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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 quantication 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 signicantly lower in patients with node metastasis. e vphistogram parameters were signicantly
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 signicantly 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 signicance 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 dierence 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 eects on
the basis of tumor perfusion. Among them, the widely used
perfusion parameters extracted from the two-compartment
pharmacokinetic Tos 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 reux 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 aer neoadjuvant
chemoradiation therapy (CRT) in locally advanced rectal
cancer has been correlated with pathologically favorable
responses in previous studies [, ]. In addition, the initial
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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 signicant dier-
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
inuence the variable results of tumor perfusion analysis
using DCE-MRI such as intrinsic limits in a simplied
pharmacokinetic model, measurement error of arterial input
function, dierence among postprocessing soware, 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 aer 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 soware [].All included patients underwent
CRT within a month aer 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.
Diusion-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 dierent 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 soware (Olea Sphere;
Olea Medical Solutions, La Ciotat, France) with Tos 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 (mM⋅s). 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 classied
accordingtothefollowingvegrades: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 (dicult 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 soware 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
coecient (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 dierence between the groups, receiver operating char-
acteristics (ROC) curve analysis was performed to calculate
the sensitivity, specicity, and diagnostic accuracy.
BioMed Research International
T : Interobserver intraclass correlation coecient 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 %condence 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 coecients using the corresponding intraclass
correlation coecients.
Comparisons of DCE-MRI parameters of rectal cancer by
group, classied according to histologic results and molecular
biology, are summarized in Table .
In patients with lymph node metastasis, AUC skewness
was signicantly 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 .%, specicity .%) for
reader and . (% CI: .-.; sensitivity .%,
specicity .%) 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 signicant correlation with PNI. ROC curve analyses
revealed that mode vpshowed the best diagnostic perfor-
mance of PNI (𝐴zof mode vp.; % CI: .-; sensi-
tivity .%, specicity .% for reader ; 𝐴zof modevp.;
% CI: .-.; sensitivity .%, specicity .% for
reader ).
e Ktrans (mean, SD, th−th percentile, and mode)
and kep histogram values (mean, th−th percentile, and
kurtosis) were signicantly 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 .%,
specicity .%; 𝐴zof mode Ktrans ., % CI: .-
.; sensitivity %, specicity .% for reader ).
Other histologic (T stage, lymphatic invasion, and vascu-
lar invasion) and immunohistochemical (EGFR expression)
results were not associated with any dierence in DCE-MRI
parameters.
3.2. Correlation with Treatment Response aer 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 dierence was not
statistically signicant (. ±. 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 dierences were not statistically
signicant. No other DCE-MRI parameter histogram analy-
sis values were signicantly 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=) Pvalue∗Az†Yes(n=) No(n=) Pvalue∗Az†
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 Az∗Ye s ( n = ) N o ( n = ) Pvalue Az∗
PNI
vpmean . (.;.) . (.;.) 0.042 . . (.;.) . (.;.) 0.046 .
vpth percentile . (.;.) . (.;.) 0.011 . . (.;.) . (.;.) 0.013 .
vpth percentile . (.;.) . (.;.) 0.022 . . (.;.) . (.;.) 0.035 .
vpth percentile . (.;.) . (.;.) 0.024 . . (.;.) . (.;.) 0.039 .
vpth percentile . (.;.) . (.;.) 0.027 . . (.;.) . (.;.) 0.042 .
vpth percentile . (.;.) . (.;.) 0.029 . . (.;.) . (.;.) 0.035 .
vpth percentile . (.;.) . (.;.) 0.032 . . (.;.) . (.;.) 0.035 .
vpth percentile . (.;.) . (.;.) 0.035 . . (.;.) . (.;.) 0.039 .
vpth 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 Az∗Yes(n=) No(n=) Pvalue Az∗
KRAS mutation
Ktransmean . (.;.) . (.;.) 0.009 0.788 . (.;.) . (.;.) 0.010 0.784
KtransSD . (.;.) . (.;.) 0.020 . . (.;.) . (.;.) 0.035 .
Ktransth percentile . (.;.) . (.,.) 0.039 . . (.;.) . (.,.) 0.048 .
Ktransth percentile . (.;.) . (.;.) 0.035 . . (.;.) . (.;.) 0.048 .
Ktransth percentile . (.;.) . (.;.) 0.028 . . (.;.) . (.;.) 0.032 .
Ktransth percentile . (.;.) . (.;.) 0.014 . . (.;.) . (.;.) 0.023 .
Ktransh percentile . (.;.) . (.;.) 0.023 . . (.;.) . (.;.) 0.039 .
Ktransmode . (.;.) . (.;.) 0.007 0.793 . (.;.) . (.;.) 0.007 0.793
kep mean . (.; .) . (.;.) 0.018 . . (.; .) . (.;.) 0.044 .
kepth percentile . (.;.) . (.;.) 0.025 . . (.;.) . (.;.) 0.032 .
kepth percentile . (.;.) . (.;.) 0.028 . . (.;.) . (.;.) 0.028 .
kepth percentile . (.;.) . (.;.) 0.018 . . (.;.) . (.;.) 0.039 .
kepth percentile . (.;.) . (.;.) 0.020 . . (.;.) . (.;.) 0.035 .
kepth percentile . (.;.) . (.; .) 0.028 . . (.;.) . (.;.) 0.032 .
kepth percentile . (.;.) . (.;.) 0.016 . . (.;.) . (.;.) 0.032 .
kepth 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 signicant 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
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T : Correlation with treatment response of neoadjuvant chemoradiotherapy aer rectal cancer.
Treatment Response Parameter Reader Reader
TRG,,(n=) TRG , (n=) Pvalue∗TRG,,(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.
signicant correlation between PNI and vp.epresence
of PNI in rectal cancer is associated with a signicantly
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
dierence did not reach statistical signicance[]. However,
the present study showed statistical signicance 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 reecting 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 signicant 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
aer CRT have no additive value for response assessment
in the combination study of T-weighted MR volumetry,
diusion-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 insucient to suggest optimal threshold values of DCE-
MRI parameters for predicting prognosis. ird, we did
not analyze the MRI aer CRT and thus cannot assess the
changes in perfusion parameters aer CRT. However, in
a clinical setting, there is actually less interest in assess-
ing treatment response aer 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 dierence 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 soware used for image analysis is currently
commercially available.
Conflicts of Interest
e authors have no conicts of interest with regard to this
study.
Acknowledgments
is study was funded by Ministry of Education, Republic of
Korea (RDAB).
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