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Semi-automated primary tumor volume measurements by dynamic contrast-enhanced
MRI in patients with head and neck cancer
Wouter L. Lodder, MD,1* Kenneth G. A. Gilhuijs, PhD,2 Charlotte A. H. Lange, MD,3 Frank A. Pameijer, MD, PhD,4 Alfons J. M. Balm, MD, PhD,1,5
Michiel W. M. van den Brekel, MD, PhD1,5
Department of Head and Neck Oncology and Surgery, The Netherlands Cancer Institute—Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The
Netherlands, 2Department of Radiology, Image Sciences Institute, University Medical Centre Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands, 3Department of
Radiology, The Netherlands Cancer Institute—Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands, 4Department of Radiology,
University Medical Centre Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands, 5Department of Otorhinolaryngology, Academic Medical Center, University of
Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
Accepted 25 January 2012
Published online 19 April 2012 in Wiley Online Library ( DOI 10.1002/hed.22988
ABSTRACT: Background. Tumor volume is a significant prognostic
factor in the treatment of malignant head and neck tumors.
Unfortunately, it is not routinely measured because of the workload
Methods. Twenty-one patients, between 2009 and 2010, were studied.
Dynamic contrast-enhanced MRI (DCE-MRI) at 3.0T was performed. A
workstation previously developed for semi-automated segmentation of
breast cancers on DCE-MRI was used to segment the head and neck
cancers. The Pearson correlation analysis was used to assess the
agreement between volumetric measurements and the manually derived
gross tumor volume (GTV).
Tumor volume is a significant prognostic factor in the
treatment of malignant head and neck tumors.1–8 Several
studies have confirmed the prognostic value of CT-determined tumor volume for outcome after definitive radiation therapy in head and neck cancer, including tonsillar,3
hypopharyngeal,4 supraglottic,5 and glottic6 cancer. Van
den Broek et al1 found that in patients with advanced,
unresectable head and neck cancer, treated with targeted
chemoradiation, primary tumor volume is the most important independent prognostic factor. The Union Internationale Contre le Cancer classification (TNM) represents the
validated standard tool to describe tumor extent and
includes prognostic information on the probability of disease control.9 Unfortunately, tumor volume has not been
incorporated in the TNM staging system. The main reason
for this is probably that tumor volume is more difficult to
assess than the maximal diameter of a tumor. Gross tumor
volume (GTV) might be a more relevant factor than a
2-dimensional descriptive TNM classification or stage
*Corresponding author: W. L. Lodder, Department of Head and Neck Oncology
and Surgery, Netherlands Cancer Institute – Antoni van Leeuwenhoek Hospital,
Amsterdam, The Netherlands. E-mail: [email protected]
Contract grant sponsor: This research was produced without financial support.
Results. In 90.5% of the patients (19 of 21) correlation could be made
between DCE-MRI and the manually derived GTV. The Pearson
correlation coefficient between the automatically derived tumor volume
at DCE-MRI and the manually derived GTVs was R2 ¼ 0.95 (p < .001).
Conclusion. Semi-automated tumor volumes on DCE-MRI were
representative of those derived from the manually derived GTV (R2 ¼ 0.95;
C 2012 Wiley Periodicals, Inc. Head Neck 35: 521–526, 2013
p < .001). V
KEY WORDS: dynamic contrast-enhanced MRI, head and neck cancer,
tumor volume, semi-automated measurement, volume measurement
grouping as prognostic factor for local control in head
and neck cancer.10 GTV represented the most important
prognostic indicator in head and neck squamous cell carcinoma treated with intensity modulated radiation therapy
and is recommended to be considered for therapeutic
decisions and estimation of outcome.10 Unfortunately, it
is not routinely measured because of the workload
Assessment of semi-automatic volume measurements of
the primary tumor has been performed for prostate cancer
and breast cancer11,12 using dynamic contrast-enhanced
MRI (DCE-MRI). Alderliesten et al13 showed that semiautomatic volumetric measurement of tumor extent accurately correlates with histopathology (R2 ¼ 0.84). In
2004, Shah et al14 reviewed the use of DCE-MRI in head
and neck cancer. They concluded that DCE-MRI had
great promise in evaluating carcinoma of head and neck,
monitoring the treatment, and differentiating tumorinvolved lymph nodes from non–tumor-involved nodes.
Recently, Hadjiski et al15 investigated the feasibility of
computerized segmentation of lesions on head and neck
CT scans and evaluated its potential for estimating
changes in tumor volume in response to treatment of
head and neck cancers. To our knowledge, no previous
study was conducted to evaluate the use of DCE-MRI for
semi-automated tumor volume measurements in patients
with head and neck cancer.
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TABLE 1. Patient and tumor characteristics.
Ethical considerations
Patient characteristics
Institutional approval for the study was received. As
patient anonymity was preserved, patient consent was not
required for the retrospective review of records and
images. DCE-MRIs were acquired using standard MRI
sequences used for imaging of head and neck tumors.
However, typically, the imaging is performed at a fixed
moment after contrast injection. In this method, imaging
is performed dynamically directly after contrast injection
throughout the normal fixed scanning moment. Neither
the scanning time nor the contrast injection dose is different using the dynamic approach compared to the standard
Primary site
Oral cavity
Pathologic T classification
Pathologic N classification
Manually derived volume
Automatically derived volume
Patient data
Twenty-four consecutive patients with histologically
proven head and neck squamous cell carcinoma, treated
between September 2009 and March 2010, were included
in this study.
A total of 21 patients were used for subsequent analyses. Three cases were excluded due to inappropriate fieldof-view. For the evaluation of the semi-automated volume
measurements, the tumor had to be in the area of interest
(differences in volume in these cases are not caused by
the failure of the segmentation technique).
Clinical characteristics including sex, primary site,
pathological T classification, and pathological N classification of the 21 patients are summarized in Table 1. The
tumors were located in the oral cavity (57%; n ¼ 12) and
oropharynx (43%; n ¼ 9). One patient had a recurrent
carcinoma and 20 patients had a primary tumor.
Imaging protocol
All patients underwent a preoperative MRI. DCE-MRI
examinations were performed at 3.0 T (Philips Achieva,
release 3.2.1, Philips Medical Systems, Best, The Netherlands) using a dedicated 16-channel SENSE neurovascular coil. Fast field echo acquisitions were used. One series
was acquired before the injection of a gadolinium-based
contrast agent (intravenous injection of 15 cc, Prohance;
Bracco-Byk Gulden, Konstanz, Germany) and 79 series
after injection. The series were acquired at intervals of
0.95 seconds. The following series (produced during the
same MRI procedure) were available to the operator performing manual tumor delineations: STIR TSE COR, TR
(repetition time), IR (inversion time), TE (echo time)
3880/180/20 ms, ETL: 12, FOV 300/228/40 mm, matrix:
320/320, 2 nex, slice thickness 4 mm; STIR TSE TRA,
TR/IR/TE 4,228/180/20, ETL: 12, FOV: 180/200/80 mm,
matrix 300/312, 2 nex, SW 3.5 mm, T1 TSE TRA, TR/
TE: 780/10, ETL: 5, FOV 180/180/80, matrix 384/384, 2
nex, slice thickness: 3.5 mm; T1 3D Thrive, TR/TE: 5/
2,22, ETL:90, TA: 10, FOV 230/272/220, matrix 288/
288, 2 nex, slice thickness: 0.8 mm.
T1 TSE COR (postcontrast): TR/TE: 812/10, ETL: 6,
FOV: 180/150/96 mm, matrix: 320/320, 3 nex, slice
thickness 3.5 mm.
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No. of patients
0.50–42.8 (mean, 10.7) cm3
0.65–42.3 (mean, 12.8) cm3
Measuring tumor extent
MRI tumor delineation. To serve as reference, the primary
tumor volume was manually delineated on the MRIs on
STIR TSE axial series. For correlation, standard axial T1weighted and contrast-enhanced T1 3D THRIVE images
were also available to select the region of interest designated as tumor. In prior studies, showing a prognostic
significance of tumor volume, all measurements are
performed on pretreatment MRI and CT images and
therefore these volumes are considered to be the ‘‘gold
standard.’’ In this study, tumor volume was measured by
multiplying the total area of the outlined tumor on every
slice with the slice thickness. One observer (F.P.), an
experienced head and neck radiologist (>15 years), performed all measurements. Images were seen in random
order. The observer was blinded for the results of the
semi-automatically derived tumor volumes.
Technique for semi-automated derived
gross tumor volume
A previously developed viewing station that permits simultaneous viewing of 2 linked series in 3 orthogonal
directions was used.16 The system visualizes all acquired
series (precontrast and 79 series postcontrast) as well as
subtractions of all series. The viewing station was also
used for semi-automated segmentation of head and neck
tumors at DCE-MRI. The required user interaction
involves indicating 1 single point in or near the location
of the center of the tumor in the MRIs. Subsequently, the
tumor is segmented in 3D resulting in a volumetric measurement of extent. After segmentation, visual inspection
of the result was performed to determine whether the segmentation correctly encompassed the enhancement in the
tumor. After approval of the segmentation result, the volume calculated from the segmented tumor volume was
FIGURE 1. Illustration of a semi-automatic segmentation of a T3 base of tongue carcinoma in a 45-year-old patient. The top row shows the uptake
images (subtraction of postcontrast after 45 seconds and precontrast) in which the user identifies the location of the tumor. By selecting a point in
1 view (cross hairs), the other views are updated to maintain correspondence in 3 directions. The bottom row shows the result of the segmentation
for visual inspection in the corresponding planes.
Semi-automated segmentation of dynamic
contrast-enhanced MRI
The method used for semi-automated segmentation of
head and neck tumors in MRIs was adopted from previous work.13 In short, after manual selection of a seed
point in or near the center of the tumor, the variance of
the image intensities over time is computed at each voxel
location, thus yielding a processed image in which the
contrast of the tumor is increased relative to the background. Then, an ellipsoidal region of interest is automatically constructed around it, and volume growing is
applied within the region of interest using a voxel valuebased stop criterion that is automatically derived from the
enhanced tumor volume (Figure 1).
Linear regression analysis was used to assess the precision of volumetric tumor extent with respect to the extent
obtained by manual delineation. Pearson correlation was
used to test for significant associations.
Dynamic contrast-enhanced measured tumor volume
For the total group of 21 patients, the mean volume of
the primary tumor was 12.78 6 12.23 cm (median volume, 7.79 cm; range, 0.65–42.3).
Pearson correlation
In 90.5% of the patients (19 of 21), correlation could
be made between the semi-automatically derived volume
and the manually derived GTV (Figure 2). In 2 cases,
no reasonable visible agreement could be achieved
(Figure 3). The first case was a patient with a recurrent
carcinoma of the base of tongue. Clinically, a large necrotic tumor mass, with diameter of 4.5 cm, was seen.
The second case was a T4N2c tongue carcinoma. Clinically, a large ulcerative tumor, with a diameter of 5 cm,
was seen. Nineteen cases were therefore used for analysis,
resulting in a significant correlation coefficient (R2 ¼
0.95; p < .001; Figure 4).
Synopsis of new findings
In 3 excluded cases, the primary tumor was not totally
covered within the craniocaudal field of view in the
dynamic series. Therefore, semi-automated tumor volume
measurement was not possible.
This study shows that semi-automated assessment of tumor volume using DCE-MRI agrees well (R2 ¼ 0.95)
with manually derived GTV. To the best of our knowledge, no prior studies have reported on the feasibility of
semi-automated tumor volume measurements by DCEMRI in patients with head and neck cancer. Primary
tumor volume has been proven to be an important prognostic indicator for a variety of head and neck squamous
cell carcinomas. Unfortunately, in daily practice, tumor
Manually derived gross tumor volume
For the total group of 21 patients, the mean manually
derived GTV of the primary tumor was 10.7 6 11.22 cm
(median volume, 6.59 cm; range, 0.50–42.79).
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FIGURE 2. Example of semi-automatic segmentation of primary tumor volume on dynamic contrast-enhanced MRI (DCE-MRI) with good visual
agreement. (A) Axial postcontrast MR image at the level of the base of the tongue (same patients as in Figure 1). The tumor (T) involves the right
tongue base with extension to the right lateral pharyngeal wall. In addition, there is extension over the midline to the left side. The uninvolved free
edge of the epiglottis is slightly deviated to the left (long arrow). Short arrows: enhancement of normal lymphoid tissue at the left tongue base.
(B) Auto-segmentation after placement of seed point. Note the delineation program automatically includes tumor enhancement, but excludes
physiologic enhancing lymphoid tissue at the left tongue base. (C) Manual tumor delineation.
volume measurements are performed infrequently probably due to the extra workload involved.
Comparisons with other studies
In a 2004 literature overview, van den Broek et al1
showed that the reported mean primary tumor volumes
vary within the different T classifications. For a certain
primary tumor volume, local control rates vary between
different tumor locations. Only a few authors report on
primary tumor volume.1–8 These studies showed that volume is an independent predictor for local and regional
control. DCE-MRI allows the study of microcirculation
of tumors and normal tissues. Enhancement of a tissue
depends on a number of factors, including vascularity,
capillary permeability, renal clearance and volume, and
composition of the extracellular fluid.14 Based upon the
differences in enhancement of body tissues, semi-automated measurements can be performed. At present, DCEMRI is frequently applied in diagnostic MRI protocols
among others in the head and neck. Fischbein et al17
showed the different values for time-to-peak, peak
enhancement, maximum slope, and washout slope for different tissues (nontumor node, sternocleoid mastoid muscle, tumor node, and submandibular gland).
In 2007, Alderliesten et al13 studied the correlation
between DCE-MRI measurements and histopathology in
43 patients with breast cancer. With R2 ¼ 0.84, they concluded that semi-automatic volumetric measurement of
breast cancer accurately describes tumor extent. In this
current study, we found a correlation of R2 ¼ 0.95. A
possible explanation for this difference in Pearson correlation could be the difference in the gold standard used.
A lower correlation coefficient between radiological and
histopathological volume can be anticipated due to tissue
under sampling and other technical limitations at histopathology. In 2010, Hadjiski et al15 described the feasibility
of computerized segmentation of lesions on head and
neck CT scans and evaluated its potential for objective
tumor volume estimation in response to treatment. The
computer-estimated change in tumor volume and percentage change in tumor volume between the pretreatment
and posttreatment scans achieved a high correlation
(intraclass correlation coefficient ¼ 0.98 and 0.98, respectively) with the estimates from manual segmentation in
13 primary tumors. These values approach our current
study results. However, we think that MRI with its superior soft tissue contrast may be more suitable for semiautomated volume measurements.
Study limitations
Some study limitations should be discussed. Only 1
observer delineated the primary tumor. Knegjens et al2
studied interobserver variability in 50 cases. The mean
difference in delineated primary tumor volume was 7.6%
(range, 0.15% to 20.9%). This difference was constant
across tumor volume. The Pearson correlation coefficient
FIGURE 3. Example of semi-automatic segmentation of primary tumor volume on dynamic contrast-enhanced MRI (DCE-MRI) with bad visual
agreement. (A) Axial contrast-enhanced MR image of a T3 tongue carcinoma (T). (B) Result of automatic segmentation after placement of the seed
point. (C) Manual tumor delineation.
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FIGURE 4. Automated versus manual tumor volume measurements.
Agreement between automatically and manually derived tumor
volumes from dynamic contrast-enhanced MRI (DCE-MRI; R2 ¼
was 0.99, which implies good agreement between the
observers. In 1997, Rasch et al18 showed that MRIderived GTVs have less interobserver variation than
CT-derived GTV. Furthermore, all studies showing the
prognostic significance of tumor volume used delineated
volumes determined on radiology. Therefore, we believe
that MRI delineation by 1 observer may yield representative results in the current study. Also, 1 observer performed the placement of the seed point. In theory, the location of the seed point should not influence the segmented
volume by much, because the technique automatically
shifts the seed point to the approximate center of mass of
enhancement before volume growing. However, we cannot
exclude possible bias. Therefore, in future research the
interobserver and intraobserver variation should be studied.
A total of 24 patients were selected for semi-automated
measurement of the primary tumor volume. In total, in 19
patients, the primary tumor volume could be assessed and
compared with the manually derived GTV. In 3 cases, the
primary tumor volume was not totally covered within the
craniocaudal field. This was caused by an improper selection of the field of interest by the MRI physician. In 2
additional patients, no visual agreement was reached
between DCE-MRI and tumor delineation on MRI. Several
reasons exist for visual disagreement. First, the segmentation method was optimized for breast cancer. In future
work, the system will be optimized to head and neck cancer. Another explanation is heterogeneity in contrast
uptake in the tumor (eg, due to necrosis). Another limitation of our study is that most of the included patients had a
primary untreated head and neck cancer; in our consecutive series only 1 patient had recurrent disease. Radiotherapy or carotid artery embolization may change the contrast
enhancement of tissues, thus influencing the performance
of the semi-automated segmentation.
Clinical applicability of the study
Currently, standard methods for tumor volume assessment
(estimation of tumor volume based on the cubic relation
with largest tumor diameters [volume ¼ a b c)19,20
or the ellipsoidal relation (volume ¼ 1/6 p a b c)21,22 as well as manual delineation of volume on
CT or MRI, are time consuming and have limited reproducibility. The mean time required to perform a volume
measurement using the summation-of-areas technique is
approximately 10 minutes for experienced radiologists,
which is impractical. Using the reported approach, it may
be possible to assess tumor volume on a routine basis
within 2 minutes. The only required user interaction
involves indicating 1 single point in or near the center of
the tumor on the MRI. Also, semi-automated segmentation may be useful for radiation therapy planning and
treatment-response measurements. In the future, the
reported method may allow for improved international
standardization of tumor volume measurements in
patients with head and neck cancer. In cases in which
DCE-MRI was not possible due to previous irradiation/
treatment, contraindications for intravenous contrast, or in
cases with no reasonable visible agreement, tumor delineation remains the gold standard.
Semi-automated measurements of tumor volume on
DCE-MRI were representative of those derived from
manual delineation (R2 ¼ 0.95; p < .001) in a group of
19 patients with head and neck cancer. When these results
are validated in a larger prospective study, they could be
implemented in routine practice.
We thank Dr. S. Muller and A. Paape for their assistance
with the dynamic MRI protocol and we thank Josien de
Bois for her help with the measurement of tumor
1. van den Broek GB, Rasch CR, Pameijer FA, et al. Pretreatment probability
model for predicting outcome after intraarterial chemoradiation for
advanced head and neck carcinoma. Cancer 2004;101:1809–1817.
2. Knegjens JL, Hauptmann M, Pameijer FA, et al. Tumor volume as prognostic factor in chemoradiation for advanced head and neck cancer. Head
Neck 2011;33:375–382.
3. Hermans R, Op de beeck K, Van den Bogaert W, et al. The relation of CTdetermined tumor parameters and local and regional outcome of tonsillar
cancer after definitive radiation treatment. Int J Radiat Oncol Biol Phys
4. Pameijer FA, Mancuso AA, Mendenhall WM, et al. Evaluation of pretreatment computed tomography as a predictor of local control in T1/T2 pyriform sinus carcinoma treated with definitive radiotherapy. Head Neck
5. Hermans R, Van den Bogaert W, Rijnders A, Baert AL. Value of computed tomography as outcome predictor of supraglottic squamous cell carcinoma treated by definitive radiation therapy. Int J Radiat Oncol Biol
Phys 1999;44:755–765.
6. Pameijer FA, Mancuso AA, Mendenhall WM, Parsons JT, Kubilis PS. Can
pretreatment computed tomography predict local control in T3 squamous
cell carcinoma of the glottic larynx treated with definitive radiotherapy?
Int J Radiat Oncol Biol Phys 1997;37:1011–1021.
7. Doweck I, Denys D, Robbins KT. Tumor volume predicts outcome for
advanced head and neck cancer treated with targeted chemoradiotherapy.
Laryngoscope 2002;112:1742–1749.
8. Mendenhall WM, Parsons JT, Mancuso AA, Pameijer FJ, Stringer SP,
Cassisi NJ. Definitive radiotherapy for T3 squamous cell carcinoma of the
glottic larynx. J Clin Oncol 1997;15:2394–2402.
9. Edge SB, Byrd DR, Compton CC, Fritz AG, Greene FL, Trotti A, editors.
American Joint Committee of Cancer – AJCC Cancer Staging Manual.
New York: Springer; 2010.
10. Studer G, Lütolf UM, El–Bassiouni M, Rousson V, Glanzmann C. Volumetric staging (VS) is superior to TNM and AJCC staging in predicting
outcome of head and neck cancer treated with IMRT. Acta Oncol 2007;46:
APRIL 2013
11. Brown J, Buckley D, Coulthard A, et al. Magnetic resonance imaging
screening in women at genetic risk of breast cancer: imaging and analysis
protocol for the UK multicentre study. UK MRI Breast Screening Study
Advisory Group. Magn Reson Imaging 2000;18:765–776.
12. Barentsz JO, Jager GJ, Witjes JA, Ruijs JH. Primary staging of urinary
bladder carcinoma: the role of MRI and a comparison with CT. Eur Radiol
13. Alderliesten T, Schlief A, Peterse J, et al. Validation of semiautomatic
measurement of the extent of breast tumors using contrast-enhanced magnetic resonance imaging. Invest Radiol 2007;42:42–49.
14. Shah GV, Fischbein NJ, Gandhi D, Mukherji SK. Dynamic contrastenhanced MR imaging. Top Magn Reson Imaging 2004;15:71–77.
15. Hadjiiski L, Mukherji SK, Ibrahim M, et al. Head and neck cancers on
CT: preliminary study of treatment response assessment based on computerized volume analysis. AJR Am J Roentgenol 2010;194:1083–1089.
16. Gilhuijs KG, Deurloo EE, Muller SH, Peterse JL, Schultze Kool LJ. Breast
MR imaging in women at increased lifetime risk of breast cancer: clinical
system for computerized assessment of breast lesions initial results. Radiology 2002;225:907–916.
APRIL 2013
17. Fischbein NJ, Noworolski SM, Henry RG, Kaplan MJ, Dillon WP, Nelson
SJ. Assessment of metastatic cervical adenopathy using dynamic contrastenhanced MR imaging. AJNR Am J Neuroradiol 2003;24:301–311.
18. Rasch C, Keus R, Pameijer FA, et al. The potential impact of CT-MRI
matching on tumor volume delineation in advanced head and neck cancer.
Int J Radiat Oncol Biol Phys 1997;39:841–848.
19. Grabenbauer GG, Steininger H, Meyer M, et al. Nodal CT density and
total tumor volume as prognostic factors after radiation therapy of stage
III/IV head and neck cancer. Radiother Oncol 1998;47:175–183.
20. Chufal KS, Rastogi M, Srivastava M, Pant MC, Bhatt ML, Srivastava K.
Analysis of prognostic variables among patients with locally advanced head
and neck cancer treated with late chemo-intensification protocol: impact of
nodal density and total tumor volume. Jpn J Clin Oncol 2006;36:537–546.
21. Dunst J, Stadler P, Becker A, et al. Tumor volume and tumor hypoxia in
head and neck cancers. The amount of the hypoxic volume is important.
Strahlenther Onkol 2003;179:521–526.
22. Kuhnt T, Mueller AC, Pelz T, et al. Impact of tumor control and presence
of visible necrosis in head and neck cancer patients treated with radiotherapy or radiochemotherapy. J Cancer Res Clin Oncol 2005;131:758–764.