Computed tomography imaging of primary lung cancer in mice using a liposomal-iodinated contrast agent.

Abstract

To investigate the utility of a liposomal-iodinated nanoparticle contrast agent and computed tomography (CT) imaging for characterization of primary nodules in genetically engineered mouse models of non-small cell lung cancer.
Primary lung cancers with mutations in K-ras alone (Kras(LA1)) or in combination with p53 (LSL-Kras(G12D);p53(FL/FL)) were generated. A liposomal-iodine contrast agent containing 120 mg Iodine/mL was administered systemically at a dose of 16 µl/gm body weight. Longitudinal micro-CT imaging with cardio-respiratory gating was performed pre-contrast and at 0 hr, day 3, and day 7 post-contrast administration. CT-derived nodule sizes were used to assess tumor growth. Signal attenuation was measured in individual nodules to study dynamic enhancement of lung nodules.
A good correlation was seen between volume and diameter-based assessment of nodules (R(2)>0.8) for both lung cancer models. The LSL-Kras(G12D);p53(FL/FL) model showed rapid growth as demonstrated by systemically higher volume changes compared to the lung nodules in Kras(LA1) mice (p<0.05). Early phase imaging using the nanoparticle contrast agent enabled visualization of nodule blood supply. Delayed-phase imaging demonstrated significant differential signal enhancement in the lung nodules of LSL-Kras(G12D);p53(FL/FL) mice compared to nodules in Kras(LA1) mice (p<0.05) indicating higher uptake and accumulation of the nanoparticle contrast agent in rapidly growing nodules.
The nanoparticle iodinated contrast agent enabled visualization of blood supply to the nodules during the early-phase imaging. Delayed-phase imaging enabled characterization of slow growing and rapidly growing nodules based on signal enhancement. The use of this agent could facilitate early detection and diagnosis of pulmonary lesions as well as have implications on treatment response and monitoring.

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Computed Tomography Imaging of Primary Lung Cancer
in Mice Using a Liposomal-Iodinated Contrast Agent
Cristian T. Badea1*, Khannan K. Athreya2, Gabriela Espinosa3,4, Darin Clark1, A. Paiman Ghafoori5,
Yifan Li5, David G. Kirsch5,6, G. Allan Johnson1, Ananth Annapragada3, Ketan B. Ghaghada3*
1Center for In Vivo Microscopy, Duke University Medical Center, Durham, North Carolina, United States of America, 2University of Texas Medical School at Houston, The
University of Texas Health Sciences Center at Houston, Houston, Texas, United States of America, 3The Edward B. Singleton Department of Pediatric Radiology, Texas
Children’s Hospital, Houston, Texas, United States of America, 4School of Biomedical Informatics, The University of Texas Health Sciences Center at Houston, Houston,
Texas, United States of America, 5Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, United States of America, 6Department of
Pharmacology and Cancer Biology, Duke University Medical Center, Durham, North Carolina, United States of America
Abstract
Purpose: To investigate the utility of a liposomal-iodinated nanoparticle contrast agent and computed tomography (CT)
imaging for characterization of primary nodules in genetically engineered mouse models of non-small cell lung cancer.
Methods: Primary lung cancers with mutations in K-ras alone (KrasLA1) or in combination with p53 (LSL-KrasG12D;p53FL/FL)
were generated. A liposomal-iodine contrast agent containing 120 mg Iodine/mL was administered systemically at a dose
of 16 ml/gm body weight. Longitudinal micro-CT imaging with cardio-respiratory gating was performed pre-contrast and at
0 hr, day 3, and day 7 post-contrast administration. CT-derived nodule sizes were used to assess tumor growth. Signal
attenuation was measured in individual nodules to study dynamic enhancement of lung nodules.
Results: A good correlation was seen between volume and diameter-based assessment of nodules (R2.0.8) for both lung
cancer models. The LSL-KrasG12D;p53FL/FLmodel showed rapid growth as demonstrated by systemically higher volume
changes compared to the lung nodules in KrasLA1mice (p,0.05). Early phase imaging using the nanoparticle contrast agent
enabled visualization of nodule blood supply. Delayed-phase imaging demonstrated significant differential signal
enhancement in the lung nodules of LSL-KrasG12D;p53FL/FLmice compared to nodules in KrasLA1mice (p,0.05) indicating
higher uptake and accumulation of the nanoparticle contrast agent in rapidly growing nodules.
Conclusions: The nanoparticle iodinated contrast agent enabled visualization of blood supply to the nodules during the
early-phase imaging. Delayed-phase imaging enabled characterization of slow growing and rapidly growing nodules based
on signal enhancement. The use of this agent could facilitate early detection and diagnosis of pulmonary lesions as well as
have implications on treatment response and monitoring.
Citation: Badea CT, Athreya KK, Espinosa G, Clark D, Ghafoori AP, et al. (2012) Computed Tomography Imaging of Primary Lung Cancer in Mice Using a
Liposomal-Iodinated Contrast Agent. PLoS ONE 7(4): e34496. doi:10.1371/journal.pone.0034496
Editor: Juri G. Gelovani, University of Texas, M.D. Anderson Cancer Center, United States of America
Received November 10, 2011; Accepted March 1, 2012; Published April 2, 2012
Copyright: ? 2012 Badea et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported in part by NIH/NCRR National Biomedical Technology Resource Center grants (P41 RR005959, NCI U24 CA092656) (CTB, GAJ).
Part of this work was supported by funding from Marval Biosciences Inc. (AA, KBG), American Society of Clinical Oncology, Young Investigator Award (APG) and
NCI K08 CA 114176 (DGK). No additional external funding was received for this study. The funders had no role in study design, data collection and analysis,
decision to publish, or preparation of the manuscript.
Competing Interests: AA is a co-founder and shareholder in Marval. CTB is a consultant for Marval and holds stock options. There is nothing to declare
regarding employment, consultancy, patents, products in development or marketed products. This does not alter the authors’ adherence to all the PLoS ONE
policies on sharing data and materials.
* E-mail: cristian.badea@duke.edu (CTB); kbghagha@texaschildrens.org (KBG)
Introduction
Lung cancer is the leading cause of cancer death (,28%) in
both men and women and the number of deaths are expected to
increase 50% by 2020 worldwide [1]. In order to reduce mortality
rates, the focus on lung cancer management has shifted to early
detection and personalized cancer therapy [2]. In a recent study,
computed tomography (CT) screening of patients at high risk
reduced lung cancer deaths by 20% [3] demonstrating the
potential benefit of screening for early stage lung cancer. However,
surveillance of suspected lung nodules frequently requires
longitudinal follow-up to evaluate changes in nodule size and
growth rate. A majority of these cases require multiple follow-up
CT scans, ranging up to two years, before a diagnosis of
malignancy is made [4]. Advances in imaging techniques that
improve characterization of pulmonary nodules could have a
substantial impact on patient management and economic burden
of lung cancer.
Novel imaging techniques that can take advantage of differences
in tumor morphology between malignant and benign nodules are
being evaluated. Dynamic contrast-enhanced (DCE)-CT imaging
has been evaluated for differentiation of benign and malignant
tumors based on nodule perfusion and tumor vessel permeability
[5,6]. While promising, the use of conventional contrast agents
present challenges in quantitative perfusion analysis due to rapid
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leakage into the extravascular space, even during first pass
imaging. Furthermore, the molecular nature of iodinated contrast
agents, similar in size to Magnetic Resonance (MR) contrast
agents, makes them less sensitive to changes in vascular
morphology that occur at the nano and micro scales [7,8,9].
Macromolecular and nanoparticle-based imaging agents could
potentially provide a more accurate measurement of nodule
perfusion and vessel permeability. In a recent pre-clinical study,
the use of a nanoparticle-based, liposomal-iodinated CT contrast
agent for differentiation of tumors based on their growth-rate was
demonstrated using 2D clinical mammography in a rat model of
mammary adenocarcinoma [10]. The study demonstrated that
rapidly growing tumors showed increased vascular permeability to
nanoparticle contrast agents when compared to slow growing
tumors. In this study, we therefore sought to evaluate the utility of
the liposomal-iodinated CT contrast agent for characterization of
slow growing and rapidly growing pulmonary nodules in
genetically engineered mouse models of primary non-small cell
lung cancer.
Materials and Methods
1. Ethics statement
All animals were handled in accordance with good animal
practice as defined by the relevant national and/or local animal
welfare bodies, and all animal work was approved by the
Institutional Animal Care and Use Committee (IACUC) of Duke
University Medical Center. The Duke University Medical Center
animal management program is accredited by the American
Association for the Accreditation of Laboratory Animal Care and
meets National Institute of Health standards as set forth in the
‘‘Guide for the Care and Use of Laboratory Animals’’ (DHHS
Publication No. (NIH) 85–23, Revised 1985). The institution also
accepts as mandatory the PHS ‘‘Policy on Humane Care and Use
of Laboratory Animals by Awardee Institutions’’ and ‘‘NIH
Principles for the Utilization and Care of Vertebrate Animals
Used in Testing, Research and Training’’.
2. Fabrication of liposomal CT contrast agent
Liposomal-iodinated CT contrast agent was prepared using
methods described previously [7]. Briefly, a lipid mixture
(150 mmol/L) consisting of 1,2-dipalmitoyl-sn-glycero-3-phospho-
choline (DPPC), cholesterol, and 1,2-distearoyl-sn-glycero-3-phos-
phoethanolamine-N-[methoxy (polyethylene glycol)-2000] (DSPE-
MPEG 2000) in a 55:40:5 molar ratio was dissolved in ethanol.
The ethanol solution was hydrated with iodixanol solution
(550 mg I/mL) and then sequentially extruded on a Lipex
Thermoline extruder (Northern Lipids, Vancouver, British
Columbia, Canada) to size the liposomes to ,100 nm. The
resulting solution was diafiltered using a MicroKrosH module
(Spectrum Laboratories, CA) to remove un-encapsulated iodix-
anol. The size distribution of liposomes in the final formulation
was determined by dynamic light scattering (DLS) using a Malvern
Zetasizer Nanoseries (Malvern Instruments, Worcestershire, UK)
at 25uC. The iodine concentration in the final liposomal solution
was quantified by spectrophotometry (Abs at 245 nm). The final
iodine concentration in the PEGylated liposomal-iodine formula-
tion was 120 mg/mL. The average liposome size was 118620 nm
and the poly-dispersity index was less than 0.15.
3. In vivo studies
i.Primary
processing.
previously [11,12,13]. Two primary lung cancer models were
lungcancermodelsand tissue
Primary lung tumors were developed as described
developed in this study: LSL-KrasG12D;p53FL/FLmice with
expression of oncogenic KrasG12Dand deletion of p53 following
intranasal infection with Adeno-Cre; and KrasLA1mice with
expression of only oncogenic KrasG12Dfollowing spontaneous
intra-chromosomal recombination of the latent Kras allele. The
LSL-KrasG12D;p53FL/FLanimals were used for the imaging study
at 12 weeks post Adeno-Cre infection. All animals were imaged at
24–30 weeks of age. A total of ten animals (five per group) were
used for the imaging study.
After the final imaging session, the animals were sacrificed and
perfused with phosphate buffered saline. The lungs were extracted,
snap frozen in liquid nitrogen and then stored at 280uC.
Hematoxylin and Eosin (H&E) staining was performed on the
tissue sections to survey tumor morphology.
ii. Micro-CT setup and imaging study.
dual-source-detector micro-CT imaging system was used for the
study [14]. Only one X-ray source and detector, were used for
imaging and a scan took about 7 minutes to complete. The
animals were scanned while free breathing under anesthesia using
2–3% isoflurane delivered by nose-cone setup. Prospective cardio-
respiratory gating was used to minimize effects of animal
respiratory and cardiac motion during scans [15]. A pneumatic
pillow positioned on the animals’ thorax connected to a pressure
transducer was used to provide an electrical signal that correlated
with the breathing motion. ECG pads were used to acquire the
ECG signal. A LabVIEW application read both the respiratory
and ECG signals and provided a Transistor–Transistor Logic
pulse required for triggering the X-ray tube and detector in end-
expiration and on the R peak of the ECG cycle. The scanning
parameters were: 80 kvp, 160 mA, 10 ms/exposure. A total of
300 views were acquired over 360u rotation. The dose associated
with a single scan was 8 cGy. Volumes were reconstructed using
the Feldkamp algorithm [16] in a matrix of 51265126512 at
88 mm isotropic voxel size.
Longitudinal micro-CT imaging was done in all animals. A pre-
contrast, baseline scan was acquired. Subsequently, the liposomal
contrast agent was intravenously injected over 2 minutes via the
tail vein at a volume dose of 16 mL/gm (,1920 mg Iodine/kg
dose) of body weight. Post-contrast scans were then acquired
immediately (0 hr time-point) and at day 3 (72 hr) and day 7
(168 hr) after administration of the liposomal contrast agent.
A custom-built
4. Image data analysis
Data analysis of all image sets was performed in Osirix (v.3.6 64-
bit). Volume and maximum diameter measurements were
performed on individual lung nodules. A minimum of four
nodules were analyzed in each animal except for one animal in the
benign group that only showed one detectable nodule. Lung
nodules were manually segmented by drawing regions of interest
in the axial plane. The largest nodule cross-section was manually
identified in the axial plan and the nodule diameter was
determined using the line tool in Osirix. The lung nodules were
then assigned into one of the following three groups based on the
nodule diameter (x in mm) measured in the axial plane: Group 1
(1.0,x#1.5), Group 2 (1.5,x#2.5) and Group 3 (2.5,x#3.5).
Nodules smaller than 1 mm were not analyzed because they
represented a very large number of nodules with extremely rapid
volume changes that were convoluted by animal positioning and
which hindered tumor matching in the imaging sets at different
time points.
Relative changes in nodule size were computed as:
% D Volume~½VolDay7{VolDay0?=VolDay0
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VolDay0 is the average of pre-contrast and 0 hr post-contrast
nodule volume; VolDay7is the nodule volume on day 7 following
contrast injection.
The blood clearance of contrast agent was determined by
measuring signal attenuation in a major vessel, the descending
aorta. For the nodules, average signal attenuation was computed
over the entire volume. ROIs were drawn at three different
locations for descending aorta. Signal attenuation was presented as
average values and standard deviations reported in Hounsfield
units (HU).
The differential signal enhancement in each nodule was
computed as:
D CT Signal~HUPOST{HUPRE
where, HUPOSTis the average nodule signal, in Hounsfield units
(HU), immediately post-contrast (0 hr) or day 3 or day 7 and
HUPREis the average nodule signal, in Hounsfield units (HU), in
pre-contrast scan.
The fractional blood volume (FBV), expressed as percentage, in
each nodule was determined using the pre-contrast and the post-
contrast 0-hr datasets according to the equation [17]:
% FBV~100 ? ½HUPOST{HUPRE?NODULE=
½HUPOST{HUPRE?BLOOD
Since residual blood-pool signal was detected on day 7, the
differential signal enhancement for the nodule was corrected to
eliminate the blood volume component of the overall nodule signal
using the following equation:
Signal Enhancement~½HUDay7?HUPRE?NODULE{
f½HUDay7?HUPRE?BLOOD? FBVg
Osirix (v-3.6, 64-bit) and ImageJ (v-1.41o) were used for visual
representation.
Results
A high blood signal enhancement was obtained immediately
post-administration of liposomal contrast agent (Figure 1a). The
signal decayed gradually over time and by day 7, the majority of
liposomal contrast agent had cleared from systemic circulation as
evident by a reduction in blood attenuation signal. Previous
imaging studies using the liposomal contrast agent in mice have
reported a blood half-life of approximately 41 hours [7]. Dynamic
analysis of lung nodules demonstrated signal enhancement
immediately post-contrast (0 hr) indicating high blood volume in
the nodules (Figure 1b). The KrasLA1model showed gradual
decrease in signal enhancement over time, similar to observed
trend for clearance of the contrast agent from systemic circulation.
On day 3 and day 7, the nodules in LSL-KrasG12D;p53FL/FL
model showed significantly higher signal enhancement (p,0.05)
compared to nodules in the KrasLA1model. Histological analysis
of both the primary lung cancer models demonstrated character-
istics similar to those described previously (Figure 2) [11,12,13].
Analysis of pulmonary nodules demonstrated good correlation
between diameter-based and volume-based measurements for
both lung cancer models (r2.0.8) (Figure 3a). The nodule volumes
were measured using micro-CT on day 0 and day 7 post-
administration of the contrast agent. During the imaging period,
the nodules in LSL-KrasG12D;p53FL/FL
mice demonstrated
significant increase (p,0.05) in volume compared to nodules in
KrasLA1mice, indicating higher growth rate (Figure 3b). The
KrasLA1mice did not show nodules larger than 2.5 mm, most
likely due to the slow-growing nature of this model. The blood-
pool property of the liposomal contrast agent also enabled
determination of fractional blood volume. No significance
differences in fractional blood volume were seen between the
two lung cancer models (p,0.05) (Figure 3c).
Both type of lung nodules showed higher attenuation immedi-
ately after administration of liposomal contrast agent, indicating
high tissue perfusion. The high blood-pool attenuation enabled
visualization of vascular network associated with the lung nodules
(Figure 4). Large blood vessels at the surface of the nodules were
observed in both models. The small feature size made it difficult to
probe the vascular structures within the nodule.
Average nodule signal attenuation was measured at baseline and
day 7 post-administration of the liposomal contrast agent. Signal
enhancement in the nodules was determined relative to baseline
(Figure 5). The lung nodules in LSL-KrasG12D;p53FL/FLmice
showed significantly higher signal enhancement compared to the
nodules in KrasLA1mice (p,0.05). The longitudinal aspect of this
study also enabled facile imaging of the delayed tumor
enhancement in the LSL-KrasG12D;p53FL/FLmice (Figure 6).
Discussion
Early detection of primary lung cancer can lead to improved
patient survival. Angiogenesis, one of the hallmarks of solid
tumors, involves the development of new blood vessels. Unlike
normal vessels, tumor-associated vessels have abnormal and ‘leaky’
architecture, exemplified by the presence of large fenestrations
within the endothelial lining that enable not only small molecules
and particulate matter to extravasate into the interstitial region but
also facilitate tumor cells to escape into the systemic circulation
[18]. Non-invasive advanced imaging techniques that can exploit
differences in tumor micro-environment can play an important
role in early detection of cancer.
Several modalities have been used for pre-clinical imaging in
mouse models of lung cancer [11,19]. Among these, CT and MRI,
which are routinely used in the clinic, provide high-spatial
resolution for assessing vascular and morphological changes.
Nuclear imaging techniques such as Positron Emission Tomogra-
phy (PET) and Single Positron Emission Computed Tomography
(SPECT), also used clinically for diagnosis and therapeutic
monitoring of lung cancer, provide high-contrast sensitivity but
with relatively low-spatial resolution. Optical imaging techniques,
such as fluorescence tomography and bioluminescence, which also
provide high sensitivity, have primarily been used pre-clinically to
study lung cancer growth as well as to monitor treatment response
[20,21]. However, the light-based modalities are plagued by low
spatial resolution and limited tissue penetration. They are
performed in combination with micro-CT to provide not only
anatomical reference but also improve image reconstruction.
Primary lung cancers in mice, unlike many other tumor types, are
challenging to image with high-resolution due to cardiac and
respiratory motion artifacts and small tumor sizes. In this work, we
investigated the use of a liposomal-iodinated contrast agent and
micro-CT for characterization of primary lung cancers in mice.
The use of prospective cardio-respiratory gating enabled acquisi-
tion of high quality tomographic and isotropic images at voxel
dimension of 88 mm. Motion challenges imposed by lung imaging
in rodents were overcome with minimally-invasive procedures i.e.,
they required no intubation and mechanical ventilation. The
associated radiation dose of 0.24 Gy accumulated over three
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imaging time points was in the typical range reported in the
literature [22] and therefore is not expected to play a role in the
outcome of results of our imaging study. We note however that the
radiation dose in preclinical studies with micro-CT is much higher
than in clinical studies. This is because a higher resolution is
required in micro-CT and this could be only achieved using more
radiation.
The genetically engineered mouse models of primary lung
cancer used in this study, have previously been characterized and
tested for evaluating chemotherapies and radiation treatment
[11,23]. The LSL-KrasG12D;p53FL/FLmodel, which has mutations
in Kras and p53, results in the generation of aggressive primary
adenocarcinomas. The KrasLA1model, which has mutations in
Kras only, results in the development of primary adenomas which
canprogress tolowgrade
KrasG12D;p53FL/FLmodel showed characteristics of rapid growth
consistent with high grade adenocarcinomas that were consistent
adenocarcinomas. TheLSL-
with previous studies [11,23]. A strong correlation (R2.0.8) was
observed between nodule diameter and volume for both cancer
models. Concerns have been raised about Response Evaluation
Criteria in Solid Tumors (i.e. RECIST)-based response assess-
ments, in part because tumors do not always expand or contract
uniformly; changes in line lengths represent only a small fraction of
the available information in the images [24]. However, because
the two measures correlated well in this study and the size-based
analysis is a commonly used technique in the clinic, subsequent
analysis of nodule growth and signal enhancement was studied by
classifying nodules based on their size.
As demonstrated in this study, the liposomal-iodinated contrast
agent provides two methods for characterizing solid tumors.
During early-phase imaging, which occurs within a few hours
post-administration of the contrast agent, the agent is primarily
distributed in the vascular compartment with negligible extrav-
asation into the tumor tissue. As a result, visualization of tumor
vasculature and blood supply is achieved, thus enabling
assessment of relative blood volume in tumors. As time
progresses, the liposomal-iodinated nanoparticles extravasate into
the tumor tissue via the enhanced permeation and retention effect
resulting in tumor signal enhancement. Consequently, during
delayed-phase imaging, which occurs over several days, the
liposomal contrast agent enables imaging and differentiation of
tumor tissue.
The higher signal enhancement observed in the LSL-
KrasG12D;p53FL/FLmodel suggest increased accumulation and
therefore enhanced vascular permeability to the nanoparticle
contrast agent. Similar phenomenon of increased vascular
permeability to nanoparticles has also been observed in other
highly aggressive tumor models [10]. Furthermore, a recent study
also showed, very elegantly, the changes in tumor vascular
permeability to nanoparticles as tumor transition from pre-
malignant status to a malignant status [25]. Taken together, these
findings suggest that rapidly growing tumors take up more
liposomal contrast agent than slow growing tumors. These could
have important clinical implications as it may enable differenti-
ation and classification of tumors based on their malignancy
potential. However, it is important to note that nodules, even
malignant ones, seen in the clinical setting have relatively slower
growth rates compared to those seen in this study. Thus, such
evaluation will ultimately have to be made in the clinic to
determine potential of this method for effective characterization
and staging of tumors. The increased uptake of liposomal contrast
Figure 1. Dynamic signal enhancement in (a) blood and (b) nodules, in the two primary lung cancer models. Errors bars represent
standard deviations (* indicates p, ,0.05).
doi:10.1371/journal.pone.0034496.g001
Figure 2. Hematoxylin and eosin staining showing character-
istics of high-grade lung cancer in LSL-KrasG12D;p53FL/FLmice
with pleomorphic nuclei (a,b) and low-grade KrasLA1lung
tumors in KrasLA1mice with regular nuclei and minimal
cytologic atypia (c,d). Images were acquired at 106 (a,c) and 406
magnification (c,d). Scale bars: 200 um in a and c; 100 um in b and d.
doi:10.1371/journal.pone.0034496.g002
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agent in rapidly growing tumors suggest that one could use nano-
carriers to deliver high payloads of chemotherapeutics or genetic
materials within these nodules, simply via passive extravasation.
Furthermore, the ability to highlight potential malignant nodules
could also facilitate biopsy as well as accurately delineating tumors
margins using CT imaging for radiotherapy or surgical removal.
As recently shown, targeted delivery of imaging agents and
therapeutics to lung tumors is possible and could provide early
Figure 3. Correlation between nodule volume and diameter in LSL-KrasG12D;p53FL/FL(N) and KrasLA1(6) models. (a). Solid lines indicate
cubic fit to the data points. An R2value of 0.93 and 0.81 was obtained for LSL-KrasG12D;p53FL/FLand KrasLA1models, respectively. Percentage change
in nodule volume in the two lung cancer models as a function of nodule diameter (* indicates p,0.05) (b). CT-derived fractional blood volume as a
function of nodule diameter (c).
doi:10.1371/journal.pone.0034496.g003
Figure 4. Orthgonal thick slab maximum intensity projection (MIP) images demonstrating visualization of nodule blood supply in
the lung cancer models. The images were acquired immediately after administration of liposomal contrast agent.
doi:10.1371/journal.pone.0034496.g004
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detection and increased therapeutic efficacy against cancer
[26]. Freedman et al [27] have recently used a targeted
immunoliposome complex for MRI imaging of lung tumors.
The versatile nature of liposomal platform and its precedent for
use in the clinic has seen a continued interest in this area
resulting in the development of non-targeted and targeted
imaging agents for use in a variety of imaging modalities
[28,29,30].
Although possible, we have not used targeted liposomes in this
study. Instead we have shown that sufficient differential
enhancement of lung tumors can be obtained based on passive
accumulation of liposomes. The current study also has some
limitations. The iodine dose used in this study was 5–10 fold
higher than the iodine dose used routinely in the clinic for
dynamic CT imaging of lung tumors. The high dose was
necessary to overcome relatively higher noise levels (6–10 fold) in
micro-CT compared to clinical CT and to visualize microvascu-
lar structures. Consequently, a longer waiting period was
required to allow blood clearance of a majority of the contrast
agent. While the dose of contrast agent can be reduced, the high
noise levels associated with micro-CT systems (.65 HU) that
arise from high spatial resolution present challenges in quanti-
tative evaluation of small features. A high lipid dose, in order to
deliver high iodine dose, was also used in this study. While such
doses cannot be used in the clinic, we believe that the low noise
levels in clinical CT scanners and the large feature size would
enable reduction in contrast agent, and a corresponding lipid,
dose. Previous studies have demonstrated imaging of clinical-size
lesions in rabbit model using liposomal contrast agent adminis-
tered at an iodine dose comparable to those used in the clinic
[31].
The study also provided insights into quantitative perfusion
imaging using a blood pool contrast agent and CT imaging.
However, routine analysis is challenging in rodent studies due to
small feature size and high noise levels on micro-CT scanners.
Advanced methods that can reduce noise levels, such as iterative
reconstruction techniques [32,33], coupled with increased
contrast-sensitive dual energy imaging techniques [34] may
help in achieving these goals. The success of such imaging
procedures would provide new opportunities to assess the
Figure 5. Differential signal enhancement on day 7 in the two
primary lung cancer nodules. (* indicates p,0.05).
doi:10.1371/journal.pone.0034496.g005
Figure 6. Representative coronal micro-CT images of LSL-KrasG12D;p53FL/FLand KrasLA1mice before liposomal contrast-agent
injection and at 0 hr, Day 3 and Day 7 post-contrast injection. Note the differential enhancement of tumors at Day 7 post-contrast time point
in the LSL-KrasG12D;p53FL/FLlesions only.
doi:10.1371/journal.pone.0034496.g006
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efficacy of anti-angiogenic therapies in pre-clinical cancer
models.
Acknowledgments
Lung tumors were produced from KrasLA1mice and LSL-KrasG12Dmice
provided by Dr. Tyler Jacks and p53FLmice provided by Dr. A. Berns.
The authors would also like to acknowledge Yi Qi for assistance with CT
imaging and Dr. D. Vela and Tommy Reese for help with histology.
Author Contributions
Conceived and designed the experiments: CTB KBG DGK. Performed the
experiments: CTB GAJ DC. Analyzed the data: KKA GE DC KBG.
Contributed reagents/materials/analysis tools: CTB APG YL AA KBG.
Wrote the paper: CTB KBG AA DGK.
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CT Imaging of Lung Cancer
PLoS ONE | www.plosone.org7 April 2012 | Volume 7 | Issue 4 | e34496