Cancer Imaging (2012) 12, 72?78
Gadoxetate acid-enhanced MRI of hepatocellular
carcinoma in a c-myc/TGF? transgenic mouse model
including signal intensity and fat
content: initial experience
Huedayi Korkusuza, Lea Knaub, Wolfgang Kromenb, Frank Huebnerb, Renate Hammerstinglb,
Sebastian Lindemayrb, Verena Bihrerc, Albrecht Piiperc, Thomas J Voglb
aDepartment of Nuclear Medicine, Johann Wolfgang Goethe University Hospital, Theodor-Stern-Kai 7,
D-60590 Frankfurt, Germany;bDepartment of Diagnostic and Interventional Radiology,
Johann Wolfgang Goethe University Hospital, Theodor-Stern-Kai 7, D-60590 Frankfurt, Germany;
cDepartment of Medicine I, Johann Wolfgang Goethe University Frankfurt, Theodor-Stern-Kai 7,
60590 Frankfurt, Germany
Corresponding address: Huedayi Korkusuz, Department of Nuclear Medicine, Johann Wolfgang Goethe University,
Theodor-Stern-Kai 7, 60590 Frankfurt, Germany.
Date accepted for publication 9 December 2011
Genetically engineered mouse models, such as double transgenic c-myc/TGF? mice, with specific pathway abnorm-
alities might be more successful at predicting the clinical response of hepatocellular carcinoma (HCC) treatment. But
a major drawback of the tumour models is the difficulty of visualizing endogenously formed tumours. The optimal
imaging procedure should be brief and minimally invasive. Magnetic resonance imaging (MRI) satisfies these criteria
and gadoxetate acid-enhanced MRI improves the detection of HCC. Fat content is stated to be an additional tool to
help assess tumour responses, for example, in cases of radiofrequency ablation. Therefore the aim of this study was to
investigate if gadoxetate acid-enhanced MRI could be used to detect HCC in c-myc/TGF? transgenic mice by
determining the relation between the signal intensity of HCC and normal liver parenchyma and the corresponding
fat content as a diagnostic marker of HCC. In our study, 20 HCC in c-myc/TGF? transgenic male mice aged 20?34
weeks were analyzed. On gadoxetate acid-enhanced MRI, the signal intensity was 752.4 for liver parenchyma and
924.5 for HCC. The contrast to noise ratio was 20.4, the percentage enhancement was 267.1% for normal liver
parenchyma and 353.9% for HCC. The fat content was 11.2% for liver parenchyma and 16.2% for HCC. There was
a correlation between fat content and signal intensity with r¼0.7791. All parameters were statistically significant
with P50.05. Our data indicate that gadoxetate acid contrast enhancement allows sensitive detection of HCC
in c-myc/TGF? transgenic mice and determination of the fat content seems to be an additional useful parameter
Keywords: MRI; hepatocellular carcinoma; gadoxetate acid; c-myc/TGF? transgenic mouse model; fat content.
Hepatocellular carcinoma (HCC) is a malignant disease
of the liver and one of the most common malignancies
worldwide. HCC, has a poor prognosis and develops
based on steatohepatitic and cirrhotic predamaged liver
parenchyma. The poor prognosis of HCC is a conse-
quence of the detection of HCC at advanced stages, at
which curative options such as transplantation, surgical
resection or local ablation are not available. Therefore in
the last few years new approaches were developed to
improve early detection of HCC and to improve
This paper is available online at http://www.cancerimaging.org. In the event of a change in the URL address, please use the DOI
provided to locate the paper.
? 2012 International Cancer Imaging Society
monitoring therapeutic success. Appropriately designed
mouse models would be highly useful to facilitate
the development of new tumour diagnostics and thera-
pies. However, neither cell-based assays nor xenograft
models are particularly successful in predicting drug
responses in humans. Genetically engineered mouse
models recapitulating specific pathway abnormalities
such as double transgenic c-myc/TGF? mice might be
more successful at predicting clinical response[2,3]. A
major drawback of the tumour models is the difficulty
to visualize the endogenously formed tumours. Optimal
imaging procedure should be brief and minimally inva-
sive. Magnetic resonance imaging (MRI) satisfies these
criteria and gadoxetate acid (Primovist, Bayer Schering
Pharma AG, Berlin, Germany)-enhanced MRI improves
the detection of HCC[4,5]and has a better performance
than computed tomography or unenhanced MRIand
Gadoxetate acid (gadolinium ethoxybenzyl diethylene-
triamine pentaacetic acid) is a liver-specific lipophilic
contrast agent, behaving both as an extracellular and
hepatobiliary agent. Most malignant lesions including
HCCs and metastatic liver tumours do not take up gadox-
etate acid, resulting in detection of tumours as hypoin-
tense nodules on the hepatobiliary phase of gadoxetate
acid-enhanced MRI. Accordingly, 92% of human HCCs
appear as hypointense nodules in gadoxetate acid-
enhanced MRI, but 3% show a hyperintense signal due
to the overexpression of a particular sodium independent
organic anion transporter. Changes in the fatty com-
ponent are one prognostic marker. Fat content is stated
to be an additional tool to help assess tumour responses
and to determine treatment success or failure of radio-
frequency ablation. To verify a correlation between
the signal intensity of gadoxetate acid-enhanced MRI
and the fat content of HCC, the fat content of HCC
was specified by Dixon in-phase (IP) and opposed-
phase (OP) MRI, confirmed by histopathologic analysis
and compared with the signal intensity of gadoxetate
acid-enhanced T1 weighted fat-suppressed MRI.
The aim of this study was to evaluate gadoxetate acid-
enhanced MRI in a c-myc/TGF? transgenic mouse
model for detecting HCC by determining the relationship
between the signal intensity and the fat content of HCC.
Materials and methods
Twenty-one c-myc/TGF? transgenic mice aged 20?34
weeks were examined by gadoxetate acid-enhanced
MRI. These double transgenic male c-myc/TGF? mice
were generated by breeding the homozygous single trans-
genic mice. Hepatocarcinogenesis was induced by zinc in
the drinking water, resulting in 100% incidence of HCC
after 6?9 months[11,12]. These animals show an overex-
pression of the oncogene c-myc and of transforming
growth factor-alpha (TGF-?). The process was confirmed
by pathologic examination after MRI analysis. The
c-myc/TGF? transgenic mouse model was established
by Thorgeirsson et al. (National Cancer Institute, NIH,
Bethesda, MD, USA) to investigate the molecular events
underlying human hepatic malignant transformation. The
governmental committee and our institutional animal
research review board approved this study.
MRI was performed by 3-T MRI (Siemens Magnetom
Trio, Erlangen, Germany). An MRI coil for 3-T (8 chan-
nel multifunctional coil; NORAS MRI Products GmbH,
H€ ochberg, Germany) was used to increase the signal.
Each coil was configured as a 4-channel array. All
images were obtained in the axial plane. The imaging
sequences included T1-weighted turbo-spin echo with
the following parameters: echo time (TE)¼20ms,
repetition time (TR)¼947ms, field of view¼100mm,
slice thickness¼1mm, flip angle¼140?and fat sup-
pression¼fat saturation. The imaging sequences for
determination of fat content were: IP: TR¼5.96ms,
TR¼5.96ms, TE¼3.675ms, slice thickness¼1mm.
The spatial resolution (matrix) was 160?160 with a
voxel size of 1.2?1.2?1mm.
Mice were anesthetized by intraperitoneal administra-
tion of ketamine and xylazine. Then 200ml of 10mM
gadoxetate acid were injected into the retrobulbar
venous plexus before MRI. The imaging sequences for
determination of fat content were performed immediately
after administration of contrast agent. The sequences to
determine the signal intensity of liver parenchyma and
HCC were obtained before and 20min after administra-
tion of contrast medium.
All MRI examinations were transferred to a picture
archiving and communication system (PACS; Centricity,
Chicago, IL, USA) viewing station. Each lesion was eval-
uated by statistical analysis based on the measurement of
the signal intensity of T1-weighted MRI by operator-
defined regions of interest (ROI). The signal intensity
of liver and HCC were determined. Five ROIs were
selected in normal liver parenchyma, avoiding blood ves-
sels, and 5 ROIs were selected in HCC. The contrast to
noise ratio and percentage enhancement were calculated
based on these values (Table 1). They were expressed as
means?standard deviation (SD).
To assess one normally distributed, independent sample,
the one-sample t test was used. In the case of 2 non-nor-
mally distributed independent samples, the non-para-
metric Mann-Whitney test was used. Results were
considered to be significant at P50.05. Linear regression
Gadoxetate acid-enhanced MRI of hepatocellular carcinoma 73
was determined by single linear regression (Pearson) and
the analysis of matched pairs was performed using the
Wilcoxon matched pairs test.
Determination of fat content
Fat content in MRI was calculated using a formula in
Dixon T1 IP and OP MRI sequences, as shown in Figs. 1
and 2. Five ROIs on each gadoxetate acid-enhanced
image were selected on IP and OP sequences in normal
liver tissue as well as in HCC. They were expressed as
means?SD. The fat content was estimated applying the
Fat content liver
¼(liver signal intensity IP?liver signal intensity OP)
liver signal intensity IP
The resected liver specimens were fixed in 4% formalin
and cut in their entirety in 3-mm slices to facilitate careful
gross examination. Specimens were embedded in paraf-
fin, cut in 4-mm sections, and stained with hematoxylin
and eosin for histopathologic evaluation.
Twenty-one c-myc/TGF? transgenic mice with HCC
were used to detect HCC by gadoxetate acid-enhanced
MRI. One mouse was excluded from this study because
T1 IP and OP images could not be analyzed for the
determination of fat content.
Gadoxetate acid-enhanced MRI parameters
T1-weighted MRI of the liver in a c-myc/ TGF? mouse
with HCC is shown in Fig. 3. On gadoxetate acid admin-
istration, 20 HCC appeared hyperintense in T1-weighted
fat-suppressed MRI. The signal intensity is statistically
images and the comparison between the signal intensity
of the liver and HCC was statistically significant
(P50.05) (Table 2).
The difference between the signal intensity of the
tumour and the liver parenchyma in relation to the back-
ground noise was 20.4?8.3 (P50.05).
Calculation of the statistical parameters of gadoxetate acid-enhanced MRI
Contrast to noise ratio
(Signal intensity of HCC ? signal intensity of liver parenchyma)/SD image noise
[(Signal intensity of Gd-EOB-DTPA-enhanced ? signal intensity of unenhanced)/signal
intensity if unenhanced]?100
Formulas to calculate the contrast to noise ratio and percentage enhancement for image analysis of gadoxetate acid-enhanced MRI of c-myc/TGF?
transgenic mice using the signal intensity and standard deviation (SD) of normal liver parenchyma and HCC.
TGF? mouse. IP chemical shift MR images of the same
lesion (arrow) as in Fig. 3.
Gadoxetate acid-enhanced MRI of a c-myc/
TGF? mouse. OP chemical shift MR images of the
same lesion (arrow) as in Fig. 3.
Gadoxetate acid-enhanced MRI of a c-myc/
74H. Korkusuz et al.
The percentage enhancement of unenhanced com-
pared with gadoxetate acid-enhanced liver parenchyma
(Table 2). This comparison was statistically significant
(P50.05). The comparison between the percentage
enhancement values of HCC and liver parenchyma
revealed that gadoxetate acid enhancement of HCC
was 84.3?35.4% higher than that of normal liver
and 353.9?39.1 forHCC
Correlation of fat content and signal
All mice had steatosis and fatty HCC. The mean value of
the fat content based on McPherson?s formula was
11.2?1.5% for liver parenchyma and 16.2?2.7% for
HCC. According to these values, the difference between
the fat content in liver parenchyma and HCC was statis-
tically significant with P50.05 (Table 2). There was a
correlation between fat content and signal intensity of
r¼0.7791 (Pearson) as shown in Fig. 4. The comparison
of these matched pairs was statistically significant
The pathologic diagnosis and evaluation of the fat con-
tent was made based on resected livers post mortem.
Histologic patterns were primarily well-differentiated
HCCs with a Dixon score of 2 and a fat content of
5?25%, representing mild fatty degeneration (Fig. 5).
Previous studies have shown that MRI is highly useful for
the diagnosis of hepatic lesions. In particular, gadoxetate
acid considerably improved the detection of HCC[14?18].
Other studies have shown that T1-weighted fat-sup-
pressed images are superior to other imaging techniques
such as non-fat-suppressed T1-weighted or T2-weighted
The results of this study confirm that gadoxetate acid is
useful to detect HCC in the c-myc/TGF? transgenic
mouse model. This is shown by the signal intensity, con-
trast to noise ratio and percentage enhancement. HCCs
could be easily distinguished from normal liver parench-
yma as demonstrated by the statistically significant
difference in the signal intensity of normal liver
Statistical parameters of gadoxetate acid-enhanced MRI
HCCLiver parenchyma Mean difference:
normal liver vs HCC
Contrast to noise ratio
Fat content (%)
**One-sample t test.
***Wilcoxon matched pairs test.
gadoxetate acid-enhanced liver parenchyma (triangle) and
gadoxetate acid-enhanced HCC (cross) with r¼0.7791
(Pearson). The comparison of these matched pairs was
statistically significant (P50.05) (Wilcoxon matched
Correlation of fat content and signal intensity of
TGF? mouse. T1-weighted 3-T MRI with HCC (arrow)
with hyperintensive enhancement.
Gadoxetate acid-enhanced MRI of a c-myc/
Gadoxetate acid-enhanced MRI of hepatocellular carcinoma 75
parenchyma compared with HCCs. Even more important
for the evaluation of the relation between normal liver
parenchyma and HCC of gadoxetate acid enhancement
is the contrast to noise ratio, which confirmed the differ-
ence in signal intensity between HCC and liver parench-
yma and excluded the influence of the image noise.
Percentage enhancement also verified the benefit of
gadoxetate acid-enhanced MRI in that HCCs showed
an increased uptake of gadoxetate acid compared with
normal liver parenchyma by 84.3?35.4%. In addition,
fatty HCCs are associated with a high signal intensity.
This reflects that HCCs contain a higher fat content than
liver parenchyma. This was confirmed by histology and a
higher signal intensity than the surrounding normal liver
parenchyma. On the other hand, normal liver tissue has a
low fat content and according to this, a low signal inten-
sity. In conclusion, a high percentage enhancement is
associated by a high fat content.
Reason for hyperintensive gadoxetate acid
enhancement: hepatic gadoxetate
Other studies addressing the reason for gadoxetate
acid enhancement of HCC in MRI stated that the
grade of cell differentiation[20,21], bile production, or
necrosis of HCCdid not correlate with the enhance-
ment ratio of gadoxetate acid. The enhancement ratios
correlate with lesion size, bile accumulation in
tumoursand with levels of sodium independent
uptake of gadoxetate acid is considered to represent
passive diffusion mediated by organic anion transporter
polypeptide 1 (OATP1), which is expressed on the hepa-
tocyte membrane. According to this hypothesis, HCC
appear hyperintensive in gadoxetate acid-enhanced MRI
because of the overexpression of transporters such as
OATP1B1, OATP1B3 and NTCP[24?27].
Gadoxetate acid characteristics
After the uptake of gadoxetate acid, the lipophilic con-
trast agent remains in the hepatocytes. Our study shows a
correlation between gadoxetate acid-enhanced signal
intensity and fat content (r¼0.7791), which is visualized
in Fig. 4. HCCs often contain an adipose component and
show various grades of fatty infiltration, which may be
diffuse throughout the tumour, in focal areas or in a
patchy pattern. The fat content was even more frequent
in small HCC nodules[10,28]. The mean value of fat con-
tent was 16.2% for HCCs and 11.2% for normal liver
parenchyma in c-myc/TGF? mice. This suggests that
the metabolic activity is reprogrammed towards steatosis.
This internal fat deposition is assessed by quantification
of intrahepatocellular lipid by MRI by exploiting charac-
teristic differences in resonant frequencies between pro-
tons in fat and water environments, determining the
differences in intensity between IP and OP images[29?31].
MRI is proven to be comparably accurate in quantifying
hepatic fat contentand neither interaction between
Dixon IP/OP or?fat saturation and stage of fibrosis or
hepatic inflammation affect the accuracy of MRI for the
assessment of steatosis. Therefore, MRI is an elegant
method for the non-invasive quantification of the hepatic
Increased lysophosphatidic acid (LPA) may provide
a potent mitogenic and proliferative microenvironment
via autocrine and paracrine activation of high-affinity
G-protein-coupled receptors and cellular proliferation is
accompanied by reprogramming of metabolic activity,
such as high rates of glycolysis, lactate production and
lipid biosynthesis. Hence lipids are linked to pathologic
process such as inflammation, obesity and liver disease,
and they are involved in cellular signaling. They are
also involved in apoptosis and cell cycle regulation.
Specific receptors, Rho and Rho kinase, have the ability
to stimulate cell proliferation. For that reason, LPA sig-
naling has been linked to cancer, which means that an
overexpression of Rho-GTPase binding proteins is asso-
ciated with lysophosphatidic acid signaling. The current
hypothesis is that control of metabolic activity in tumour
cells is synchronous with that of growth factor signaling.
The fat content in HCC and in liver parenchyma of
c-myc/TGF? transgenic mice make it likely that growth
factor signaling regulates metabolic activity. This may
explain the high fat content of HCC compared with
normal liver parenchyma. Thus, the increased fat content
of HCC may retain gadoxetate acid in HCC cells, pre-
venting hepatic secretion via the multidrug-resistance
associated proteins, ABCB4 and ABCC2.
Fat content as a prognostic marker
of successful therapy
Fat content is a valuable predictor of successful therapy.
This is demonstrated by other studies. They stated that
(arrow) with a Dixon score of 2.
Normal liver parenchyma and liver tumour
76H. Korkusuz et al.
visceral fat accumulation, which is related to the severity
of fatty liver, increases the risk of HCC development and
is an independent risk factor of HCC after curative treat-
ment. In particular, a high fat content of the liver,
with regard to non-alcoholic steatohepatitis, is a risk
factor for HCC[36?38]. Other risk factors are age 462
years, poor histopathologic grading, multifocal tumour,
portal vein thrombosis, higher alpha-fetoprotein and
serum bilirubin levels[38,39]. The risk factor fat content
can be surveyed by gadoxetate acid-enhanced MRI. In
accordance with our results that fat content and signal
intensity correlate, high signal intensity by gadoxetate
acid-enhanced MRI might be used as a prognostic
marker of successful therapy. According to this, changes
in the fatty component might be used to monitor therapy.
Thus, Pupulim et al.demonstrated monitoring for
therapy success of radiofrequency ablation.
Benefit of gadoxetate acid-enhanced MRI
in the c-myc/TGF? mouse model
Transgenic HCC mouse models such as the c-myc/TGF?
model used in the present study are valuable for detecting
HCC due to its similarity to human HCC. The advantage
of mouse models in research has already been shown by
the detection of HCC initiation and progression in trans-
genic mouse models such as alb-myctg with clinical 1.5-T
MR scanners and gadoxetate acid enhancement by
revealing hypointense lesions.
The second advantage of gadoxetate acid-enhanced
MRI for detecting HCC is the relation between signal
intensity and fat content. Our study indicates that one
reason for gadoxetate acid enhancement is steatosis.
Therefore, changes in the fatty component could be an
additional finding to help to assess tumour responses and
success of therapy. This was revealed by a study of radio-
frequency ablation of fatty HCCs and suggests that fat
content could be used as a prognostic marker for HCC
Even though this transgenic mouse model for detecting
HCC has many advantages, such as histologic similarity
to human tumours, the tumours arise in immunocompe-
tent mice, metastatic distribution similar to the clinical
situation, relevant host immune cell infiltration and
tumour microenvironment, the results of gadoxetate
acid enhancement cannot uncritically be transferred to
Highly hepatocyte-selective enhancement of gadoxetate
acid is correlated with the amount of HCC fat content.
Gadoxetate acid enhancement, based on its correlation
with the fat content, might be a useful tool as a prognos-
tic marker or for monitoring therapy. This animal model
may help to develop a better understanding of HCC
gadoxetate acid contrast enhancement and thus of
HCC diagnostics and therapy.
We thank Snorri S. Thorgeirsson (National Cancer
Institute, NIH, Bethesda, MD, USA) for kindly providing
c-myc/TGF? transgenic mice.
 Kudo M. Multistep human hepatocarcinogenesis: correlation of
imaging with pathology. J Gastroenterol 2009; 44(Suppl 19):
 Newell P, Villanueva A, Llovet JM. Molecular targeted therapies
in hepatocellular carcinoma: from pre-clinical models to clinical
trials. J Hepatol 2008; 49: 1?5. doi:10.1016/j.jhep.2008.04.006.
 Olive KP, Tuveson DA. The use of targeted mouse models for
preclinical testing of novel cancer therapeutics. Clin Cancer Res.
2006; 12: 5277?87. doi:10.1158/1078-0432.CCR-06-0436.
 Kanematsu M, Kondo H, Goshima S, Tsuge Y, Watanabe H.
Magnetic resonance imaging of hepatocellular carcinoma.
Oncology 2008; 75(Suppl 1): 65?71. doi:10.1159/000173426.
 Montfoort JEV, Stieger B, Meijer DK, Weinmann HJ, Meier PJ,
Fattinger KE. Hepatic uptake of the magnetic resonance imaging
contrast agent gadoxetate by the organic anion transporting poly-
peptide Oatp1. J Pharmacol Exp Ther 1999; 290: 153?7.
 Ichikawa T, Saito K, Yoshioka N, et al. Detection and character-
ization of focal liver lesions: a Japanese Phase III, multicenter
comparison between gadoxetic acid disodium-enhanced magnetic
resonance imaging and contrast-enhanced computed tomography
predominantly in patients with hepatocellular carcinoma and
 Kim YK, Kim CS, Han YM, et al. Detection of hepatocellular
carcinoma: gadoxetic acid-enhanced 3-dimensional magnetic res-
onance imaging versus multi-detector row computed tomography.
J Computer Assist Tomogr 2009; 33: 844?50. doi:10.1097/
 Vogl TJ, K€ ummel S, Hammerstingl R, et al. Liver tumors: com-
parison of MR imaging with Gd-EOB-DTPA and Gd-DTPA.
Radiology 1996; 200: 59?67.
 Kogita S, Imai Y, Okada M, et al. Gd-EOB-DTPA-enhanced mag-
netic resonance images of hepatocellular carcinoma: correlation
with histological grading and portal blood flow. Eur Radiol 2010;
20: 2405?13. doi:10.1007/s00330-010-1812-9.
 Pupulim LF, Hakim? e A, Barrau V, Abdel-Rehim M, Zappa M,
Vilgrain V. Fatty hepatocellular carcinoma: radiofrequency abla-
tion: imaging findings. Radiology 2009; 250: 940?8. doi:10.1148/
 CalvisiDF, Thorgeirsson
hepatocarcinogenesis in transgenic mouse models of liver
cancer. Toxicol Pathol 2005;
 Murakami H, Sanderson ND, Nagy P, Marino PA, Merlino G,
Thorgeirsson SS. Transgenic mouse model for synergistic effects
of nuclear oncogenes and growth factors in tumorigenesis: inter-
action of c-myc and transforming growth factor alpha in hepatic
oncogenesis. Cancer Res 1993; 53: 1719?23.
 McPherson S, Jonsson JR, Cowin GJ, et al. Magnetic resonance
imaging and spectroscopy accurately estimate the severity of stea-
tosis provided the stage of fibrosis is considered. J Hepatol 2009;
51: 389?97. doi:10.1016/j.jhep.2009.04.012.
 Ahn SS, Kim M, Lim JS, Hong HS, Chung YE, Choi JY. Added
value of gadoxetic acid-enhanced hepatobiliary phase MR
Gadoxetate acid-enhanced MRI of hepatocellular carcinoma 77
imaging in the diagnosis of hepatocellular carcinoma. Radiology Download full-text
2010; 255: 459?66. doi:10.1148/radiol.10091388.
 Shimofusa R, Ueda T, Kishimoto T, et al. Magnetic resonance
imaging of hepatocellular carcinoma: a pictorial review of novel
insights into pathophysiological features revealed by magnetic
resonance imaging. J Hepato-Biliary-Pancreatic Surg 2009; 17:
 Reimer P, Rummeny EJ, Daldrup HE, et al. Enhancement char-
acteristics of liver metastases, hepatocellular carcinomas, and
hemangiomas with Gd-EOB-DTPA: preliminary results with
dynamicMR imaging. Eur
 Tanimoto A, Lee JM, Murakami T, Huppertz A, Kudo M,
Grazioli L. Consensus report of the 2nd International Forum
for Liver MRI. Eur Radiol 2009; 19(Suppl 5): S975?89.
 Motosugi U, Ichikawa T, Sou H, et al. Liver parenchymal
enhancement of hepatocyte-phase images in Gd-EOB-DTPA-
enhanced MR imaging: which biological markers of the liver func-
tion affect the enhancement? J Magn Reson Imaging 2009; 30:
 Koushima Y, Ebara M, Fukuda H, et al. Small hepatocellular
carcinoma: assessment with T1-weighted spin-echo magnetic res-
onance imaging with and without fat suppression. Eur J Radiol
2002; 41: 34?41. doi:10.1016/S0720-048X(01)00346-1.
 Frericks BB, Loddenkemper C, Huppertz A, et al. Qualitative and
quantitative evaluation of hepatocellular carcinoma and cirrhotic
liver enhancement using Gd-EOB-DTPA. AJR Am J Roentgenol
2009; 193: 1053?60. doi:10.2214/AJR.08.1946.
 Fujita M, Yamamoto R, Takahashi M, et al. Paradoxic uptake of
Gd-EOB-DTPA by hepatocellular carcinoma in mice: quantitative
image analysis. J Magn Reson Imaging 1997; 7: 768?70.
 Vossen JA, Buijs M, Geschwind JH, et al. Diffusion-weighted and
Gd-EOB-DTPA-contrast-enhanced magnetic resonance imaging
for characterization of tumor necrosis in an animal model.
J Computer Assist Tomogr 2009; 33: 626?30. doi:10.1097/
 Bos ICVD, Hussain SM, Dwarkasing RS, et al. MR imaging of
hepatocellular carcinoma: relationship between lesion size and
imaging findings, including signal intensity and dynamic enhance-
ment patterns. J Magn Reson Imaging 2007; 26: 1548?55.
 Tsuboyama T, Onishi H, Kim T, et al. Hepatocellular carcinoma:
hepatocyte-selective enhancement at gadoxetic acid-enhanced
MR imaging?correlation with expression of sinusoidal and cana-
licular transporters and bile accumulation. Radiology 2010; 255:
 Narita M, Hatano E, Arizono S, et al. Expression of OATP1B3
determines uptake of Gd-EOB-DTPA in hepatocellular carci-
noma. J Gastroenterol 2009; 44: 793?8. doi:10.1007/s00535-
 Leonhardt M, Keiser M, Oswald S, et al. Hepatic uptake of the
magnetic resonance imaging contrast agent Gd-EOB-DTPA, role
Radiol 1997;7: 275?80.
of human organic anion transporters. Drug Metab Dispos 2010;
38: 1024?8. doi:10.1124/dmd.110.032862.
 Kudo M. Will Gd-EOB-MRI change the diagnostic algorithm in
hepatocellular carcinoma? Oncology 2010; 78(Suppl 1): 87?93.
 Saito K, Kotake F, Ito N, et al. Gd-EOB-DTPA enhanced MRI for
hepatocellular carcinoma: quantitative evaluation of tumor
enhancement in hepatobiliary phase. Magn Reson Med Sci
2005; 4: 1?9. doi:10.2463/mrms.4.1.
 Dixon WT. Simple proton spectroscopic imaging. Radiology
1984; 153: 189?94.
 Hussain HK, Chenevert TL, Londy FJ, et al. Hepatic fat fraction:
MR imaging for quantitative measurement and display?early
 Schuchmann S, Weigel C, Albrecht L, et al. Non-invasive quan-
tification of hepatic fat fraction by fast 1.0, 1.5 and 3.0 T MR
imaging. EurJ Radiol2007;
 Skill NJ, Scott RE, Wu J, Maluccio MA. Hepatocellular carci-
noma associated lipid metabolism reprogramming. J Surg Res
 Griffitts J, Tesiram Y, Reid GE, Saunders D, Floyd RA,
Towner RA. In vivo MRS assessment of altered fatty acyl unsa-
turation in liver tumor formation of a TGF alpha/c-myc trans-
genic mousemodel.J Lipid
 Ohki T, Tateishi R, Shiina S, et al. Visceral fat accumulation is an
independent risk factor for hepatocellular carcinoma recurrence
after curative treatment in patients with suspected NASH. Gut
2009; 58: 839?44. doi:10.1136/gut.2008.164053.
 Takuma Y, Nouso K. Nonalcoholic steatohepatitis-associated
hepatocellular carcinoma: our case series and literature review.
World J Gastroenterol 2010; 16: 1436?41. doi:10.3748/
 Kawada N, Imanaka K, Kawaguchi T, et al. Hepatocellular
carcinoma arising from non-cirrhotic nonalcoholic steatohepati-
tis. J Gastroenterol 2009; 44: 1190?4. doi:10.1007/s00535-009-
 Hashizume H, Sato K, Takagi H, et al. Primary liver cancers with
nonalcoholic steatohepatitis. Eur J Gastroenterol Hepatol 2007;
19: 827?34. doi:10.1097/MEG.0b013e3282748ef2.
 Kirchner G, Kirovski G, Hebestreit A, et al. Epidemiology and
survival of patients with hepatocellular carcinoma in Southern
Germany. Int J Clin Exp Med 2010; 3: 169?79.
 Freimuth J, Gassler N, Moro N, et al. Application of magnetic
resonance imaging in transgenic and chemical mouse models of
hepatocellular carcinoma. Mol Cancer 2010; 9: 94. doi:10.1186/
 Wu L, Tang Z, Li Y. Experimental models of hepatocellular
carcinoma: developments and evolution. J Cancer Res Clin
Oncol 2009; 135: 969?81. doi:10.1007/s00432-009-0591-7.
Res 2009;50: 611?622.
78 H. Korkusuz et al.