Hajime Sagawa1, Masako Kataoka1, Shotaro Kanao2, Natsuko Onishi3, Maya Honda2, Dominik Nickel4, Masakazu Toi5, Katsuhiko Ueda1, and Kaori Togashi2
1Division of Clinical Radiology Service, Kyoto University Hospital, Kyoto, Japan, 2Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan, 3Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States, 4MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany, 5Department of Breast Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
Synopsis
The contrast effects of 1.0M gadobutrol
were compared to that of 0.5M gadoteridol in breast cancer at 3T using hybrid
MRI protocol which combined conventional DCE (C-DCE) with ultrafast DCE
(UF-DCE). The significant differences were observed only in UF-DCE, maximum
enhancement ratio (MER) and maximum slope (MS). The slightly higher peak
relative enhancement produced by gadobutrol compared with gadoteridol might
help in the detection and evaluation of breast lesions in the earlier phases.
Introduction
Gadobutrol (Gadovist, Bayer Healthcare
Pharmaceuticals) is a gadolinium-based contrast agent (GBCA) with a high
concentration and high T1 relaxation rate. The concentration of gadolinium in
gadobutrol is 1.0M (mol/L), which is 2-fold higher than the other GBCA. The T1
relaxation ratio of gadobutrol is 5.0 L/mmol/s at 3 Tesla, 37˚C in human plasma1. Several studies compared the
contrast effects of gadobutrol to representatives of the most commonly used 0.5
molar GBCAs for breast MRI2,3. Eva M et al.3 have demonstrated the higher
relative enhancement values of benign and malignant lesions in conventional dynamic
contrast enhanced (C-DCE) MRI compared with Gd-DOTA at 1.5T. The approach of
semi-quantitative and quantitative DCE MRI with higher temporal resolution
improves diagnostic accuracy for breast cancer compared to C-DCE MRI4–7. The aim of this study was to
compare the contrast effects of gadobutrol with gadoteridol (Prohance, Eisai)
in breast cancer at 3T using hybrid MRI protocol which combined C-DCE with ultrafast
DCE (UF-DCE).
Methods
Our institutional review board approved
this study with retrospective design. This study compared two
groups of patients with historical control design, not intra-individual
comparison. Thirty-seven patients pathologically diagnosed with invasive breast
cancer who underwent hybrid MRI protocol which is combined C-DCE
MRI with UF-DCE MRI were included. Thirty-seven patients were historically
separated in two groups. From December 2015 to March 2016, 14 patients
administered 0.2 mL/kg body weight of 0.5M gadoreridol. From April 2016 to June
2017, 23 patients administered 0.1 mL/kg body weight of 1.0M gadobutrol. All
breast MRI examinations were performed using a 3T system (MAGNETOM Skyra,
Siemens Healthcare GmbH, Erlangen, Germany) and an 18-channel breast coil. The hybrid
DCE-MRI protocol consisted of UF-DCE using the prototype sequence (from 13 s
before to 60 s after contrast injection), followed by C-DCE sequence of initial
phase (60-120 s), delayed phase (300-360 s) and high spatial resolution
post-contrast images. UF-DCE MRI was performed using
a prototypical non-fat-suppressed 3D volumetric interpolated breath-hold
examination (VIBE) sequence with the following imaging parameters: repetition
time (TR), 5.04 ms; echo time (TE), 2.46 ms; flip angle, 15°; slice thickness, 2.5
mm; 60 partitions; matrix size, 384 x 269; FOV, 360 x 360 mm; compressed sensing
acceleration, 16.5; and temporal resolution, 3.65 s. DCE-MRI was sequentially
acquired at 20 consecutive time points with a total acquisition time of 73 s. C-DCE using VIBE sequence
with fat suppression were performed with the following parameters: TR, 3.84 ms;
TE, 1.43 ms; FA, 15°; slice thickness, 1.0 mm; 144 partitions; matrix size, 384
x 346, FOV, 330 x 330 mm; orientation, axial.
Image analysis for UF-DCE and C-DCE MRI
were performed by measuring relative enhancement ratio, which is the percent
increase of MR signal intensity before and after the injection of GBCA. Regions of interest(ROI) were placed in the strongest enhanced lesion with
at least 10 pixels by experienced radiological technologist. The same ROI was
copied to the other dynamic series to measure signal intensity of ROI. In UF-DCE MRI, maximum
enhancement ratio (MER), maximum slope (MS), and arrival time (AT) were
measured (Figure 1-a). In C-DCE MRI, wash-in ratio and wash-out ratio were
measured (Figure 1-b). If the curve type shows persistent kinetics pattern,
wash-out ratio is negative value. These analyses were
performed per each lesion.
Results
The tumor characteristics are summarized
in Figure 2. Results for kinetic parameters are
summarized in Figure 3. In UF-DCE MRI, MER and MS for gadobutrol were
significantly higher than that for gadoteridol (p<0.05). On the other hand,
AT showed no difference between gadobutrol and gadoteridol. In C-DCE MRI, there
were no difference for Wash-in ratio and Wash-out ratio. Example of UF-DCE and
C-DCE images with gadobutrol and gadoteridol were shown in Figure 4 and 5,
respectively.
Discussion
The 1.0 M chelate gadobutrol has been
shown to allow a narrower bolus profile, resulting in an increased, more
compact intravascular concentration and, consecutively a sharper bolus peak
during first pass compared with 0.5 M agents 8–10. This might be the reason of significant
difference observed in only very early phase, MER and MS. Limitation of the
study is the lack of intra-individual control; Variation of breast cancer cases
in the two group may affect the results.
Conclusion
The present study demonstrated the higher
contrast enhanced effect of gadobutrol compared with gadoteridol for breast
cancer especially in UF-DCE at 3T MRI. The slightly higher peak relative
enhancement produced by gadobutrol compared with gadoteridol might help in the
detection and evaluation of breast lesions in the earlier phases.Acknowledgements
This work was partly supported by
Grant-in-Aid for Scientific Researchon Innovative Areas “Initiative for
High-Dimensional Data-DrivenScience through Deepening of Sparse Modeling”, MEXT
grant numbers25120002, 25120008.References
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