Jun Li1, Yi Li1, Yuan-Yuan Chen1, Xiao-Ying Wang1, Cai-Xia Fu2, Robert Grimm3, Ying Ding1, and Meng-Su Zeng1
1Zhongshan Hospital of Fudan University, Shanghai, China, 2Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China, 3Siemens Heathineers AG., Erlangen, Germany
Synopsis
Keywords: Quantitative Imaging, Liver, MRI, gadoxetic acid, hepatic insufficiency, indocyanine green
Motivation: Current clinical modalities still have several limitations for accurately predicting post-hepatectomy liver failure (PHLF).
Goal(s): To explore more effective non-invasive tools to quantitatively predict PHLF.
Approach: The performances of the hematological tests, the indocyanine green (ICG) clearance test and the newly-based albumin-bilirubin (ALBI) scoring system for predicting PHLF were compared with that of whole-liver histogram analysis on gadoxetic acid-enhanced T1 maps.
Results: Whole-liver histogram analysis on gadoxetic acid-enhanced T1 maps had a better performance than the ICG clearance test and ALBI scoring system. It also showed potential for stratifying preoperative liver function.
Impact: The histogram parameters extracted
from whole-liver regions of interest (ROI) on gadoxetic acid-enhanced T1 maps were
proved to be effective and non-invasive tools for assessing liver function.
Further accurate liver function assessment based on sectional histogram
analysis is promising.
Introduction
This study aimed to explore the
potential of whole-liver histogram analysis on gadoxetic acid-enhanced T1 maps for
predicting post-hepatectomy liver failure (PHLF) in patients who underwent
partial hepatectomy and to compare with the indocyanine green (ICG) clearance
test and the newly-based albumin-bilirubin (ALBI) scoring system.Methods
101 consecutive patients who
underwent gadoxetic acid-enhanced MRI examinations on a 1.5T MRI scanner (MAGNETOM
Aera, Siemens Healthineers, Erlangen, Germany) were retrospectively collected. The
inclusion criteria were as follows: 1) underwent liver resection; 2) underwent
gadoxetic acid-enhanced MRI including T1 mapping within four weeks before the
surgery; 3) had ICG clearance test and an albumin and bilirubin test before the
surgery; and 4) had an international normalized ratio (INR) and bilirubin
examination on or after postoperative day five. The exclusion criteria were: 1)
patients who had undergone non-resection treatment; 2) patients whose images
had severe respiratory motion artifacts. 37 patients were enrolled. T1 mapping
was performed before and 20 minutes (hepatobiliary phase, HBP) after a bolus
injection of gadoxetic acid (Primovist: Bayer Schering Pharma, Berlin, Germany)
using a volumetric interpolated breath-hold examination (VIBE) sequence with
flip-angles of 2° and 12°. Other parameters of T1 mapping were as follows:
repetition time/echo time, 4.36 msec/1.93 msec, matrix, 288 × 216, field of
view, 380 × 285 mm, 22.4 cm slab thickness with an interpolated 4.0-mm slice
thickness, and bandwidth, 400 Hz/pixel. In addition, a parallel imaging
technique (acceleration factor of 2) was performed with generalized
auto-calibrating partially parallel acquisition (GRAPPA). Quantitative T1 maps
were automatically reconstructed on a voxel-by-voxel basis after data
acquisition by the MapIt processing tool (Siemens Healthcare, Erlangen,
Germany). Whole-liver histogram analysis of T1 maps on pre-contrast (T1pre) and
HBP (T1HBP) were performed using the research application MR Multiparametric
Analysis software (Siemens Healthcare) (Figure 1). The differences between
patients with and without PHLF were compared for univariate analysis. A
multivariate binary logistic regression analysis was used to identify
independent predictors for PHLF. Pearson or Spearman analysis was used to
evaluate the correlation of histogram analysis-extracted parameters of the T1
map to the ICG test and ALBI scoring system. The diagnostic performance of each
parameter was tested via receiver operating characteristic (ROC) analysis. The
DeLong test [1] was used to compare the area
under the curve (AUC) for diagnostic accuracy. P < 0.05 was considered
statistically significant.Results
35.1% (13/37) of patients developed PHLF. T1pre mean, T1pre 95th percentile, T1HBP SD, T1HBP 95th percentile, T1HBP kurtosis, and ICG-R15 showed statistically significant differences between the PHLF and non-PHLF groups (all p < 0.05), whereas the ALBI scores showed no statistically significant differences between the two groups (p = 0.937) (Table 1). T1HBP SD showed the best diagnostic performance with an AUC of 0.785 (Figure 2 and Table 2). Multivariate analysis showed that a higher T1HBP 95th percentile was the independent predictor for PHLF (p < 0.05; OR = 1.014) (Table1). In addition, T1HBP mean and T1HBP median showed the best significant correlation to the ICG test and ALBI scoring systems.Discussion
Gadoxetic acid-enhanced MRI is
routinely used to evaluate the hepatic function of patients with focal liver
lesions before hepatectomy. The process of the histogram analysis, based on the
automatic liver segmentation provided by the MR Multiparametric Analysis
software was easier to implement and understand than the higher-order texture
analysis methods. We also found that the AUCs of T1HBP SD and T1HBP 95th
percentile were more significant than those of ICG-R15 and ALBI scores. Higher T1HBP
95th percentile was the independent predictor for PHLF. It demonstrated the
superiority of the histogram analysis based on gadoxetic acid-enhanced T1
mapping over the ICG clearance test and ALBI scoring system in differentiating
the PHLF risks. The results showed that T1HBP mean, T1HBP median, and T1HBP
95th percentile were significantly correlated with the ICG clearance test (Rho:
0.758 - 0.818, P < 0.0001), which suggested the potential of histogram
parameters for preoperative liver function stratification.Conclusion
Whole-liver
histogram analysis on the gadoxetic acid-enhanced T1 maps is a potential
candidate for preoperative prediction and risk stratification of PHLF, which
outperformed the ICG clearance test and ALBI scoring system. Acknowledgements
We
greatly appreciate the support from the National Science Foundation for Young
Scientists of China (Grant No. 81701682) and Shanghai Municipal Key Clinical
Specialty (shslczdzk03202). References
1. Elizabeth R. DeLong,
D.M.D., and and D.L. Clarke-Pearson, Comparing
the Areas Under Two or More Correlated Receiver Operating Characteristic
Curves: A Nonparametric Approach. Biometrics, 1988. 44: p. 837-845.