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Histogram Analysis of Perfusion Parameters from Dynamic Contrast-Enhanced MR Imaging with Tumor Characteristics and Therapeutic Response in Locally Advanced Rectal Cancer

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Purpose To explore the role of histogram analysis of perfusion parameters from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) based on entire tumor volume in discriminating tumor characteristics and predicting therapeutic response in rectal cancer. Materials and Methods Thirty-seven DCE-MRIs of locally advanced rectal cancer patients who received chemoradiation therapy (CRT) before surgery were analyzed by pharmacokinetic model for quantification and histogram analysis of perfusion parameters. The results were correlated with tumor characteristics including EGFR expression, KRAS mutation, and CRT response based on the pathologic tumor regression grade (TRG). Results The area under the contrast agent concentration-time curve (AUC) skewness was significantly lower in patients with node metastasis. The vp histogram parameters were significantly higher in group with perineural invasion (PNI). The receiver operating characteristics (ROC) curve analyses showed that mode vp revealed the best diagnostic performance of PNI. The values of Ktrans and kep were significantly higher in the group with KRAS mutation. ROC curve analyses showed that mean and mode Ktrans demonstrated excellent diagnostic performance of KRAS mutation. DCE-MRI parameters did not demonstrate statistical significance in correlating with TRG. Conclusion These preliminary results suggest that a larger proportion of higher AUC skewness was present in LN metastasis group and a higher vp histogram value was present in rectal cancer with PNI. In addition, Ktrans and kep histogram parameters showed difference according to the KRAS mutation, demonstrating the utility of the histogram of perfusion parameters derived from DCE-MRI as potential imaging biomarkers of tumor characteristics and genetic features.
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Research Article
Histogram Analysis of Perfusion Parameters from Dynamic
Contrast-Enhanced MR Imaging with Tumor Characteristics and
Therapeutic Response in Locally Advanced Rectal Cancer
Dong Myung Yeo ,1Soon Nam Oh ,2Moon Hyung Choi,2Sung Hak Lee,3
Myung Ah Lee,4and Seung Eun Jung 2
1Department of Radiology, Daejeon St. Mary’s Hospital, e Catholic University of Korea, Republic of Korea
2Departments of Radiology, Seoul St. Mary’s Hospital, e Catholic University of Korea, Republic of Korea
3Hospital Pathology, Seoul St. Mary’s Hospital, e Catholic University of Korea, Republic of Korea
4Division of Oncolog y, Department of Internal Medicine, Seoul St. Mary’s Hospital, e Catholic University of Korea, Republic of Korea
Correspondence should be addressed to Soon Nam Oh; hiohsn@gmail.com
Received 9 February 2018; Revised 22 July 2018; Accepted 7 August 2018; Published 13 August 2018
Academic Editor: Franco M. Buonaguro
Copyright ©  Dong Myung Yeo et al. is is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
Purpose. To explore the role of histogram analysis of perfusion parameters from dynamic contrast-enhanced magnetic resonance
imaging (DCE-MRI) based on entire tumor volume in discriminating tumor characteristics and predicting therapeutic response
in rectal cancer. Materials and Methods. irty-seven DCE-MRIs of locally advanced rectal cancer patients who received
chemoradiation therapy (CRT) before surgery were analyzed by pharmacokinetic model for quantication and histogram analysis
of perfusion parameters. e results were correlated with tumor characteristics including EGFR expression, KRAS mutation, and
CRT response ba sed on the pathologic tu mor regression grade (TRG). Results. e area underthe contrast agent concentration-time
curve (AUC) skewness was signicantly lower in patients with node metastasis. e vphistogram parameters were signicantly
higher in group with perineural invasion (PNI). e receiver operating characteristics (ROC) curve analyses showed that mode
vprevealed the best diagnostic performance of PNI. e values of Ktrans and kep were signicantly higher in the group with
KRAS mutation. ROC curve analyses showed that mean and mode Ktrans demonstrated excellent diagnostic performance of KRAS
mutation. DCE-MRI parameters did not demonstrate statistical signicance in correlating with TRG. Conclusion. ese preliminary
results suggest that a larger proportion of higher AUC skewness was present in LN metastasis group and a higher vphistogram
value was present in rectal cancer with PNI. In addition, Ktrans and kep histogram parameters showed dierence according to the
KRAS mutation, demonstrating the utility of the histogram of perfusion parameters derived from DCE-MRI as potential imaging
biomarkers of tumor characteristics and genetic features.
1. Introduction
Perfusion parameters from dynamic contrast-enhanced mag-
netic resonance imaging (DCE-MRI) based on pharma-
cokinetic modeling have been investigated as promising
imaging biomarkers to assess tumor biologic properties and
behaviors and to monitor and predict therapeutic eects on
the basis of tumor perfusion. Among them, the widely used
perfusion parameters extracted from the two-compartment
pharmacokinetic Tos model [] have Ktrans [volume trans-
fer constant between blood plasma and the extravascular
extracellular space (EES), which is determined by blood ow
and vascular permeability], kep (rate constant or reux rate
between blood plasma and EES, kep =Ktrans/ve), ve(fractional
EES volume), vp(fractional plasma volume), and area under
the contrast agent concentration-time curve (AUC, total
amount of contrast agent).
In rectal cancer, change in Ktrans aer neoadjuvant
chemoradiation therapy (CRT) in locally advanced rectal
cancer has been correlated with pathologically favorable
responses in previous studies [, ]. In addition, the initial
Hindawi
BioMed Research International
Volume 2018, Article ID 3724393, 9 pages
https://doi.org/10.1155/2018/3724393
BioMed Research International
Ktrans measured by preoperative DCE-MRI was also reported
to be a useful marker in predicting good response to neoad-
juvant CRT [, ].However, contradictory ndings have also
been reported. Kim et al. [] found no signicant dier-
ence in the initial value or change in perfusion parameters
between good responders and nonresponders of CRT or
between pathologic complete responders and noncomplete
responders. Furthermore, correlations of TNM stage with
perfusion parameters also showed discrepant results[, ].
Based on these previous studies, there are many factors that
inuence the variable results of tumor perfusion analysis
using DCE-MRI such as intrinsic limits in a simplied
pharmacokinetic model, measurement error of arterial input
function, dierence among postprocessing soware, small
number of cases, sampling bias of region of interest (ROI),
or inherent tumor heterogeneity [, ].In order to reduce
and avoid sampling bias and to overcome limited results
arising from intrinsic tumor heterogeneity, entire lesion-ROI
analysis has been demonstrated to be more a reproducible
method with low interobserver variability [, ]. Further-
more, histogram analysis of the entire tumor can provide
direct information on the heterogeneity of the tumor using
the value of each pixel or voxel. In recent studies, histogram
analysis based on MRI has been performed in various areas
of cancer research [–].To our knowledge, volume-based
histogram analysis of perfusion maps in rectal cancer has not
been well demonstrated in the literature. e purpose of our
study was to explore the role of histogram analysis of DCE-
MRI based on entire tumor volume in discriminating tumor
characteristics and predicting neoadjuvant CRT response.
2. Materials and Methods
2.1. Patient Population. e institutional review board ap-
proved this retrospective study, and patient informed con-
sent was waived. From December  to March , 
consecutive patients with locally advanced rectal cancer
(stages II (cT-, N, M) and III (cT-, N+, M) were
treated with CRT at our institution. e inclusion criteria
for our study were biopsy-proven adenocarcinoma of the
rectum treated with neoadjuvant CRT followed by resection
of the tumor, adequate MR examinations to delineate the
rectal cancer that included sequences to obtain a perfusion
map before CRT, and availability of detailed surgical and
histopathologic reports. In total,  met these inclusion
criteria and formed the population of this study. ere were
 men and  women. e median age was  years (range,
- years).e other  patients were excluded for no
obtainment of MR sequences for perfusion map (n = ),
image distortion by motion or metallic artifact (n = ),
and inadequate histopathologic reports (n = ). Preoperative
MR imaging including sequences to produce perfusion map
was not performed for the following reasons: other MR
equipment which was not available to produce perfusion map
was used (n = ), and patients were not expected to be
treated neoadjuvant CRT aer understaging by computed
tomography and colonoscopy (n = ).
Among this cohort, one patient was reported elsewhere;
it was addressed whether only mean values of quantitative
parameters derived from DCE-MRI are correlated with
angiogenesis and biologic aggressiveness of rectal cancer
using other soware [].All included patients underwent
CRT within a month aer MRI (median , range  days)
and underwent complete resection of the tumor as follows:
lower anterior resection (n = ), proctosigmoidectomy (n
= ), abdominoperineal resection (n = ), proctocolectomy
(n = ), and endoscopic resection (n = ). Radiation therapy
of . Gy was delivered to the pelvis in  patients and
 Gy was delivered in one patient. Twenty-two patients were
treated with -uorouracil plus leucovorin and  patients
with capecitabine.
2.2. MR Imaging Techniques. All MRI studies were per-
formed using a T MR scanner (Magnetom Verio; Siemens
Medical Solutions, Erlangen, Germany) with six-channel
phased-array surface coil (Body Matrix) combined with up to
six elements of the integrated spine coil. Before MR scanning,
approximately  - mL of sonography transmission gel
was administered for appropriate distension of the rectum,
which assisted in delineating the tumor, particularly in small
tumors. e MR images were obtained using the following
sequences. First, a sagittal image was obtained with a T-
weighted fast spin-echo sequence. A plane perpendicular
to the long axis of the rectal cancer was selected for axial
scanning, covering the rectum with the lower edge at least
 cm below the symphysis pubis and the upper edge below
the sacral promontory.
en, an oblique axial T-weighted fast spin-echo
sequence (TR/TE of /; ip angle of ;eldofview
[FOV] of  × mm; matrix size of  ×;  NEX;
slice thickness of  mm with no gap; and acquisition time of
 minutes  seconds) and an oblique axial T-weighted fast
spin-echosequence(TR/TEof/;ipangleof
;
FOV of  × mm; spectral width of  hz/pixel; matrix
size of x;  NEX; slice thickness of  mm with no
gap; acquisition time of  minutes  seconds) were applied.
Diusion-weighted MR images were acquired on the sagittal
and oblique axial planes using the single shot-echo planar
imaging technique with b of , , and  seconds/mm2;
TR/TE of /; FOV of  mm; matrix size of  ×;
 NEX; slice thickness of  mm with no slice gap; and an
acquisition time of  minutes  seconds. DCE-MRI included
two precontrast T-weighted volumetric interpolated breath-
hold examinations (D VIBE, TR/TE of ./., FOV  ×
 mm, matrix  ×,  axial slices [slice thickness,
 mm]) with dierent ip angles (,
) to determine the
T relaxation time in the tissue before the arrival of contrast
agent. is imaging was followed by a DCE series with fat
suppression on the axial plane with TR/TE of ./.; ip
angle of ; slice thickness of . mm; acquisition time of
 minute  seconds; and an intravenous bolus injection
of . mmol/kg gadobutol (Gadovist, Schering, Berlin, Ger-
many) at a rate of  mL/s, followed by a  mL saline ush.
2.3. Image Analysis. Perfusion parametric maps were
obtained using dedicated DCE-MRI soware (Olea Sphere;
Olea Medical Solutions, La Ciotat, France) with Tos model
implementation [, ].
BioMed Research International
(a) T-weighted, axial image shows an irregular fun-
gating mass (arrow) of the rectum. e tumor invades
through the rectal wall into the perirectal fat
(b) Region of interest (ROI) was manually drawn
along the edges of the tumors on T-weighted axial
images s ection by section at a thickness of  mm for
the entire tumor (not shown)
(c) e color-coded Ktrans map shows the dominant
purple color in the corresponding tumor. e calcu-
lated mean Ktrans value was . min−1
(d) e color-coded vpmap shows the dominant
red color in the corresponding tumor. e calculated
mode vpvalue was .
F : Rectal carcinoma in a -year-old female patient with perineural invasion and KRAS gene mutation (+).
e arterial input function was selected automatically
using a cluster analysis algorithm individually.
For voxel-wise histogram analysis of DCE-MRI perfusion
parameters, tumor ROIs were manually drawn along the
edges of the tumors on T-weighted axial images section
by section at a thickness of  mm for the entire tumor,
while avoiding areas of necrosis/cystic area or hemorrhage
by two abdominal radiologists (S.N.O and M.H.C with 
and  years of experience) independently. ROIs were copied
andpastedoverautomaticallydrivenperfusionmapsfrom
thesoware.en,thefollowinghistogramanalysisvalues
of each perfusion parameter were derived: mean; mini-
mum; maximum; standard deviation (SD); mode (the value
exhibiting the highest peak on the histogram); skewness;
kurtosis;th,th,th,th,th,th,th,th,and
th percentiles (the nth percentile is the point at which
n% of the voxel values that form the histogram are found
to the le) of the DCE-MRI parameters, composed of the
volume transfer constant between the blood plasma and
EES (Ktrans,min
−1); the rate constant between EES and
the blood plasma (kep,min
−1); volume of EES space per
unit volume of tissue (ve); fractional blood-plasma volume
(vp); and AUC (mMs). Skewness represents the degree of
asymmetry of a distribution. Negative skewness indicates
that the distribution is concentrated on the right of the
gure, and positive skewness indicates the converse dis-
tribution pattern. Kurtosis represents the sharpness of the
peaked of the distribution. Higher kurtosis indicates a shaper
peak.
Representative cases for histogram analysis of DCE-MRI
are shown in Figures  and .
2.4. Histopathologic Analysis. Histopathologic information
was obtained from pathology reports. We assessed morpho-
logical factors, including depth of invasion (T stage), lymph
node metastasis (N stage), and the presence of lymphatic,
BioMed Research International
(a) T-weighted, axial image shows anulcerofungating
mass (arrow) in the le wall of the rectum. e tumor
invades through the rectal wall into the perirectal fat
(b) ROIs were manually drawn along the edges of the
tumors on T-weighted axial images section by section
at a thickness of  mm for the entire tumor (not shown)
(c) e color-coded Ktransmap shows the dominant
purple color in the corresponding tumor. e calcu-
lated mean Ktrans value was . min−1
(d) e color-coded vpmap shows the dominant red
color in the corresponding tumor. e calculated mode
vpvalue was .
F : Rectal carcinoma in a -year-old male patient without perineural invasion and KRAS gene mutation (-).
vascular, and perineural invasion (PNI) as well as biologic
markers including expression of EGFR, KRAS gene muta-
tions, and tumor regression grade (TRG) as described by
Dworak et al.[], indicating pathologic grading of regression
following neoadjuvant CRT. Tumor regression was classied
accordingtothefollowingvegrades:Grade,noregres-
sion; Grade , dominant tumor mass with obvious brosis
and/or vasculopathy; Grade , dominantly brotic changes
with few tumor cells or groups (easy to nd); Grade ,
very few (dicult to nd microscopically) tumor cells in
brotic tissue with or without mucous substance; and Grade
, no tumor cells, only brotic mass (total regression or
response).
2.5. Statistical Analysis. Statistical analyses were performed
using statistical soware R version ..[] and MedCalc,
version ... [MedCalc, Mariakerke, Belgium]). To assess
interobserver reliability of the DCE-MRI parameters, mea-
surements were analyzed using the intraclass correlation
coecient (lower than ., poor agreement; .–.,
fair to good agreement; and higher than ., excellent
agreement). e cases were assigned to groups based on
histologic results including depth of invasion (T stage), lymph
node metastasis (negative versus positive), lymphovascular
invasion (negative versus positive), PNI (negative versus
positive), EGFR expression (negative versus positive), and
KRAS gene mutation (negative versus positive). To assess
neoadjuvant CRT response predictability, the patents were
also divided into groups of TRG nonresponders (Grades
, , and ) and TRG responders (Grades  and ) and
complete response (CR) group and non-CR group. e values
from histogram analysis of DCE-MRI parameters (Ktrans,k
ep,
ve,v
p, and AUC; mean, minimum, maximum, SD, mode,
skewness,kurtosis,th,th,th,th,th,th,th,
th, and th percentile value) are compared between the
groups using the Mann–Whitney U test with the moonBook
package [].
For the parameters that demonstrated statistically signif-
icant dierence between the groups, receiver operating char-
acteristics (ROC) curve analysis was performed to calculate
the sensitivity, specicity, and diagnostic accuracy.
BioMed Research International
T : Interobserver intraclass correlation coecient for measurements of perfusion parameters.
Parameter Ktrans vpAUC kep ve
Mean . (., .) . (., .) . (., .) . (., .) . (., .)
Minimum . (., .) . (., .) . (., .) . (., .) . (., .)
th percentile . (-., .) . (., .) . (., .) . (., .) . (., .)
th percentile . (-., .) . (., .) . (., .) . (., .) . (., .)
th percentile . (., .) . (., .) . (., .) . (., .) . (., .)
th percentile . (.,.) . (., .) . (., .) . (., .) . (., .)
th percentile . (., .) . (., .) . (., .) . (., .) . (., .)
th percentile . (., .) . (., .) . (., .) . (., .) . (., .)
th percentile . (., .) . (., .) . (., .) . (., .) . (., .)
th percentile . (., .) . (., .) . (., .) . (., .) . (., .)
th percentile . (., .) . (., .) . (., .) . (., .) . (., .)
Maximum . (., .) . (., .) . . (., .) .
Standard deviation . (., .) . (., .) . (., .) . (., .) . (., .)
Mode . (., .) . (., .) . (., .) . (., .) . (., .)
Skewness . (., .) . (., .) . (., .) . (., .) . (., .)
Kurtosis . (., .) . (., .) . (., .) . (., .) . (., .)
Note. Data in parentheses are %condence intervals.
3. Results
3.1. Correlation with Prognostic Histologic Results and DCE-
MRI Parameters. Histogram analysis measurements of per-
fusion parameters showed overall excellent interreader agree-
ment except for some minimum or lower percentile mea-
surements. Table  summarizes the interobserver agreement
correlation coecients using the corresponding intraclass
correlation coecients.
Comparisons of DCE-MRI parameters of rectal cancer by
group, classied according to histologic results and molecular
biology, are summarized in Table .
In patients with lymph node metastasis, AUC skewness
was signicantly lower than that in patients without lymph
node metastasis (-.; median [-.,-.; interquartile range]
versus -. [-.,.], p= .).erefore, a larger proportion
of higher AUC values were present in the nodal metastasis
group compared to the group with nonnodal metastasis. e
area under the ROC curve (𝐴z) of AUC skewness was .
(% CI: .-.; sensitivity .%, specicity .%) for
reader  and . (% CI: .-.; sensitivity .%,
specicity .%) for reader . AUC kurtosis and vpkurtosis
also showed higher values in the nodal metastasis group,
which was represented by a sharper histogram peak, in reader
only.
e vp-associated histogram values (mean, thth
percentile, skewness, kurtosis, and mode) showed statisti-
cally signicant correlation with PNI. ROC curve analyses
revealed that mode vpshowed the best diagnostic perfor-
mance of PNI (𝐴zof mode vp.; % CI: .-; sensi-
tivity .%, specicity .% for reader ; 𝐴zof modevp.;
% CI: .-.; sensitivity .%, specicity .% for
reader ).
e Ktrans (mean, SD, thth percentile, and mode)
and kep histogram values (mean, thth percentile, and
kurtosis) were signicantly higher in the group with KRAS
gene mutation and vekurtosis was lower in KRAS-mutated
than in nonmutated tumors. ROC curve analyses showed
that mean Ktrans and mode Ktrans demonstrated excellent
diagnostic performance of KRAS gene mutation (𝐴zof
mean Ktrans ., % CI: .-.; sensitivity .%,
specicity .%; 𝐴zof mode Ktrans ., % CI: .-
.; sensitivity %, specicity .% for reader ).
Other histologic (T stage, lymphatic invasion, and vascu-
lar invasion) and immunohistochemical (EGFR expression)
results were not associated with any dierence in DCE-MRI
parameters.
3.2. Correlation with Treatment Response aer Neoadjuvant
CRT and DCE-MRI Parameters. Of the total  patients, 
were in TRG ,  were in TRG ,  was in TRG , and 
were in TRG  (CR). e mean Ktrans values of the responder
and nonresponders groups were similar (.; median [.,
.; interquartile range] versus .[., .], p= .). e
mean kepwas lower in the TRG responder group compared
to the TRG nonresponder group, but the dierence was not
statistically signicant (. ±. versus . ±., p= .).
e mean Ktrans and mean kep were lower in the CR
group compared to the non-CR group (.[.; .] versus
.[.; .], p= .; . [.,.] versus . [.; .], p=
., respectively), but the dierences were not statistically
signicant. No other DCE-MRI parameter histogram analy-
sis values were signicantly correlated with CRT treatment
response. e mean, maximum, skewness, and kurtosis of
Ktrans and kep, based on TRG and CR, are summarized in
Table .
4. Discussion
e aim of the present study was to explore the role of
histogram analysis of model-based perfusion parameters
from DCE-MRI in rectal cancer for discriminating tumor
BioMed Research International
T : Correlation of histogram analysis of perfusion parameters with biologic aggressiveness.
Parameter Reader  Reader 
Yes(n=) No(n=) PvalueAzYes(n=) No(n=) PvalueAz
Biologic aggressiveness
AUC skewness -. (-.;-.) -. (-.;.) 0.016 . -.(-.;-.) -.(-.;.) 0.012 .
AUC kurtosis -. (-.;.) -. (-.;-.) 0.036 . -. (-.;.) -.(-.;-.) 0.098 .
vpkurtosis -. (-.;.) . (-.;.) 0.036 . -. (-.;.) . (-.;.) 0.052 .
Ye s ( n =  ) No ( n =   ) Pvalue AzYe s ( n =  ) N o ( n =   ) Pvalue Az
PNI
vpmean . (.;.) . (.;.) 0.042 . . (.;.) . (.;.) 0.046 .
vpth percentile . (.;.) . (.;.) 0.011 . . (.;.) . (.;.) 0.013 .
vpth percentile . (.;.) . (.;.) 0.022 . . (.;.) . (.;.) 0.035 .
vpth percentile . (.;.) . (.;.) 0.024 . . (.;.) . (.;.) 0.039 .
vpth percentile . (.;.) . (.;.) 0.027 . . (.;.) . (.;.) 0.042 .
vpth percentile . (.;.) . (.;.) 0.029 . . (.;.) . (.;.) 0.035 .
vpth percentile . (.;.) . (.;.) 0.032 . . (.;.) . (.;.) 0.035 .
vpth percentile . (.;.) . (.;.) 0.035 . . (.;.) . (.;.) 0.039 .
vpth percentile . (.;.) . (.;.) 0.046 . . (.;.) . (.;.) 0.042 .
vpskewness . (.;.) . (.;.) 0.022 . . (.;.) . (.;.) 0.020 .
vpkurtosis -. (-.;-.) . (-.;.) 0.018 . -. (-.;-.) . (-.;.) 0.035 .
kpmode . (.;.) . (.;.) 0.002 0.859 . (.;.) . (.;.) 0.016 0.783
Yes (n = ) No (n = ) Pvalue AzYes(n=) No(n=) Pvalue Az
KRAS mutation
Ktransmean . (.;.) . (.;.) 0.009 0.788 . (.;.) . (.;.) 0.010 0.784
KtransSD . (.;.) . (.;.) 0.020 . . (.;.) . (.;.) 0.035 .
Ktransth percentile . (.;.) . (.,.) 0.039 . . (.;.) . (.,.) 0.048 .
Ktransth percentile . (.;.) . (.;.) 0.035 . . (.;.) . (.;.) 0.048 .
Ktransth percentile . (.;.) . (.;.) 0.028 . . (.;.) . (.;.) 0.032 .
Ktransth percentile . (.;.) . (.;.) 0.014 . . (.;.) . (.;.) 0.023 .
Ktransh percentile . (.;.) . (.;.) 0.023 . . (.;.) . (.;.) 0.039 .
Ktransmode . (.;.) . (.;.) 0.007 0.793 . (.;.) . (.;.) 0.007 0.793
kep mean . (.; .) . (.;.) 0.018 . . (.; .) . (.;.) 0.044 .
kepth percentile . (.;.) . (.;.) 0.025 . . (.;.) . (.;.) 0.032 .
kepth percentile . (.;.) . (.;.) 0.028 . . (.;.) . (.;.) 0.028 .
kepth percentile . (.;.) . (.;.) 0.018 . . (.;.) . (.;.) 0.039 .
kepth percentile . (.;.) . (.;.) 0.020 . . (.;.) . (.;.) 0.035 .
kepth percentile . (.;.) . (.; .) 0.028 . . (.;.) . (.;.) 0.032 .
kepth percentile . (.;.) . (.;.) 0.016 . . (.;.) . (.;.) 0.032 .
kepth percentile . (.;.) . (.;.) 0.025 . . (.;.) . (.;.) 0.048 .
vekurtosis . (-.;.) . (.;.) 0.035 . . (-.;.) . (.;.) 0.018 .
Note. All gures of perfusion parameters in the above table have been rounded to one decimal place and are presented as median value (interquartile range)
according to the data distribution.
Numbers in bold are statistically signicant P-values. Parameters in bold are high in area under the ROC curve.
AUC, area under the concentration curve; PNI, perineural invasion; SD, standard deviation.
Determined with the Mann-Whitney U test.
Az= area under the ROC curve.
characteristics and predicting CRT response. Our results
showed that histogram values from DCE-MRI parameters
correlated with prognostic factors including LN metastasis,
PNI, and KRAS gene mutation. e histogram analysis values
of DCE-MRI parameters were not correlated with pathologic
CRT response.
Previous studies have reported discrepant results regard-
ing the correlation of TNM staging and DCE-MRI param-
eters. Yao et al. suggested that Ktrans positively correlates
with LN metastasis []. However, Kim et al. reported no
relationship between TN staging and Ktrans and ve[]. In our
study, Ktrans,k
ep,andv
erevealed no correlation with TNM
staging, and the AUC data of the group with nodal metastasis
demonstrated wider spread to the right of the mean compared
to that of the group with nonnodal metastasis, illustrating
that a larger proportion of patients with nodal metastasis had
higher AUC values than patients without nodal metastasis.
To the best of our knowledge, there have been no studies
regarding the correlations between the PNI of rectal cancer
and DCE-MRI parameters. Our present study showed a
BioMed Research International
T : Correlation with treatment response of neoadjuvant chemoradiotherapy aer rectal cancer.
Treatment Response Parameter Reader  Reader 
TRG,,(n=) TRG , (n=) PvalueTRG,,(n=) TRG, (n=) Pvalue
TRG
Ktransmean . (.;.) . (.;.) . . (.;.) . (.;.) .
Ktransmaximum . (.;.) . (.;.) . . (.;.) . (.;.) .
Ktransskewness . (.;.) . (.;.) . . (.;.) . (.;.) .
Ktranskurtosis . (-.;.) -. (-.;.) . . (-.;.) -. (-.;.) .
kepmean . (.;.) . (.;.) . . (.;.) .(.;.) .
kepmaximum . (.;.) . (.;.) . . (.;.) . (.;.) .
kepskewness . (.;.) . (.;.) . . (.;.) . (.;.) .
kepkurtosis . (.;.) . (-.;.) . . (.;.) . (-.;.) .
CR (n=) nonCR (n=) Pvalue CR (n=) nonCR (n=) Pvalue
CR
Ktransmean . (.;.) . (.;.) . . (.;.) . (.;.) .
Ktransmaximum . (.;.) . (.;.) . . (.;.) . (.;.) .
Ktransskewness . (.;.) . (.; .) . . (.;.) . (.;.) .
Ktranskurtosis . (-.;.) .(-.;.) . . (-.;.) .(-.;.) .
kepmean . (.;.) . (.;.) . . (.;.) . (.;.) .
kepmaximum . (.; .) . (.;.) . . (.;.) . (.;.) .
kepskewness . (.;.) . (.;.) . . (.;.) . (.;.) .
kepkurtosis . (.;.) .(-.;.) . . (.;.) .(-.;.) .
Note. TRG, tumor regression grade; TRG, no regression; TRG, dominant tumor mass with obvious brosis and/or vasculopathy; TRG, dominantly brotic
changes with few tumor cells or groups; TRG, very few tumor cells in brotic tissue with or without mucous substance; TRG, no tumor cells, only brotic
mass; TRG nonresponders (Grades ,, and ) and TRG responders (Grades  and ); CR, complete response.
All gures of perfusion parameters in the above table have been rounded to one decimal place and are presented as median value (interquartile range) according
to the data distribution.
Determined with the Mann-Whitney U test.
signicant correlation between PNI and vp.epresence
of PNI in rectal cancer is associated with a signicantly
worse prognosis [, ], indicating that a high vpis a poor
prognostic factor.
In patients with metastatic colorectal cancer, treatment
using EGFR-directed antibodies such as cetuximab or pan-
itumumab is recommended. However, KRAS (exon or
nonexon) or NRAS mutations are known to be resistant to
EGFR-targeting agents; therefore, anti-EGFR therapy cannot
be used in patients with RAS gene mutations. In the present
study, there were no patients with NRAS mutation, and 
patients (. %, /) with KRAS mutation. Most histogram
values of Ktrans and kep were higher in the KRAS mutation
group. In our previous study, there was also a higher mean
Ktrans in the group with KRAS mutation, although the
dierence did not reach statistical signicance[]. However,
the present study showed statistical signicance of higher
Ktrans and kep correlating with presence of aKRAS gene
mutation. It is well known that the mutant KRAS oncogene
can induce or strongly upregulate various proangiogenic
factors such as vascular endothelial growth factor/vascular
permeability factor (VEGF/VPF) and transforming growth
factors 𝛽(TGF- 𝛽)or𝛼(TGF- 𝛼) in a cascade manner.
Although the precise mechanism has not been discovered,
the current study suggests the possibility of MRI-derived
perfusion parameters reecting an event at the genetic level
of tumorigenesis[, ].Although further studies of clinical
validity with a larger sample size are required, Ktrans or
kep may be important imaging biomarkers in predicting
an individual’s response to anti-EGFR therapy, even before
genotyping.
Contrary to the signicant results regarding the useful-
ness of mean Ktrans for response assessment or prediction of
CRTinpreviousstudies[,],ourstudydemonstrated
no correlation of histogram values of Ktrans,k
ep,orv
e
with CRT response. However, several studies have reported
similar results. Lim et al.[]demonstrated that Ktrans was not
predictive of TRG, and Kim et al. [] also reported that
Ktrans,k
ep,andv
eare not useful to assess or predict CR.
Furthermore, Intven et al. [] revealed that changes in Ktrans
aer CRT have no additive value for response assessment
in the combination study of T-weighted MR volumetry,
diusion-weighted MR imaging, and DCE-MRI. Further
studies with larger sample sizes are needed to investigate
clinical validation and additive values of perfusion MRI for
response assessment or prediction of CRT.
Our study has several limitations. First, this is a retro-
spective study and therefore has an unavoidable selection
bias. Second, the sample size was relatively small and was
thus insucient to suggest optimal threshold values of DCE-
MRI parameters for predicting prognosis. ird, we did
not analyze the MRI aer CRT and thus cannot assess the
changes in perfusion parameters aer CRT. However, in
a clinical setting, there is actually less interest in assess-
ing treatment response aer CRT compared to predicting
response before CRT. For this reason, we performed this
study to explore the role of DCE-MRI in predicting treatment
response before CRT. ese preliminary results suggest that
BioMed Research International
a larger proportion of higher AUC skewness was present in
LN metastasis group and a higher vphistogram value was
present in rectal cancer with PNI. In addition, Ktrans and
kep histogram parameters showed dierence according to the
KRAS mutation, demonstrating the utility of the histogram
of perfusion parameters derived from DCE-MRI as potential
imaging biomarkers of tumor characteristics and genetic
features.
Data Availability
is study is based on medical images of patients. Sharing
data is believed to be a possible source of legal and ethical
issues. e soware used for image analysis is currently
commercially available.
Conflicts of Interest
e authors have no conicts of interest with regard to this
study.
Acknowledgments
is study was funded by Ministry of Education, Republic of
Korea (RDAB).
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... They found that responsive tumors were more permeable (p = 0.03). Higher K trans values have also been found in responders in several other studies [48,72,99,115] but not in others [62,65,75,102]. In our analysis, K trans values were higher in responders but this did not reach significance (MD 0.15, 95% CI −0.13, 0.42, p = 0.29). ...
... Other perfusion parameters such as K ep , V2 [72], K21 and TPP [51], and the kinetic parameter 'late slope' [70] have also shown promise in differentiating between responders and non-responders. Yeo DM et al [99] performed a histogram analysis of perfusion parameters, which demonstrated no significant difference between treatment groups. T1 Mapping De Vries AF et al [30,31,34,61] and Kremser C et al [36] used dynamic contrast enhanced T1 mapping with FLASH sequencing to identify a perfusion index (PI). ...
... In regards to functional MRI, ADC values were significantly lower in pCR patients in several studies [15,38,46,59,63,76,81,82,84]. IVIM parameters (D, f and D*) were found to have mixed predictive value [86,95], as did DCE-MRI parameters (K trans , K ep and IAUGC) [62,65,70,72,99,115]. ...
Article
Introduction: The standard of care for locoregionally advanced rectal cancer is neoadjuvant therapy (NA CRT) prior to surgery, of which 10-30% experience a complete pathologic response (pCR). There has been interest in using imaging features, also known as radiomics features, to predict pCR and potentially avoid surgery. This systematic review aims to describe the spectrum of MRI studies examining high-performing radiomic features that predict NA CRT response. Areas covered: This article reviews the use of pre-therapy MRI in predicting NA CRT response for patients with locoregionally advanced rectal cancer (T3/T4 and/or N1+). The primary outcome was to identify MRI radiomic studies; secondary outcomes included the power and the frequency of use of radiomic features. Expert opinion: Advanced models incorporating multiple radiomics categories appear to be the most promising. However, there is a need for standardisation across studies with regards to; the definition of NA CRT response, imaging protocols, and radiomics features incorporated. Further studies are needed to validate current radiomics models and to fully ascertain the value of MRI radiomics in the response prediction for locoregionally advanced rectal cancer.
... However, to the best of our knowledge, the present study is only one of few studies to evaluate the predictive performance of histogram parameters for the PNI status. The other study by Yeo et al. [33] derived histogram parameters from dynamic contrast-enhanced MRI of 37 patients and showed that fractional plasma volume histogram parameters were significantly associated with PNI. The voxel gray-level intensity of dynamic contrast-enhanced MRI depends on the amount and distribution of the injected contrast agent, which may be affected by the variable hemodynamic conditions among the patients [34]. ...
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Purpose To investigate the utility of histogram analysis of zoomed EPI diffusion-weighted imaging (DWI) for predicting the perineural invasion (PNI) status of rectal cancer (RC). Methods This prospective study evaluated 94 patients diagnosed with histopathologically confirmed RC between July 2020 and July 2021. Patients underwent preoperative rectal magnetic resonance imaging (MRI) examinations, including the zoomed EPI DWI sequence. Ten whole-tumor histogram parameters of each patient were derived from zoomed EPI DWI. Reproducibility was evaluated according to the intra-class correlation coefficient (ICC). The association of the clinico-radiological and histogram features with PNI status was assessed using univariable analysis for trend and multivariable logistic regression analysis with β value calculation. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic performance. Results Forty-two patients exhibited positive PNI. The inter- and intraobserver agreements were excellent for the histogram parameters (all ICCs > 0.80). The maximum (p = 0.001), energy (p = 0.021), entropy (p = 0.021), kurtosis (p < 0.001), and skewness (p < 0.001) were significantly higher in the positive PNI group than in the negative PNI group. Multivariable analysis showed that higher MRI T stage [β = 2.154, 95% confidence interval (CI) 0.932–3.688; p = 0.002] and skewness (β = 0.779, 95% CI 0.255–1.382; p = 0.006) were associated with positive PNI. The model combining skewness and MRI T stage had an area under the ROC curve of 0.811 (95% CI 0.724–0.899) for predicting PNI status. Conclusion Histogram parameters in zoomed EPI DWI can help predict the PNI status in RC. Graphical abstract
... Additionally, Palmisano et al. prospectively analyzed quantitative pre-CRT DCE on 21 rectal cancer patients using a histogram-based approach and showed v e skewness and kurtosis were significantly higher in non-GR than GR patients [60]. However, Yeo et al. applied histogram analysis on pre-CRT DCE MRI, and did not find a significant correlation between quantitative DCE parameters and TRG [68]. Zou et al. implemented texture analysis on pre-and Content courtesy of Springer Nature, terms of use apply. ...
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Purpose of Review Early response prediction for locally advanced rectal cancer (LARC) provides an opportunity for response-tailored treatment management. The goal of this review is to summarize recent advances in applying functional MRI, such as diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI), to predict treatment response for LARC patients, as well as to discuss the associated limitations and future directions. Recent Findings Many recent studies incorporated advanced data analysis methods, such as radiomics and deep learning, to enhance prediction performance. Multi-parametric imaging has also become a trend that utilizes complementary information from each technique. However, there are wide variations in patient enrollment, imaging time points, scan parameters, and treatment response endpoint definitions, which leads to a range of findings among these studies. Moreover, small sample size and lack of independent validation of most studies also weaken conclusions. Summary Functional MRI has been shown as a potential early biomarker for rectal cancer treatment response estimation. To incorporate functional MRI into clinical workflow, future work with large standardized data are warranted.
... DCE-MRI has been used as an imaging biomarker to evaluate of tumor heterogeneity, chemotherapy response and prognosis of lung cancer [15,16]. Considering the heterogeneity of tumor tissue and microvascular distribution, the histogram analysis of perfusion parameters has been applied in various fields of tumor research [17]. Commonly used histogram parameters include: meanvalue, quantile, skewness, kurtosis, uniformity, entropy, energy, etc. ...
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... In a recently published paper, only a correlation between post pCRT skewness and TRG was revealed [18]. Moreover, no significant correlations have been reported between histogram analysis of dynamic contrast-enhanced MRI with TRG [19]. 18 F-fluorodeoxyglucose positron emission tomography/ computed tomography scan ( 18 F-FDG PET/CT) is another useful tool, even if not routinely recommended in patients with LARC, that in a recent meta-analysis showed a sensitivity of 73% and a specificity of 77% for the identification of responders to pCRT [20]. ...
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Background The aim of our study was to investigate the correlation among T2-weighted (T2w) images, apparent diffusion coefficient (ADC) maps, ¹⁸ F-fluorodeoxyglucose ( ¹⁸ F-FDG) positron emission tomography (PET) images, histogram analysis and the pathological response in locally advanced rectal cancer (LARC) after preoperative chemoradiotherapy (pCRT). Methods Patients with LARC were prospectively enrolled between February 2015 and August 2018 and underwent PET/magnetic resonance imaging (MRI). MRI included T2w and diffusion-weighted imaging (DWI)-sequences. ADC maps and PET images were matched to the T2w images. Voxel-based standardized uptake values (SUVs,) ADC and T2w-signal-intensity values were collected from the volumes of interest (VOIs) and mean, skewness and kurtosis were calculated. Spearman’s correlation coefficient was applied to evaluate the correlation among the variables and tumor regression grade (TRG), T stage, N stage and fibrosis. Results Twenty-two patients with biopsy-proven LARC in the low or mid rectum were enrolled [17 males, mean age was 69 years (range 49–85 years)]. Seven patients experienced complete regression (TRG1). A significant positive correlation was found between SUV mean values ( ρ = 0.480; p = 0.037) and TRG. No other significant correlations were found. Conclusions Histogram analysis of SUV values is a predictor of TRG in LARC.
... Various MR imaging modules [11][12][13][14][15][16] (i.e. diffusionweighted MR imaging [DWI], magnetic resonance spectroscopy [MRS], arterial spin labelling [ASL]) and advanced analysis for routine MR imaging [17][18][19][20][21] have been introduced in the oncologic field to evaluate tumoral biological characteristics and predict KRAS status. Nevertheless, the radiologic features of rectal cancer with KRAS mutation have not yet been fully described. ...
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This article reviews the correct methodology of MRI in patients with rectal cancer (RC). The technical aspects of scanning, the advantages and disadvantages of the various sequences and methods used, the particularities of preparation and positioning, and their rationale are considered. Compliance with the correct methodology allows to obtain high-quality and high-informative images, thereby allowing to assess the extent of the process, the invasion of adjacent organs and structures with high accuracy.
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Until now, several investigators have explored the value of diffusion-weighted magnetic resonance imaging (DWI) for the preoperative tumor grading of endometrial cancer. However, the diagnostic value of DWI with quantitative analysis of apparent diffusion coefficient (ADC) has been controversial. To explore the role of histogram analysis of ADC maps based on entire tumor volume in determining the grade of endometrial cancer. This study was IRB-approved with waiver of informed consent. Thirty-three patients with endometrial cancer underwent DWI (b = 0, 600, 1000 s/mm(2)), and corresponding ADC maps were acquired. Regions of interest (ROIs) were drawn on all slices of the ADC map in which the tumor was visualized including areas of necrosis to derive volume-based histographic ADC data. Histogram parameters (5th-95th percentiles, mean, standard deviation, skewness, kurtosis) were correlated with histological grade using one-way ANOVA with Tukey-Kramer test for post hoc comparisons, and were compared between high (grade 3) and low (grades 1/2) grade using Student t-test. ROC curve analysis was performed to determine the optimum threshold value for each parameter, and their corresponding sensitivity and specificity. The standard deviation, quartile, 75th, 90th, and 95th percentiles of ADC showed significant differences between grades (P ≤ 0.03 for all) and between high and low grades (P ≤ 0.024 for all). There were no significant correlations between tumor grade and other parameters. ROC curve analysis yielded sensitivities and specificities of 75% and 96%, 62.5% and 92%, 100% and 52%, 100% and 72%, and 100% and 88%, using standard deviation, quartile, 75th, 90th, and 95th percentiles for determining high grade with corresponding areas under the curve (AUCs) of 0.787, 0.792, 0.765, 0.880, and 0.925, respectively. Histogram analysis of ADC maps based on entire tumor volume can be useful for predicting the histological grade of endometrial cancer. The 90th and 95th percentiles of ADC were the most promising parameters for differentiating high from low grade.
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To determine whether quantitative perfusion parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) correlate with immunohistochemical markers of angiogenesis in rectal cancer. Preoperative DCE-MRI was performed in 63 patients with rectal adenocarcinoma. Transendothelial volume transfer (K(trans) ) and fractional volume of the extravascular-extracellular space (Ve) were measured by Interactive Data Language software in rectal cancer. After surgery, microvessel density (MVD) and vascular endothelial growth factor (VEGF) expression scores were determined using immunohistochemical staining of rectal cancer specimens. Perfusion parameters (K(trans) , Ve) of DCE-MRI in rectal cancer were found to be correlated with MVD and VEGF expression scores by Spearman's rank coefficient analysis. T stage and N stage (negative or positive) were correlated with perfusion parameters and MVD. Significant correlation was not found between any DCE-MRI perfusion parameters and MVD (rs = -0.056 and p = 0.662 for K(trans) ; rs = -0.103 and p = 0.416 for Ve), or between any DCE-MRI perfusion parameters and the VEGF expression score (rs = -0.042, p = 0.741 for K(trans) ; r = 0.086, p = 0.497 for Ve) in rectal cancer. TN stage showed no significant correlation with perfusion parameters or MVD (p > 0.05 for all). DCE-MRI perfusion parameters, K(trans) and Ve, correlated poorly with MVD and VEGF expression scores in rectal cancer, suggesting that these parameters do not simply denote static histological vascular properties.
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Perineural invasion (PNI) is a possible route for metastatic spread in various cancer types, including colorectal cancer (CRC). PNI is linked to poor prognosis, but systematic analyses are lacking. This study systematically reviews the frequency and impact of PNI in CRC. A literature search was performed using PubMed database from inception to January 1, 2014. Data were analyzed using Review Manager 5.3. A quality assessment was performed on the basis of modified REMARK criteria. Endpoints were local recurrence (LR), 5-year disease-free survival (5yDFS), 5-year cancer-specific survival (5yCSS), and 5-year overall survival (5yOS). Meta-analysis was performed in terms of risk ratios (RR) and hazard ratios (HR) with 95% confidence interval (95% CI). In this meta-analysis, 58 articles with 22,900 patients were included. PNI was present in 18.2% of tumors. PNI is correlated with increased LR (RR 3.22, 95% CI, 2.33-4.44) and decreased 5yDFS (RR 2.35, 95% CI, 1.66-3.31), 5yCSS (RR 3.61, 95% CI, 2.76-4.72), and 5yOS (RR 2.09, 95% CI, 1.68-2.61). In multivariate analysis PNI remains an independent prognostic factor for 5yDFS, 5yCSS, and 5yOS (HR 2.35, 95% CI, 1.97-3.08; HR 1.91, 95% CI, 1.50-2.42; and HR 1.85, 95% CI, 1.63-2.12, respectively). We confirmed the strong impact of PNI for LR and survival in CRC. The prognostic value of PNI is similar to that of well-established prognostic factors as depth of invasion, differentiation grade, lymph node metastases, and lymphatic and extramural vascular invasion. Therefore, PNI should be one of the factors in the standardized reporting of CRC and might be considered a high-risk feature.
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PurposeTo determine the ability of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathological complete response (pCR) before preoperative chemoradiotherapy (CRT) in locally advanced rectal cancer.Materials and Methods In a prospective clinical trial, 38 enrolled patients underwent pre- and post-CRT DCE-MRI at 3.0T. The tumor length and the following perfusion parameters (Ktrans, kep, ve) were measured for the tumor and compared between the pCR group and the non-pCR group, as well as before and after CRT. For categorical variable comparison, the Kruskal-Wallis test was used. P < 0.05 was considered significant.ResultsNo difference in tumor length was found between the pCR and non-pCR group pre- and post-CRT (P = 0.26 (0.15,0.45), 0.35 (0.21,0.52), respectively). Before CRT, the mean tumor Ktrans in the pCR group was significantly higher than in the non-pCR group (P = 0.01). A Ktrans of 0.66 emerged as the best cutoff for distinguishing pCR from non-pCR. Regarding kep and ve, significant differences were also observed between the pCR and non-pCR groups (P = 0.02, 0.01, respectively). The mean Ktrans, kep, and ve values post-CRT were lower in the pCR group than in the non-pCR group, although there was no significant difference (P = 0.10 (0.04,0.16), 0.11 (0.07,0.26), 0.10 (0.06,0.23), respectively).Conclusion Before neoadjuvant chemoradiotherapy in rectal cancer, DCE-MRI can distinguish between complete and incomplete response using a Ktrans threshold of 0.66 with a sensitivity of 100%. J. Magn. Reson. Imaging 2014.
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Background: The value of diffusion-weighted imaging (DWI) for reliable differentiation between pathologic complete response (pCR) and residual tumor is still unclear. Recently, a few studies reported that histogram analysis can be helpful to monitor the therapeutic response in various cancer research. Purpose: To investigate whether post-chemoradiotherapy (CRT) apparent diffusion coefficient (ADC) histogram analysis can be helpful to predict a pCR in locally advanced rectal cancer (LARC). Material and methods: Fifty patients who underwent preoperative CRT followed by surgery were enrolled in this retrospective study, non-pCR (n = 41) and pCR (n = 9), respectively. ADC histogram analysis encompassing the whole tumor was performed on two post-CRT ADC600 and ADC1000 (b factors 0, 600 vs. 0, 1000 s/mm(2)) maps. Mean, minimum, maximum, SD, mode, 10th, 25th, 50th, 75th, 90th percentile ADCs, skewness, and kurtosis were derived. Diagnostic performance for predicting pCR was evaluated and compared. Results: On both maps, 10th and 25th ADCs showed better diagnostic performance than that using mean ADC. Tenth percentile ADCs revealed the best diagnostic performance on both ADC600 (AZ 0.841, sensitivity 100%, specificity 70.7%) and ADC1000 (AZ 0.821, sensitivity 77.8%, specificity 87.8%) maps. In comparison between 10th percentile and mean ADC, the specificity was significantly improved on both ADC600 (70.7% vs. 53.7%; P = 0.031) and ADC1000 (87.8% vs. 73.2%; P = 0.039) maps. Conclusion: Post-CRT ADC histogram analysis is helpful for predicting pCR in LARC, especially, in improving the specificity, compared with mean ADC.
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Background Patient selection for organ sparing treatment after good response to neo-adjuvant chemoradiation (CRT) for locally advanced rectal cancer is challenging as no optimal restaging modality is available after CRT. In this study, we assessed the value of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for rectal cancer pathological response prediction.Methods In 51 patients with locally advanced rectal cancer, the tumor volume and volume transfer constant (Ktrans) were obtained at 3 Tesla before CRT and surgery. The predictive potential for pathological complete response (pCR) and good response (GR) was assessed. GR was defined as pCR and near-pCR based on the tumor regression grade.ResultsThe GR group consisted of 10 patients (19.6%) with six pCR (11.8%). Both the post-CRT tumor volume and post-CRT Ktrans values and the relative change in volume (ΔVolume) and Ktrans (ΔKtrans) were predictive for pathological response. ΔKtrans showed the best predictive potential with a positive predictive value (PPV) of 100% for GR using a cutoff value of 32% reduction in Ktrans. For pCR the best PPV was 80% with a multiparameter model containing ΔVolume and ΔKtrans.ConclusionDCE-MRI has predictive potential for pathological response after CRT in rectal cancer with the relative ΔKtrans being the most predictive parameter.J. Magn. Reson. Imaging 2014. © 2014 Wiley Periodicals, Inc.
Article
To investigate whether quantitative parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are correlated with angiogenesis and biologic aggressiveness of rectal cancer. A total of 46 patients with rectal cancer underwent DCE-MRI. Using a two-compartmental model, quantitative parameters (K(trans) , kep , ve , and iAUC) were calculated from the whole-transverse region of interest (ROI) and high K(trans) area ROI of entire tumors. Histological specimens were analyzed for tumor size; T/N stage; lymphatic, vascular, perineural invasion; expression of epidermal growth factor receptor (EGFR); and KRAS gene mutations. Tumor angiogenesis was evaluated based on the microvessel density (MVD) and the expression level of the vascular endothelial growth factor. Correlations of the DCE-MRI parameters with histological markers and angiogenesis were determined using Student's t-test and analysis of variance (ANOVA). The mean kep from high K(trans) area ROIs showed a significantly positive correlation with MVD (P = 0.030, r = 0.514, R(2) = 0.264). The mean kep from the whole-transverse ROIs showed a significant inverse correlation with T stage (T1 vs. T2-4, P = 0.021). EGFR-positive cancer displayed higher mean K(trans) (P = 0.045) and kep (P = 0.038) than EGFR-negative cancer in whole-transverse ROIs. These preliminary results suggest that the determination of kep of high K(trans) area permits the noninvasive estimation of tumor angiogenesis in rectal cancer and that DCE-MRI parameters can be used as imaging biomarkers to predict the biologic aggressiveness of the tumor and patient prognosis.J. Magn. Reson. Imaging 2013. © 2013 Wiley Periodicals, Inc.
Article
To evaluate the usefulness of perfusion parameters derived from dynamic contrast-enhanced MR imaging (DCE-MRI) for assessing the therapeutic response to neoadjuvant chemoradiation therapy (CRT) for locally advanced rectal cancer (LARC). Fifty patients with LARC (≥T3 or lymph-node metastasis) who underwent CRT and subsequent surgery, were included in this study. All patients underwent pre- and post-CRT DCE-MRI on a 1.5 Tesla unit. By using a postprocessing software, the following perfusion parameters (K(trans) , kep , ve ) were measured for tumor. Those perfusion parameters were compared not only between the T-downstaged group and the nondownstaged group, but also before and after CRT in each group. After CRT, the mean K(trans) (min(-1) ) significantly decreased from 1.24 ± 0.53 to 0.76 ± 0.45 in the T-downstaged group (n = 24) (P = 0.0007), whereas it did not significantly decrease in the nondownstaged group (n = 26) (from 1.02 ± 0.53 to 0.87 ± 0.48, P = 0.24). The percentage difference between pre- and post-CRT K(trans) in the T-downstaged group was significantly higher than that in the nondownstaged group (43%, 16%, respectively, P = 0.0092). However, none of the other parameters showed significant differences. A large decrease in the mean K(trans) after CRT was associated with a good therapeutic response to CRT for LARC.J. Magn. Reson. Imaging 2013;. © 2013 Wiley Periodicals, Inc.
Article
Purpose: To test the reproducibility of model-derived quantitative and semiquantitative pharmacokinetic parameters among various commercially available perfusion analysis solutions for dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging. Materials and methods: The study was institutional review board approved and HIPAA compliant, with waiver of informed consent granted. The study group consisted of 15 patients (mean age, 44 years; range, 28-60 years), with 15 consecutive 1.5-T DCE MR imaging studies performed between October 1, 2010, and December 27, 2010, prior to uterine fibroid embolization. Studies were conducted by using variable-flip-angle T1 mapping and four-dimensional, time-resolved MR angiography with interleaved stochastic trajectories. Images from all DCE MR imaging studies were postprocessed with four commercially available perfusion analysis solutions by using a Tofts and Kermode model paradigm. Five observers measured pharmacokinetic parameters (volume transfer constant [K(trans)], v(e) [extracellular extravascular volume fraction], k(ep)[K(trans)/v(e)], and initial area under the gadolinium curve [iAUGC]) three times for each imaging study with each perfusion analysis solution (between March 13, 2011, and September 8, 2011) by using two different region-of-interest methods, resulting in 1800 data points. Results: After normalization of data output, significant differences in mean values were found for the majority of perfusion analysis solution combinations. The within-subject coefficient of variation among perfusion analysis solutions was 48.3%-68.8% for K(trans), 37.2%-60.3% for k(ep), 27.7%-74.1% for v(e), and 25.1%-61.2% for iAUGC. The intraclass correlation coefficient revealed only poor to moderate consistency among pairwise perfusion analysis solution comparisons (K(trans), 0.33-0.65; k(ep), 0.02-0.81; v(e), -0.03 to 0.72; and iAUGC, 0.47-0.78). Conclusion: A considerable variability for DCE MR imaging pharmacokinetic parameters (K(trans), k(ep), v(e), iAUGC) was found among commercially available perfusion analysis solutions. Therefore, clinical comparability across perfusion analysis solutions is currently not warranted. Agreement on a postprocessing standard is paramount prior to establishing DCE MR imaging as a widely incorporated biomarker.