
guidance of therapy based on dynamic con-
trast-enhanced MRI. Previous studies have
also reported considerable overlap between
prostatitis and prostate cancer when using
MR spectroscopic imaging [35]. Therefore, a
major limitation of MRI in the assessment of
prostate cancer is its restricted ability to differentiate between prostatitis and cancer.
Thus, regardless of the technique used, MRI
will likely continue to falsely identify some
benign disorders (e.g., prostatitis, benign prostatic hyperplasia) as prostate cancer.
An important limitation of our study is that
we used transrectal sonographically guided biopsy as the method of validation, which could
have led to misinterpretation of the exact locations of tumors. Thus, sextants that were
deemed normal in this study may actually have
contained a tiny focus of cancer [36]. We used
transrectal sonographically guided biopsy as
the standard because many of our patients went
on to receive treatment with radiation therapy
and whole specimens were not obtained. Thus,
by using biopsy material as the reference standard and including future radiation therapy–receiving patients as study subjects, we were
able to include a more balanced group of patients than would have been possible if only
surgical patients had been selected.
In addition, even within 5 weeks of biopsy,
there can still be considerable postbiopsy artifact on MRI. However, this is a practical
limitation of such studies because it is unethical to delay treatment for longer periods of
time for a research MRI study. Moreover, it
would be highly inefficient to perform MRI
before biopsy because many men who undergo prostate biopsy do not have prostate
cancer. It is possible that a pulse sequence
such as diffusion-weighted MRI or magnetization transfer may be less influenced by traumatic changes; however, this theory remains
to be proven, and to date, all the most commonly performed MR sequences are subject
to the effects of biopsy.
We should also note that pathologists can
differ in their interpretations of core biopsy
specimens, although most disagreements are
in assigning Gleason scores and not in determining whether cancer is present or not [37].
In this study variability was minimized by relying on a single experienced pathologist.
In summary, the results of this study show
that dynamic contrast-enhanced MRI performed at 3 T greatly increases the specificity
of conventional MRI. Dynamic contrast-enhanced MRI–derived pharmacokinetic parameters are significantly associated with
prostate cancer. The ROC curves based on
W199 — 9/27/07
Ocak et al.
these data suggest that optimally set points for
multiparametric analysis result in a sensitivity
of 78% and specificity of 88% for prostate
cancer. This is in keeping with the experience
of other studies at 1.5 T and points to an absolute “ceiling” for the detection of prostate
cancer using dynamic contrast-enhanced
MRI. Certainly for many patients, the combination of T2-weighted MRI and dynamic
contrast-enhanced MRI yields improved results at 3 T, but not for all.
It is hoped that the evolving field of molecular imaging may provide improved answers
to the problem of localizing prostate cancer;
the molecular targeting of imaging agents to
specific cell surface markers overexpressed
by cancer cells using PET, radionuclides, or
MRI-targeted contrast agents may someday
help localize prostate cancers.
We thank Yolanda McKinney, Jim Sedlacko, and Anurag K. Singh for their support
in this research.
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APPENDIX 1: Logistic Regression Model
We developed a predictor of cancer status based on using all four variables in a logistic regression model. The estimated predictor is as follows:
exp { – 2.2 + 1.19T2WI + 2.24DCE – 0.56 log ( K
) + 1.88 log ( AUGC + 0.5 ) + 0.68 log ( k ep ) }
P ( Cancer ) = -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------trans
[ 1 + exp { – 2.2 + 1.19T2WI + 2.24DCE – 0.56 log ( K
) + 1.88 log ( AUGC + 0.5 ) + 0.68 log ( k ep ) } ]
where P(Cancer) is prostate cancer, exp is exponent, T2WI is T2-weighted MRI, DCE is dynamic contrast-enhanced MRI, Ktrans is forward volume transfer constant (minв€’1), AUGC is area under the gadolinium concentration curve in the first 90 seconds after injection, and kep is reverse
reflux rate constant between extracellular space and plasma (min–1).
The variables T2-weighted MRI, dynamic contrast-enhanced MRI, log(Ktrans), and log(kep) were all statistically significant in the multivariate
model (p = 0.03, p < 0.001, p = 0.01, and p = 0.05, respectively), suggesting that the predictive ability of the multivariate model improves over
what can be achieved with a single pharmacokinetic parameter.
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