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AJVR • Vol 77 • No. 5 • May 2016 471
High-grade glial tumors are one of the most preva-
lent primary brain tumors in dogs for which the
prognosis is grave. In the United Kingdom, the inci-
dence of intracranial neoplasia in dogs is approxi-
mately 20 cases/10,000 dogs/y.1 Median survival times
for affected dogs range from 2 to 3 months, despite
the use of novel and aggressive treatments.2,3 In hu-
mans, a high correlation exists between the amount of
tumor that remains after resection and rate of tumor
recurrence, confirming that advanced imaging can be
a valuable prognostic tool.4 The incidence of primary
brain tumors is reportedly greater in dogs than in hu-
mans, so many subjects are potentially available for
preclinical and clinical trials to assess effects of novel
experimental treatments in dogs. Additionally, in terms
of disease-free interval, progression of neoplasms in
dogs occurs at a rate 4.6 times as fast as that in hu-
Comparison of visual metric and planimetry methods
for brain tumor measurement in dogs
Chris B. Thomson dvm
Kevin H. Haynes dvm
G. Elizabeth Pluhar dvm, phd
Received April 28, 2015.
Accepted July 17, 2015.
From the Department of Veterinary Clinical Sciences,
University of Minnesota, Saint Paul, MN 55108. Dr.
Thomson’s present address is School of Veterinary
Medicine, University of Wisconsin, Madison, WI 53706.
Dr. Haynes’ present address is VCA West Los Angeles
Animal Hospital, 1900 S Sepulveda Blvd, Los Angeles, CA
90025.
Address correspondence to Dr. Pluhar (pluha006@
umn.edu).
OBJECTIVE
To compare the orthogonal diameter (visual metric) method against a manual
perimeter tracing (planimetry) method to measure volume of brain tumors in
dogs by use of MRI scans.
SAMPLE
22 sets of MRI brain scans pertaining to 22 client-owned dogs with histologi-
cally confirmed glioma.
PROCEDURES
MRI scans were reviewed by 2 operators, and scans revealing tumors with
a degree of gadolinium enhancement that allowed discrimination between
tumor tissue and healthy parenchyma were used. Each operator calculated tu-
mor volume for each set of scans twice by use of visual metric and planimetry
methods. Inter- and intraoperator variability were assessed by calculation of
an agreement index (AI).
RESULTS
Mean ± SD intraoperator AIs were 0.79 ± 0.24 for the visual metric method
and 0.89 ± 0.17 for the planimetry method. Intraoperator variability for both
operators was significantly less when the planimetry method was used than
when the visual metric method was used. No significant differences were
identified in mean interoperator AI between visual metric (0.68 ± 0.28) and
planimetry (0.67 ± 0.31) methods.
CONCLUSIONS AND CLINICAL RELEVANCE
The lower intraoperator variability achieved with the planimetry versus visual
metric method should result in more precise volume assessments when the
same operator performs multiple volume measurements of brain tumors in
dogs. Equivocal results for interoperator variability may have been due to
method variance or inadequate preliminary training. Additional studies are
needed to evaluate the suitability of planimetry for assessing response to
treatment. (Am J Vet Res 2016;77:471–477)
mans, creating the potential to more rapidly advance
science.5
Antemortem quantification of lesion size via CT
or MRI is commonly performed to assess response
to treatment in experimental and clinical studies. Se-
rial gadolinium-enhanced MRI and clinical evaluation
are important methods used in human brain tumor
research to monitor therapeutic efficacy and progres-
sion-free survival times. Historically, the visual metric
method involving Macdonald criteria6 has been used,
by which the product of the 2 greatest orthogonal
diameters is calculated to estimate brain tumor size.
However, technological advances have resulted in
new software that allows more precise measurements.
Such advances include visual tracing of the tumor
perimeter (planimetry method)7,8; computer-assist-
ed, threshold-based methods8,9; and fully automated,
computer facilitated methods.10–12 Since the origi-
nal Macdonald criteria were developed, new criteria
have been proposed to assess therapeutic responses
in both human and veterinary neuro-oncology pa-
ABBREVIATIONS
AI Agreement index
WHO World Health Organization
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472 AJVR • Vol 77 • No. 5 • May 2016
tients. For brain tumor evaluation in human medicine,
these include updated Macdonald criteria,13 response
evaluation criteria in solid tumors,14 and response as-
sessment in neuro-oncology.15 Recently, criteria were
proposed for assessment of veterinary neuro-oncol-
ogy patients, referred to as response assessment in
veterinary neuro-oncology.16 Criteria for human and
veterinary assessment are largely based on diameter
measurements in 2 or 3 dimensions.
The original Macdonald criteria, developed in
1990, involved use of the largest cross-sectional diam-
eter in a single plane on an MRI scan, multiplied by the
perpendicular diameter in the same plane to estimate
the size of brain tumors. These criteria were devel-
oped to assess response to various treatments on the
basis of MRI findings and clinical signs to designate
complete or partial response, stable disease, or pro-
gression, but they also provided a foundation for tu-
mor volume assessment. The Macdonald criteria were
largely developed in response to the WHO initiative
to standardize the reporting of patient responses to
various cancer treatments. Although tumor volumes
in dogs may be estimated from MRI scans through sim-
ilar methods, the authors are unaware of any studies
conducted to compare and validate different volumet-
ric quantification techniques for glioma in dogs. Veteri-
nary researchers and clinicians are now treating dogs
with glioma by surgical resection and other therapeu-
tic modalities and need an accurate and reproducible
method to measure response to treatment.
The purpose of the retrospective study reported
here was to assess accuracy and reproducibility of 2
protocols to measure tumor volume in dogs by use
of MRI data. The optimal method would involve com-
puter software that was user-friendly, widely available
to veterinarians, and relatively cost effective. It would
also allow an individual with the skill and knowledge
of a technologist to calculate tumor volume in a re-
peatable manner that was operator independent. Our
null hypothesis was that there would be no significant
differences in interoperator and intraoperator reliabil-
ity between the planimetry method and visual metric
method for estimation of brain tumor volume in dogs.
Materials and Methods
Sample
A total of 66 image series from 22 sets of brain
MRI scans were included in the study. Scans pertained
to 22 client-owned dogs with histologically confirmed
high-grade glioma. Images were acquired by several re-
ferral institutions that had MRI units of various magnet
strengths, ranging between 1.0 and 3.0 T. All dogs had
brain tumors that yielded some degree of contrast en-
hancement after gadolinium administration at a dose
of 1 mL/4.45 kg. All measurements were performed
on T1-weighted postgadolinium images in 3 anatomic
planes (axial, sagittal, and coronal). All dogs had un-
dergone surgical debulking of solitary primary brain
tumors, and tissue specimens from those tumors were
histologically evaluated at the University of Minneso-
ta Masonic Cancer Center by pathologists who were
board-certified by the American College of Veterinary
Pathology.
Volumetric measurements
Two operators, one experienced in reading
MRI scans (KHH) and another with no previous ex-
perience reading MRI scans (CBT), independently
evaluated each MRI scan 4 times, performing 2 mea-
surements for each of the 2 methods (Figure 1). To
decrease familiarity or recognition of specific tumors,
MRI studies in the different planes were randomized
between successive measurement events. The 2 opera-
tors were selected on the basis of their availability for
the time commitment involved in these evaluations
and their general lack of relevant diagnostic expertise,
which could be expected in intended future users. Pri-
or to performing volumetric calculations for the study,
operators participated in a brief instructional session
about the software used to perform the 2 volumetric
methods and practice sessions involving each method
and sample MRI scans. In addition, a veterinary sur-
geon that had 10 years of experience specializing in
the treatment of dogs with brain tumors made mea-
surements with both methods to serve as a reference
standard for comparisons. The imaging softwarea used
to obtain measurements was chosen on the basis of
affordability, technical simplicity, and availability.
The visual metric method consisted of identifying
and marking the longest diameter of the gadolinium-
enhanced portion of the tumor mass by use of digital
calipers within the designated software on each im-
age slice and subsequently identifying and marking
the orthogonal diameter of the mass (Figure 2).3 The
2 diameters were used to calculate an area for every
slice in which tumor could be identified by use of the
following formula:
Ellipsoid area = 0.5d1 X 0.5d2 X π
where d1 and d2 are orthogonal diameters. The sum of
the area measurements, multiplied by slice thickness
and intersection gap, was used to determine the vol-
ume of each tumor in each of the 3 anatomic planes.
The planimetry method consisted of manually
tracing and segmenting the gadolinium-enhanced
portion of the tumor mass on each individual slice by
use of the designated software. The software automati-
cally calculated the area from the traced perimeter.
Consistent with the visual metric method, area mea-
surements were summed and multiplied by the slice
thickness and intersection gap and the volume of
each tumor was determined in each of the 3 anatomic
planes. Volumes calculated on the basis of each meth-
od were used for statistical analysis.
Statistical analysis
Agreement indices were calculated to assess reli-
ability of each method within and between operators
by use of the following equation8:
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AJVR • Vol 77 • No. 5 • May 2016 473
AI = 1 – |xa – xb|/([ xa + xb]/2)
where xa and xb represent different sets of
measurements.
As the AI approaches 1, the 2 compared vol-
ume measurements are more similar and therefore
more reliable. Intraoperator reliability was defined
as degree of agreement, or similarity, between 2
volume calculations made by the same operator for
the same tumor in an individual plane. For calcula-
tion of intraoperator AI, xa was the first calculated
volume and xb was the second calculated volume
by the same operator for the same method for each
tumor in 1 plane. Interoperator reliability was de-
fined as the agreement between operators in their
volume calculation for the same tumor in an individ-
ual plane. Because each operator calculated 2 vol-
umes for each and every tumor with both methods
in each plane, the mean of the 2 volumes was used
for interoperator comparisons. For the interopera-
tor AI, xa was the mean of the 2 calculated volumes
for the first operator and xb was the mean of the 2
calculated volumes for the second operator by use
of the same method for the same tumor.
Because of the wide range in tumor sizes, raw
data from the volume calculations were logarithmi-
cally (base 10) transformed in an attempt to normalize
the values. For intraoperator AI comparisons, a mixed
ANOVA model was fit by use of statistical software,b
with dog included as a block effect. For interoperator
AI, a similar mixed ANOVA model was fit but without
operator as a variable because the response was not
unique to each operator but to the AI between the
two.
Figure 1—Representative T1-weighted axial MRI scans ob-
tained after gadolinium administration to a dog with glio-
blastoma multiforme showing contrast enhancement of the
tumor (A), tracing of the same scan by use of imaging soft-
ware for area calculation via the planimetry method (B), and
diameter markings made on the same scan via the visual
metric method (C). Tumor area calculated by means of the
visual metric method was 1.52 cm2, and that calculated by
means of the planimetry method was 1.15 cm2.
Figure 2—Axial MRI scan of a dog with a glioblastoma multi-
forme showing the longest orthogonal diameters (thick white
lines) and planimetric tracing (green line) of the tumor and
graphic representation of the inherent overestimation of tumor
volume associated with the diameter method (thin white lines).
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474 AJVR • Vol 77 • No. 5 • May 2016
For each model, terms were tested for significance
with type II tests; additionally, least squares means
were computed to estimate the mean value for each
level and compared via pairwise comparisons. The
Tukey honest significant difference test was used for
variables with > 2 categories, such as view and meth-
od. Values of P < 0.05 were considered significant.
Results
Tumors
All but 1 tumor was classified as grade III or IV
on the basis of WHO criteria and consisted of glioblas-
toma multiforme (n = 11), anaplastic astrocytoma (7),
anaplastic oligodendroglioma (3), and primitive neu-
roectodermal tumor (1). One tumor was classified as
a ganglioglioma, which in humans would be classified
as a WHO grade I tumor but in dogs has unknown bio-
logical behavior.
A total of 528 volume measurements were made
from the MRI scans (Figure 3). Tumor volumes ranged
from 6.61 to 252.21 mm3, with mean and median vol-
umes of 62.80 mm3 and 42.71 mm3, respectively. Esti-
mated least squares mean (SE) logarithmic values for tu-
mor volume were 1.61 (0.07) and 1.69 (0.07) for each
of the 2 inexperienced operators; 1.68 (0.07) and 1.61
(0.07) for the visual metric and planimetry methods, re-
spectively; and 1.66 (0.07), 1.63 (0.07), and 1.66 (0.07)
for the axial, coronal, and sagittal planes, respectively.
Mean difference in logarithmic volumes between the
2 operators was 0.08, representing a mean difference
in tumor size of 18% (P < 0.001). Volumes measured
by 1 inexperienced operator were on average smaller,
regardless of method or plane used, than those made
by the other inexperienced operator. Mean difference
in volumes between the 2 methods was 0.07, repre-
senting a mean difference in size of 16% (P < 0.001).
Volume calculations made with the planimetry method
were smaller than those made with the visual metric
method, regardless of operator or plane. No significant
differences were identified in volume measurements
among imaging planes.
Agreement indices used in assessments of intra-
operator agreement were graphically plotted (Figure
4). In these assessments, one inexperi-
enced operator had a median AI of 0.94,
whereas the other had a median AI of
0.89, indicating that the first operator’s
measurements were more precise (P <
0.001). The visual metric method had a
median AI of 0.89 (mean ± SD, 0.79 ±
0.24), and the planimetry method had a
median AI of 0.94 (0.89 ± 0.17), indicat-
ing significant (P < 0.001) differences
between the 2 methods, with the pla-
nimetry method having less variability.
There were no significant differences
among planes.
Agreement indices used in assess-
ments of interoperator agreement
were graphically plotted (Figure 5).
In these assessments, the visual metric
method had a median AI of 0.77 (mean
± SD, 0.68 ± 0.28) and the planimetry
method had a median AI of 0.74 (0.67 ±
0.31). These median values did not dif-
fer significantly. Mean and median inter-
operator AIs were lower than mean and
median intraoperator AIs. Median AIs
for each plane were 0.63 for axial, 0.73
for coronal, and 0.66 for sagittal. These
values differed significantly (P = 0.01)
in that greater variability was identified
between measurements made by use of
MRI scans obtained in the coronal and
axial planes.
Discussion
Results of the present study indi-
cated that the planimetry method of
measuring brain tumor volume in dogs
by use of MRI scans was more reliable
Figure 3—Logarithmically (base 10) transformed volumetric data for gliomas in 22
dogs (patients A through V) as measured in axial (A and B), coronal (C and D), and
sagittal (E and F) planes by 2 inexperienced operators (squares and triangles) and
1 highly trained operator (circles) by use of the visual metric method (A, C, and E)
and planimetry method (B, D, and F). Clustering of data points represents similarity
among volume calculations; tighter clusters represent data with less variability. For
example, the data set for the visual metric method within the sagittal plane (panel
E) appears to have greater variability than that attained with the planimetry method.
On the other hand, minimal variability is evident among the data points for the pla-
nimetry method in the coronal plane (panel D). ID = Identification.
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AJVR • Vol 77 • No. 5 • May 2016 475
than the visual metric method for repeated measure-
ments, particularly when made by the same individ-
ual. Comparison of measurements between 2 fairly
inexperienced operators revealed that the methods
were equivalent in terms of reliability. Mean AIs were
consistent with calculations of operator variability in
another study.7 Differences in tumor volume calcula-
tion between operators that led to greater variability
were likely attributable to the inability
to precisely define tumor borders.
Both operators in the present study
reported that measurements were fast-
er and easier to perform when the pla-
nimetry method was used. When the
visual metric method was used, it took
more time to successfully find the lon-
gest diameter, the longest orthogonal
diameter, and the angle measurements
to ensure the 2 diameters were perpen-
dicular than it did to trace the gadolin-
ium-enhanced perimeter with the pla-
nimetry method. Theoretically, tracing
the tumor would provide a more reli-
able estimation of absolute tumor vol-
ume because delineating the orthogo-
nal diameter resulted in ellipse-shaped
area in each segment, whereas tracing
allowed more flexibility in perimeter
measurement (Figure 2). This inher-
ent limitation of the original method
described by Macdonald to accurately
measure irregularly shaped tumors has
been described in human medical lit-
erature.14 The planimetry method was
designed to mitigate this shortcoming
through a more adaptable determinant
of tumor area per slice. Data reported
here for dogs suggested that use of the
visual metric method resulted in a con-
siderable overestimation of tumor vol-
ume because of failure to account for
perimeter voids that did not occur with
the planimetry method.
The present study revealed no sig-
nificant differences in the volumetric
data acquired from 3 MRI planes by
the same operator. However, significant
differences were identified between
operators. Volume calculations made
from the images in the coronal plane
had the lowest variability. The mag-
nitude of variability was significantly
different between volumes measured
in the coronal versus axial planes. This
information would be beneficial when
a single plane is used for volume cal-
culations. The original Macdonald cri-
teria involved estimation of tumor size
by measuring area from the single MRI
slice with the greatest tumor diameter.
A study17 involving humans with malignant glioma re-
vealed that calculation of tumor volume rather than
area from a single image is more sensitive and specific
to changes in tumor size over time.
In the study reported here, all measurements
were made on T1-weighted, gadolinium-enhanced
images that revealed certain characteristics of high-
grade glioma, including a profound mass effect, peritu-
Figure 4—Intraoperator AIs for repeated (twice) calculations of glioma volume in
each of 22 dogs made by one operator (A and B) and another operator (C and D)
by means of the visual metric method (A and C) and the planimetry method (B and
D). Dashed line represents median AI, and solid line represents mean AI.
Figure 5—Interoperator AIs for single calculations of glioma volume in each of
22 dogs by 2 operators by means of the visual metric method (A) and planimetry
method (B). See Figure 4 for remainder of key.
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476 AJVR • Vol 77 • No. 5 • May 2016
moral edema, ring enhancement, and central necrotic
area.3,18,19 Although the visibility of many high-grade
gliomas is enhanced with contrast techniques, which
makes it easier to estimate tumor borders, this charac-
teristic is not universal.3 T2-weighted images may be
useful for measuring volume of tumors that lack en-
hancement when fluid-attenuated inversion recovery
sequence images are used to differentiate peritumoral
edema from tumor during segmentation. This is also
important because contrast enhancement can be af-
fected by treatment with antiangiogenic or corticoste-
roid drugs and with certain radiographic techniques
without true changes to the underlying tumor size.15
We used pretreatment MRI scans in the present study
to minimize the effects associated with tumor pseu-
doprogression or hyper- or hypoenhancement associ-
ated with other treatments or procedures.
Many factors need to be taken into account when
evaluating treatment efficacy in patients with brain
tumors. Neurocognitive function, resolution of clini-
cal signs, quality of life, and progression-free survival
time are variables currently used to assess response
to treatment. New methods need to be developed to
provide a more objective assessment than is currently
possible. In the present study, only pretreatment tu-
mor volumes were measured; however, serial evalua-
tions are needed to assess patient response to treat-
ment, tumor volume after resection, and progressive
changes in tumor volume. The Macdonald criteria and
new criteria to assess response to novel treatments
by veterinary neuro-oncology patients over time have
not been evaluated, and research is warranted into the
applicability of these criteria to gliomas in dogs.
A limitation of the present study was that, except
for the highly trained operator whose results were
used as the reference standard, only 2 inexperienced
operators calculated the tumor volumes. Use of more
operators with varied experience would minimize
study error attributable to interindividual variance.
However, use of a homogenous, highly-trained group
of operators does not necessarily lead to a decrease
in interoperator variance.20 This supposition was sup-
ported by the scatter plots in the present study in
which values calculated by the 2 inexperienced op-
erators were plotted along with those calculated by
the highly trained operator (Figure 3). More training
and practice trials may allow each operator to more
precisely define or recognize diseased regions on MRI
scans. We found that the lowest variation among vol-
ume calculations was achieved when the same indi-
vidual made the measurements, which is an important
factor to consider when assessing serially acquired
MRI scans.
Inherent limitations of visual metric and planim-
etry methods include the use of stacked slices for
volume determination. Volume calculated by sum-
mation of areas measured from stacked slices may
vary from the absolute tumor volume. Each slice as-
sumes a slab thickness that may not be consistent
throughout the given slice. Additionally, potential
exists for over- and underestimation of volume, de-
pending on the extent of inclusion or exclusion of
the end cap volumes.4 In the present study, images
had been acquired at several institutions with MRI
units of differing strength, ranging from 1.0 to 3.0 T.
This variability may have affected image evaluation
but in the authors’ opinion, appropriately reflected
how the technique would be used in clinical prac-
tice or research.
Accurate assessment of treatment response is im-
portant to tailor treatment to individual patients, facili-
tate communications about tumor size, perform clini-
cal trials, and allow reliable comparisons of treatment
results. The planimetry method used in the present
study offers an alternative method to the previously
described standard for measuring tumor volume. Con-
tinual assessment of and improvement in the reliabil-
ity of MRI measurements will allow this modality to be
used as end point verification for research and will aid
in prognosis determination and treatment planning
for dogs with brain tumors.
Acknowledgments
Supported in part by the American Humane Association.
The authors thank Dr. Aaron Rendahl for performing the sta-
tistical analyses.
Footnotes
a. OsiriX DICOM software, version 5.6, Pixmeo Sarl, Geneva,
Switzerland.
b. R Core Team (2013), R: a language and environment for statisti-
cal computing, Foundation for Statistical Computing, Vienna,
Austria.
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