Sensitivity and accuracy of volumetry of pulmonary nodules on low-dose 16- and 64-row multi-detector CT: an anthropomorphic phantom study.

Abstract

OBJECTIVE: To assess the sensitivity of detection and accuracy of volumetry by manual and semi-automated quantification of artificial pulmonary nodules in an anthropomorphic thoracic phantom on low-dose CT. METHODS: Fifteen artificial spherical nodules (diameter 3, 5, 8, 10 and 12 mm; CT densities -800, -630 and +100 HU) were randomly placed inside an anthropomorphic thoracic phantom. The phantom was examined on 16- and 64-row multidetector CT with a low-dose protocol. Two independent blinded observers screened for pulmonary nodules. Nodule diameter was measured manually, and volume calculated. For solid nodules (+100 HU), diameter and volume were also evaluated by semi-automated software. Differences in observed volumes between the manual and semi-automated method were evaluated by a t-test. RESULTS: Sensitivity was 100 % for all nodules of >5 mm and larger, 60-80 % for solid and 0-20 % for non-solid 3-mm nodules. No false-positive nodules but high inter-observer reliability and inter-technique correlation were found. Volume was underestimated manually by 24.1 ± 14.0 % for nodules of any density, and 26.4 ± 15.5 % for solid nodules, compared with 7.6 ± 8.5 % (P < 0.01) semi-automatically. CONCLUSION: In an anthropomorphic phantom study, the sensitivity of detection is 100 % for nodules of >5 mm in diameter. Semi-automated volumetry yielded more accurate nodule volumes than manual measurements. KEY POINTS: • Computed tomography has become the definitive investigation of the chest. • Low-dose CT techniques have recently been introduced. • Low-dose CT is reliable for detecting spherical pulmonary nodules of >5 mm. • Semi-automated volumetry is more accurate than manual measurement for pulmonary nodules. • No difference in the accuracy of volumetry was found between 16- and 64- MDCT.

Full text

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CHEST
Sensitivity and accuracy of volumetry of pulmonary nodules
on low-dose 16- and 64-row multi-detector CT:
an anthropomorphic phantom study
Xueqian Xie & Yingru Zhao & Roland A. Snijder &
Peter M. A. van Ooijen & Pim A. de Jong & Matthijs Oudkerk &
Geertruida H. de Bock & Rozemarijn Vliegenthart &
Marcel J. W. Greuter
Received: 12 March 2012 /Revised: 28 May 2012 /Accepted: 1 June 2012
#The Author(s) 2012. This article is published with open access at Springerlink.com
Abstract
Objective To assess the sensitivity of detection and accuracy
of volumetry by manual and semi-automated quantification
of artificial pulmonary nodules in an anthropomorphic
thoracic phantom on low-dose CT.
Methods Fifteen artificial spherical nodules (diameter 3, 5,
8, 10 and 12 mm; CT densities -800, -630 and +100 HU)
were randomly placed inside an anthropomorphic thoracic
phantom. The phantom was examined on 16- and 64-row
multidetector CT with a low-dose protocol. Two indepen-
dent blinded observers screened for pulmonary nodules.
Nodule diameter was measured manually, and volume
calculated. For solid nodules (+100 HU), diameter and
volume were also evaluated by semi-automated software.
Differences in observed volumes between the manual and
semi-automated method were evaluated by a t-test.
Results Sensitivity was 100 % for all nodules of >5 mm and
larger, 60–80 % for solid and 0–20 % for non-solid 3-mm
nodules. No false-positive nodules but high inter-observer
reliability and inter-technique correlation were found. Volume
was underestimated manually by 24.1±14.0 % for nodules of
any density, and 26.4±15.5 % for solid nodules, compared
with 7.6±8.5 % (P<0.01) semi-automatically.
Conclusion In an anthropomorphic phantom study, the
sensitivity of detection is 100 % for nodules of >5 mm in
diameter. Semi-automated volumetry yielded more accurate
nodule volumes than manual measurements.
Key Points
• Computed tomography has become the definitive investiga-
tion of the chest.
• Low-dose CT techniques have recently been introduced.
• Low-dose CTis reliable for detecting spherical pulmonary
nodules of >5 mm.
• Semi-automated volumetry is more accurate than man-
ual measurement for pulmonary nodules.
• No difference in the accuracy of volumetry was found
between 16- and 64- MDCT.
Keywords Lungneoplasms.Massscreening.
Pulmonarynodule.Phantoms.Imaging.Tomography.
X-raycomputed
X. Xie:Y. Zhao:R. A. Snijder:P. M. A. van Ooijen:
R. Vliegenthart:M. J. W. Greuter (*)
Department of Radiology, University of Groningen,
University Medical Center Groningen,
EB44, P.O. Box 30.001, 9700 RB Groningen, The Netherlands
e-mail: m.j.w.greuter@umcg.nl
X. Xie:Y. Zhao:P. M. A. van Ooijen:M. Oudkerk:
R. Vliegenthart
Center for Medical Imaging–North East Netherlands,
Department of Radiology,
University of Groningen, University Medical Center Groningen,
Groningen, The Netherlands
P. A. de Jong
Department of Radiology, University Medical Center Utrecht,
Utrecht, The Netherlands
G. H. de Bock
Department of Epidemiology, University of Groningen,
University Medical Center Groningen,
Groningen, The Netherlands
Eur Radiol
DOI 10.1007/s00330-012-2570-7

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Abbreviations and acronyms
16-MDCT
64-MDCT
CT
HU
NELSON
16-Row multi-detector CT
64-Row multi-detector CT
Computed tomography
Hounsfield units
The Dutch-Belgian Randomized Lung
Cancer Screening Trial
The National Lung Screening Trial
The Response Evaluation Criteria in
Solid Tumors
NLST
RECIST
Introduction
The most common cause of cancer-related death is lung
cancer. In 2000, lung cancer accounted for 17 % of total
cancer mortality [1]. Despite advances in treatment, the 5-
year survival rate is still only 15 % or even less, as many
lung cancers are found at a relatively late stage [2]. Poten-
tially, screening for lung cancer may improve the prognosis.
About a decade ago, low-dose computed tomography (CT)
was proposed as a promising method to screen for lung
cancer. Several cohort studies and randomised clinical trials
using low-dose CT were started, aiming to investigate the
effect of lung cancer screening on the distribution of tumour
stages and eventually the effect on survival [3–5]. Recently,
the initial, encouraging results of one of the largest trial
cohorts were published: lung cancer screening by low-dose
CTreduced lung cancer-specific mortality by 20 % compared
with chest radiography [3].
Efficient lung cancer screening depends on the accurate
distinction between benign and malignant pulmonary
lesions, but starts with sensitive observer detection of pul-
monary nodules. There are only scarce validation data on
the detectability of small pulmonary nodules by low-dose
CT [6, 7]. In one previous study using older generation CT
equipment with slightly thicker collimation, the sensitivity
of the detection of pulmonary nodules was evaluated in a
thoracic phantom without pulmonary vessels, in which arti-
ficial nodules were placed at known locations. Pulmonary
nodules as small as 2.4 mm in diameter could be detected by
conventional dose CT [8]. However, no data exist about the
observer sensitivity on current thin-slice low-dose CT for
pulmonary nodules in an anthropomorphic pulmonary
background with pulmonary vessels. In addition, limited
data are available on the accuracy of volumetry of pulmo-
nary nodules on low-dose CT. Therefore, the aim of this
manuscript was to assess the sensitivity of detection of
artificial pulmonary nodules on low-dose CT, randomly
placed in an anthropomorphic pulmonary background with
pulmonary vessels, and to determine the accuracy of
volumetry of detected nodules by manual and semi-
automated measurements.
Materials and methods
An anthropomorphic thoracic phantom (Lungman, Kyoto
Kagaku, Tokyo, Japan) with artificial thoracic wall, heart,
mediastinum, diaphragm and lung with pulmonary vessels
was used (Fig. 1). The phantom consisted of an accurate
life-size anatomical model of a male thorax with soft tissue
substitute materials made of polyurethane resin composites
and synthetic bones made of epoxy resin with X-ray absorp-
tion rates very close to those of human tissue. The space
between the pulmonary vessels in the thoracic cavity con-
sisted of air. In addition, we used 15 artificial spherical
pulmonary nodules with a smooth surface in five diameters
(3, 5, 8, 10 and 12 mm, corresponding to a volume of 14, 65,
268, 523 and 904 mm3) and 3 CT densities [-800, -630 and
+100 Hounsfield Units (HU)]. The artificial solid nodules
were made of polyurethane resin and the artificial non-solid
nodules were made of polyurethane foam resin.
16-row multi-detector CT (16-MDCT) and 64-row multi-
detector CT (64-MDCT) (Sensation 16 and Sensation 64,
respectively, Siemens, Forchheim, Germany) were used. A
clinically used low-dose protocol for lung cancer screening
was applied for image acquisition [9]: spiral acquisition at
120 kV, 20 mAs, 0.5 s rotation time, pitch 1.5 and collima-
tion 16×0.75 mm and 2×32×0.6 mm. The CT images were
reconstructed with a slice thickness of 1 mm and increment
of 0.7 mm using a medium B30f kernel and a field of view
300 mm.
The artificial nodules were randomly positioned in both
artificial lungs. All nodules were attached to pulmonary
vessels. None of the nodules were attached to pleura or
positioned sub-pleurally. A random, pre-determined set of
6 nodules was positioned in the artificial lungs, after which
CTexamination was performed. Each of the 15 nodules was
examined in total 5 times; thus, 13 different sets of nodules
were positioned in the phantom (the last set consisted only
of 3 nodules). The CT examination was, for each new
phantom nodule set-up, repeated five times, with a small
translocation and rotation of the phantom in between each
examination to simulate participant movement. The thoracic
phantom was also examined five times without pulmonary
nodules to serve as a control. Thus, per CT technique 70
examinations were performed, including 65 test examina-
tions and 5 control examinations. The thoracic phantom was
examined with the same settings for the two CT techniques.
Furthermore, all the nodules were examined in air, on the
CT table, to confirm the visibility on low-dose CT.
The reconstructed data were evaluated on a dedicated
workstation (Leonardo, Siemens, Forchheim, Germany) by
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two independent observers, both with experience in thoracic
diagnostic imaging for more than 8 years, who were blinded
to information about the presence, properties and location of
the artificial pulmonary nodules. All the examinations were
read by both observers. The observers were instructed to
review the images for the presence of nodules within a
clinically relevant time duration (about 2 min per examina-
tion). The observers were asked to report whether there were
one or more pulmonary nodules present or not. If a potential
nodule was observed, the images were compared with the
images of the control CT examination without nodules to
confirm it was not a false-positive finding caused by pul-
monary background structures. Subsequently, the slice with
maximal cross-sectional area of the nodule was selected.
Then, the diameter and CT density were manually mea-
sured. The diameter was measured according to the Re-
sponse Evaluation Criteria in Solid Tumors (RECIST)
[10]. A region of interest was drawn as large as possible
within the nodule border to measure the CT density. Be-
cause all nodules were spherical, the volume of the nodules
could be easily calculated from the measured diameter.
Additionally, a dedicated semi-automated software tool
(LungCARE, Siemens, Forchheim, Germany) was used to
measure the volume of the detected solid nodules (CT
density +100 HU). The diameter and volume of identified
nodules were automatically calculated by this three-
dimensional volumetric assessment software.
Statistics
The sensitivity of detection of artificial pulmonary nodules
was calculated for three densities (-800, -630 and +100 HU)
and for 16- and 64-row multidetector CT. The inter-observer
reliability for both manual and semi-automated measure-
ments was assessed using an intraclass correlation coeffi-
cient. The correlation of measurements between 16- and 64-
row multidetector CT was expressed as a Pearson’s correla-
tion coefficient. The inter-observer and inter-machine agree-
ment of nodule volumetry was analysed using Bland-
Altman plots. If there was a difference in the measurements
between 16- and 64-row multidetector CT, or between the
two observers, an independent sample t-test was used. If
there was a difference in measuring diameter and volume
using the manual and semi-automated methods, a paired-
samples t-test was used. To find the difference between the
observed value (diameter, volume and density) and the
actual value, a one-sample t-test was used. Results were
given as mean ± standard deviation (SD). A P<0.05 was
considered to be statistically significant. All statistical anal-
yses were performed with a software package (SPSS 18.0.3,
IBM, New York, USA).
Results
Representative CT images of the anthropomorphic thoracic
phantom are shown in Fig. 2. Nodules sized 5 mm in
diameter and larger of all CT densities were detected by
both observers on all examinations obtained with 16- and
64-MDCT. For nodules sized 3 mm in diameter, solid nod-
ules (CT density +100 HU) were detected on 15 out of 25
examinations (60 %) for both reviewers in 16-MDCT, and
15 (60 %) and 20 (80 %) examinations for the first and the
second reviewer respectively in 64-MDCT. However, non-
solid nodules with CT density of -630 HU were only
detected on 5 examinations out of 25 (20 %) by the second
reviewer on 16-MDCT. Non-solid nodules with CT density
of -800 HU were not detected on any examination (Fig. 3).
Each observed nodule was compared with the control
examination to confirm the presence of the nodule; no
false-positive nodules were found. All the nodules found
were measured successfully using the manual or semi-
automated method, except for one nodule with a diam-
eter of 3 mm and a density of +100 HU for which
segmentation by the semi-automated method failed. Fur-
thermore, on the CT examinations of the nodules in air
(without the phantom), all 15 nodules were visualised
on low-dose CT.
Fig. 1 a and b An anthropomorphic thoracic phantom with (c) artificial pulmonary nodules with five different diameters (3, 5, 8, 10 and 12 mm,
corresponding to volumes of 14, 65, 268, 523 and 904 mm3) and three different densities (-800, -630 and +100 HU)
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The inter-observer reliability was very good with an
intraclass correlation coefficient of 0.985 (P<0.001) and
1.000 (P<0.001) for manual and semi-automated measure-
ment. The correlation of nodule measurements between 16-
MDCTand 64-MDCTwas high with a Pearson’s correlation
coefficient of 0.983 (P<0.001) and 0.999 (P<0.001) for
manual and semi-automated measurements, respectively.
An increasing relative inter-observer and inter-machine
volumetry difference at smaller nodule size was found
(Figs. 4 and 5). The mean absolute value of relative inter-
observer difference was 11.7±14.4 % and 3.3 %±6.6 % for
manual and semi-automated volumetry, respectively. The
mean absolute value of relative inter-scanner difference
was 12.9±12.4 % and 4.0±5.6 %, respectively. There was
Fig. 2 (a) Axial, (b) coronal
and (c) sagittal images of the
anthropomorphic thoracic
phantom with (d) a non-solid
pulmonary nodule measured
manually and (e) a solid nodule
assessed semi-automatically
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no significant difference in the diameter measurements or
CT density between the two techniques or between the two
observers (P>0.05).
In both the manual and the semi-automated method,
nodulediameterandvolumeweresignificantlyunderestimated
compared with the actual properties (P<0.01) (Table 1). An
increasing underestimation of nodule diameter and volume
at smaller nodule size was found (Figs. 6 and 7). In diameter
evaluation, the overall underestimation for nodules of any
density was 9.2±6.0 % using the manual method. For solid
nodules, the underestimation was 10.1±6.9 % using the man-
ual method, compared with 3.7±7.1 % (P<0.01) using the
semi-automated method. In volumetry, the overall underesti-
mation for nodules of any density was 24.1±14.0 % using the
manual method. For solid nodules, the underestimation was
26.4±15.5 % using the manual method, compared with 7.6±
8.5 % (P<0.01) using the semi-automated method.
The mean measured CT density was -813±23 HU, -647±
9 HU and 123±61 HU for nodule density of -800 HU,
-630 HU and +100 HU, respectively (Table 1), thus deviating
1.7±2.3 %, -2.7±1.5 % and 26±57 % from the expected
density (P<0.01).
Discussion
In one of the first pulmonary nodule validation studies using
an anthropomorphic thoracic phantom and current low-dose
CT technology, we have shown that a clinically used lung
cancer screening protocol with low-dose CT has 100 %
sensitivity of detection for spherical pulmonary nodules
sized 5 mm in diameter and larger. In addition, we have
shown that low-dose CT yields more accurate nodule vol-
ume measurements when using a semi-automated method
than in the case of the manual method, with negligible
underestimation of actual size, especially for small nodules.
We found a sensitivity of 100 % for nodules with a
diameter equal to or larger than 5 mm for all three nodule
Fig. 3 Sensitivity of the detection of artificial pulmonary nodules for
three densities (-800, -630 and +100 HU) and two CT techniques
(16-MDCT and 64-MDCT)
Fig. 4 Bland-Altman plots for inter-observer agreement of the measured volume
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densities in this anthropomorphic thoracic phantom, and
60–80 % and 0–20 % for solid and non-solid nodules with
a diameter of 3 mm, respectively. This anthropomorphic
thoracic phantom was also used in another study in which
sensitivity of 95 % for solid nodules and 74–81 % for
non-solid nodules were found for nodule diameter equal to
or larger than 5 mm [11]. Unlike the low-dose acquisition
protocol for lung cancer screening in this study, the authors
Fig. 5 Bland-Altman plots for inter-scanner agreement of the measured volume
Table 1 Measurements of diameter and volume of artificial pulmonary nodules by the manual and semi-automated methods
Actual property Manual measurement Semi-automated measurement
Density, HU Diameter, mm Volume, mm3
Density, HUDiameter, mm Volume, mm3
Diameter, mm Volume, mm3
-8003
5
8
10
12
3
5
8
10
12
3
5
8
10
12
14
65
268
523
904
14
65
268
523
904
14
65
268
523
904
n/d
-811±15*
-818±6*
-819±18*
-806±8*
-639±25*
-646±14*
-646±5*
-648±6*
-650±6*
-2±56*
161±24*
158±10*
137±8*
128±6*
n/d
4.3±0.3*
7.4±0.3*
9.4±0.4*
11.4±0.3*
2.6±0.1*
4.3±0.3*
7.4±0.3*
9.4±0.3*
11.3±0.4*
2.4±0.3*
4.2±0.2*
5.9±1.2*
9.5±0.3*
11.3±0.3*
n/d
41.4±8.2*
210±27*
431±54*
768±70*
9.3±1.3*
42.7±8.0*
213±29*
439±39*
763±69*
7.1±3.9*
37.9±6.0*
193±25*
445±48*
761±51*
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
2.6±0.4*†
4.8±0.2*†
7.6±0.2*†
9.8±0.3*†
11.7±0.3*†
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
10.9±2.4*†
59.7±2.6*†
245±4.5*†
513±12*†
856±8*†
-630
+100
The measured values were averaged over 16-slice and 64-slice CT for both observers
*P<0.01 when compared with actual properties,†P<0.01 when compared with manual measurement
n/a: Not available, n/d: not detectable
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did not use a low-dose protocol, which limits comparability.
In addition, the authors used 5-mm reconstructed slice thick-
ness, compared with 1 mm in this study. It is well known
that a larger slice width results in lower sensitivity of pul-
monary nodules [6]. This anthropomorphic phantom was
also used for an image database of nodules of diameter
larger than 5 mm of several shapes, but results for nodule
detectability and comparing between manual and semi-
automated measurements were not reported [12]. In some
nodule detectability studies, solid nodules with a diameter of
2 to 3 mm were detected in all cases [8, 13, 14]. In these
studies, the nodules were placed in known order and
examined in a thoracic phantom without lung vessels. On
the other hand, in our study the artificial nodules were
randomly positioned inside the lungs of an anthropomorphic
thoracic phantom, thus limiting detection bias and strength-
ening the findings. As adjacent tissue can interfere with the
nodule image reconstruction and reading, especially for
non-solid nodules, and because this interference increases
with decreasing radiation dose, we expect that this interfer-
ence explains why we could not detect some of the 3-mm
nodules.
No false-positive nodules were found compared with the
control examinations. That is to say, all nodules detected on
Fig. 6 Deviation of the
measured diameter from the
actual diameter by manual
measurement for nodules
of -800, -630 and +100 HU,
and by semi-automated
measurement for nodules
of +100 HU
Fig. 7 Deviation of the
measured volume from the
actual volume by manual
volumetry for nodules of
-800, -630 and +100 HU, in
addition to by semi-automated
volumetry for nodules of
+100 HU
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low-dose CT were actual nodular lesions. Nevertheless, in
clinical practice, pulmonary parenchyma can be distorted
and may contain scars and variations, which can erroneously
be interpreted as a pulmonary nodule; thus, the specificity in
a clinical setting is usually decreased.
We found an increasing underestimation of the nodule
volume at smaller nodule diameters, which was also found
in some previous studies [15, 16]. However, some other
studies reported an increasing overestimation of the nodule
volume at smaller nodule diameters [17–22]. For small
pulmonary nodules, the transit zone between nodule and
pulmonary background caused by partial volume effect
was found to be important for accurate volumetry [23].
Thus, measurement errors in small nodules when measured
manually should be considered.
In this study, we found that the measured nodule density
was significantly different from the expected density. In
lung cancer screening by unenhanced CT examination,
accurate CT density is important mainly to differentiate
between solid and non-solid nodules, and to evaluate
increases in density over time in the case of non-solid
nodules. However, as we only had one type of solid nodule,
and two types of non-solid nodule with a relatively large
difference in CT density compared with the solid nodules,
no reliable conclusion can be drawn about the potential
impact of CT density variation on lung cancer screening
results. Future studies with more variation in the density of
solid nodules, with CT densities within the clinically rele-
vant range, have been planned to investigate the impact of
CT density on nodule volumetry in more detail.
No difference was found between low-dose 16- and 64-
row multi-detector CT from the same vendor regarding
manual and semi-automated volumetry. However, a previ-
ous study found different nodule volumetry outcomes for
four 16-row CT systems from different vendors [17]. As
the follow-up of screened participants or clinical patients
can last for an extensive period, different CT systems from
different vendors can be used. A direct comparison of
nodule volumes obtained from different CT systems from
the same vendor seems valid, at least for the vendor
investigated in this study. However, whether similar
nodule volumes would have been found for other vendors
is unknown.
Clinical implications
Some lung cancer screening projects were mainly based on
nodule diameter [3], whereas other lung cancer screening
projects were mainly based on nodule volume measure-
ments [9]. In the National Lung Screening Trial (NLST)
study, a positive result indicating suspected lung cancer on
low-dose CT was defined as the presence of a nodule
with a largest transverse axis of at least 4 mm [4]. In the
Dutch-Belgian Randomized Lung Cancer Screening Trial
(NELSON) study, a positive result was defined as either a
fast-growing nodule with a volume of at least 50 mm3, i.e.,
nearly 5 mm in diameter, or a nodule with a volume of at
least 500 mm3[9]. The results of this study validate these
screening protocols, as all solid nodules and non-solid
nodules with a diameter of 5 mm could be detected by
observers on low-dose CT against an anthropomorphic
pulmonary background.
Pulmonary nodule volumetry is used to guide the diag-
nostic strategy in the follow-up of lung cancer screening [9].
Repeated CT-derived volumetry of pulmonary nodules can
be used to determine the risk of lung cancer and can be used
to monitor tumour response in the case of non-surgical
therapy. The accuracy and precision of pulmonary nodule
volumetry depend on a number of factors, including image
acquisition and reconstruction parameters, nodule character-
istics, and the performance of algorithms for nodule seg-
mentation and volume estimation [24]. Size measurement
needs to be as accurate as possible in order to enable the
assessment of nodule growth. A commonly used criterion
for pulmonary nodule growth is given by the Response
Evaluation Criteria in Solid Tumors (RECIST), which states
that nodules in the stable disease category should not be
larger than 20 % or smaller than 30 % in diameter on
subsequent CT examinations [10]. However, a 20 % error
in diameter measurement could result in an error in volume
for a spherical nodule of up to 73 %, which could result in
inaccurate growth rate evaluation. To improve accuracy in
growthrateevaluation,semi-automatedvolumetryisfavoured
over manual volumetry.
Limitations
There are limitations to this study. Firstly, only spherical
nodules were used with five discrete sizes. Additional data
on the sensitivity of nodules with sizes between 3 and 5 mm
in diameter are needed in order to determine the sensitivity
of current low-dose CTand to optimise diagnostic screening
strategies for small nodules. A further extension to our study
is the assessment of the sensitivity of low-dose CT for non-
spherical and irregularly shaped (lobulated and/or spicu-
lated) nodules. Secondly, we simulated healthy pulmonary
tissues. The sensitivity of nodule detection is dependent on
pulmonary structures, and surrounding pathological lesions
such as fibrosis, emphysema or consolidation could influ-
ence nodule detectability, which can make pulmonary
nodules undetectable or be erroneously interpreted as pul-
monary nodules. We therefore expect that the sensitivity in
an in vivo setting may be lower, and that the false-positive
rate may be higher, compared with the findings in this study.
Thirdly, we used only one clinical low-dose CT screening
protocol. Although the sensitivity of pulmonary nodules is
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also dependent on CT protocol, the protocol we used is the
most common in current lung cancer screening projects
using thin-slice, low-dose CT. Therefore, we expect that this
protocol is the most relevant for the sensitivity of pulmonary
nodule detection in lung cancer screening. Finally, semi-
automated volumetry was not performed for non-solid
nodules, because the present commercially available volu-
metry software was only for solid nodules. In case of the
considerable volumetry deviation from the actual volume in
non-solid nodules by manual measurements, a special
software package for semi-automated volume measurement
of non-solid nodules is needed to assess these non-solid
nodules.
In conclusion, this anthropomorphic phantom study
shows that a lung cancer screening protocol with low-dose
CT is highly reliable for the detection of spherical pulmo-
nary nodules of 5 mm in diameter and larger. Low-dose CT
yields more accurate nodule volumetry when using a semi-
automated software tool than manual measurements, with
negligible underestimation of actual size, especially for
small nodules. For early lung cancer detection, in which
mostly small nodules are found, accurate measurement is
especially necessary to enable assessment of volume dou-
bling time. Thus, a semi-automated volume measurement
should be used in the setting of lung cancer screening. No
difference in the accuracy of volumetry was found between
16- and 64-row multi-detector CT.
Acknowledgements
MSc, for performing the CT acquisitions.
The authors wish to thank Wim GJ Tukker,
Open Access
tive Commons Attribution Noncommercial License which permits any
noncommercial use, distribution, and reproduction in any medium,
provided the original author(s) and the source are credited.
This article is distributed under the terms of the Crea-
References
1. Shibuya K, Mathers CD, Boschi-Pinto C, Lopez AD, Murray CJ
(2002) Global and regional estimates of cancer mortality and
incidence by site: II. Results for the global burden of disease
2000. BMC Cancer 2:37
2. Janssen-Heijnen ML, Coebergh JW (2001) Trends in incidence
and prognosis of the histological subtypes of lung cancer in North
America, Australia, New Zealand and Europe. Lung Cancer
31:123–137
3. Aberle DR, Adams AM, Berg CD et al (2011) Reduced lung-
cancer mortality with low-dose computed tomographic screening.
N Engl J Med 365:395–409
4. Gatsonis CA, National Lung Screening Trial Research Team
(2011) The National Lung Screening Trial: overview and study
design. Radiology 258:243–253
5. Nair A, Hansell DM (2011) European and North American lung
cancer screening experience and implications for pulmonary
nodule management. Eur Radiol 21:2445–2454
6. Silverman JD, Paul NS, Siewerdsen JH (2009) Investigation of
lung nodule detectability in low-dose 320-slice computed
tomography. Med Phys 36:1700–1710
7. Paul NS, Siewerdsen JH, Patsios D, Chung TB (2007) Investigating
the low-dose limits of multidetector CT in lung nodule surveillance.
Med Phys 34:3587–3595
8. Ko JP, Rusinek H, Jacobs EL et al (2003) Small pulmonary
nodules: volume measurement at chest CT - phantom study.
Radiology 228:864–870
9. van Klaveren RJ, Oudkerk M, Prokop M et al (2009) Management
of lung nodules detected by volume CT scanning. N Engl J Med
361:2221–2229
10. Eisenhauer EA, Therasse P, Bogaerts J et al (2009) New response
evaluation criteria in solid tumours: revised RECIST guideline
(version 1.1). Eur J Cancer 45:228–247
11. Higashihara H, Osuga K, Azuma T et al (2011) Detection of
pulmonary nodules by C-arm CT using a phantom lung: compar-
ison with CT. Acta Radiol 52:964–968
12. Gavrielides MA, Kinnard LM, Myers KJ et al (2010) A resource
for the assessment of lung nodule size estimation methods: data-
base of thoracic CT scans of an anthropomorphic phantom. Opt
Express 18:15244–15255
13. Lee YC, Fullerton GD, Baiu C, Lescrenier MG, Goins BA (2011)
Preclinical multimodality phantom design for quality assurance of
tumor size measurement. BMC Med Phys 11:1
14. Ravenel JG, Leue WM, Nietert PJ, Miller JV, Taylor KK, Silvestri
GA (2008) Pulmonary nodule volume: effects of reconstruction
parameters on automated measurements—a phantom study.
Radiology 247:400–408
15. Linning E, Ma D (2009) Volumetric measurement pulmonary
ground-glass opacity nodules with multi-detector CT: effect of
various tube current on measurement accuracy - a chest CT
phantom study. Acad Radiol 16:934–939
16. Zhang L, Yankelevitz DF, Henschke CI, Jirapatnakul AC, Reeves
AP, Carter D (2010) Zone of transition: a potential source of error
in tumor volume estimation. Radiology 256:633–639
17. Das M, Ley-Zaporozhan J, Gietema HA et al (2007) Accuracy of
automated volumetry of pulmonary nodules across different
multislice CT scanners. Eur Radiol 17:1979–1984
18. Kim KG, Goo JM, Kim JH et al (2005) Computer-aided diagnosis
of localized ground-glass opacity in the lung at CT: initial
experience. Radiology 237:657–661
19. Marten K, Funke M, Engelke C (2004) Flat panel detector-based
volumetric CT- prototype evaluation with volumetry of small artifi-
cial nodules in a pulmonary phantom. J Thorac Imaging 19:156–163
20. Tao P, Griess F, Lvov Y et al (2004) Characterization of small
nodules by automatic segmentation of X-ray computed tomography
images. J Comput Assist Tomogr 28:372–377
21. Way TW, Chan HP, Goodsitt MM et al (2008) Effect of CT
scanning parameters on volumetric measurements of pulmonary
nodules by 3D active contour segmentation: a phantom study. Phys
Med Biol 53:1295–1312
22. Winer-Muram HT, Jennings SG, Meyer CA et al (2003) Effect of
varying CT section width on volumetric measurement of lung
tumors and application of compensatory equations. Radiology
229:184–194
23. Goo JM, Tongdee T, Tongdee R, Yeo K, Hildebolt CF, Bae KT
(2005) Volumetric measurement of synthetic lung nodules with
multi-detector row CT: effect of various image reconstruction
parameters and segmentation thresholds on measurement accuracy.
Radiology 235:850–856
24. Gavrielides MA, Kinnard LM, Myers KJ, Petrick N (2009)
Noncalcified lung nodules: volumetric assessment with thoracic
CT. Radiology 251:26–37
Eur Radiol