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Validation of goose liver fat measurement by CSE-MRI with biochemical extraction as reference
Li Xu1, Yangyang Duanmu1, Xiaoqi Wang2, Glen M Blake3, Peng Wang4, Manling Zhang5, Chao Wang6, and Xiaoguang Cheng1

1Department of Radiology, Beijing Jishuitan Hospital, Beijing, China, 2Philips Healthcare, Beijing, China, 3Biomedical Engineering Department, King’s College London, London, United Kingdom, 4Department of Pathology, Capital Medical University Affiliated Beijing Ditan Hospital, Beijing, China, 5China national food & safety supervision and inspection center, Beijing, China, 6Department of Statistics, Beijing Jishuitan Hospital, Beijing, China

Synopsis

This study aimed to validate chemical shift encoded magnetic resonance imaging (CSE-MRI) to assess hepatic steatosis. Twenty-two geese with a wide range of hepatic steatosis were collected, and proton density fat fraction by MRI (MRI-PDFF), biochemical triglyceride content, and histology were performed within the left lobe, upper and lower half of the right lobe of the geese livers. MRI correlated highly with chemical extraction (r = 0.949 (p < 0.001)). Chemically extracted triglyceride was accurately predicted by MRI-PDFF (Y = -1.8 + 0.773﹡X). In conclusion, CSE-MRI measurement of goose liver fat was accurate and reliable compared with biochemical measurement.

INTRODUCTION

Non-alcoholic fatty liver disease (NAFLD) represents a spectrum of disorders characterized by the accumulation of fat in liver, and NAFLD is the most common etiology of chronic liver disease (1-4). Chemical shift encoded MRI (CSE-MRI) can discriminate between fat and water spins based on their different resonance frequencies, and multi-echo CSE-MRI techniques has been validated with excellent correlation with proton magnetic resonance spectroscopy (1H-MRS) and histological methods (5-10). The purpose of the present study was to validate the quantification of liver fat content by CSE-MRI on a group of geese, using biochemical extracted Triglyceride as the reference.

METHODS

Twenty-two geese with a wide range of hepatic steatosis were collected. CSE-MRI examinations (mDixon quant) were performed on a 3T MRI unit (Ingenia 3.0T TX, Philips, Best, the Netherlands) for all geese, and then liver of each goose was removed and samples were taken from the left lobe, upper and lower half of the right lobe for biochemical measurement and histology (Figure. 1). Triglyceride content (g) of the dry samples were determined by Soxhlet extraction (11), and the triglyceride mass percent of the goose liver were then obtained using the triglyceride content (g) and the wet weight (g) of the sample. According to the percentage of cells affected by fat vacuoles, the histological results were interpreted as: grade 0 for less than 5%, grade 1 for 5–30%, grade 2 for 31–50%, grade 3 for 51–75%, and grade 4 for more than 75% (12). Fat percentages by proton density fat fraction by MRI (MRI-PDFF) were measured within the sample regions of biochemical measurement. The intra-observer agreement of MRI-PDFF measurements was assessed using two ROI measurements by the same radiologist with an interval of one month. The accuracy of MRI-PDFF measurement was assessed through Spearman correlation coefficient (r) and Passing and Bablok regression equation using biochemical measurement as the gold standard. To detect the variability of fat distribution, we compare the values of the three ROIs derived from the same goose liver, using mixed model repeated measurements analysis.

RESULTS

Gross visual evaluation demonstrated the wide range of hepatic steatosis of the geese livers, and these differences of hepatic steatosis were clearly observed by MRI-PDFF and histology (Figure. 2). The mean value (± standard deviation (SD), range) of biochemical triglyceride mass percentage and MRI-PDFF was 24.13% (± 21.1%, 0.04–58.7%) and 33.81% (± 27.53%, 0.88–79.41%), respectively. Bland-Altman analysis demonstrated a very high intra-observer agreement for MRI-PDFF ROI measurement, the intra-class correlation coefficient of was 0.998 (p < 0.001) (Figure. 3). High correlation was detected between MRI-PDFF and triglyceride mass percentage (r = 0.949, p < 0.001). Passing and Bablok regression indicated that triglyceride mass percentage can be predicted by MRI-PDFF (Figure. 4). The mean value of difference between MRI-PDFF and triglyceride mass percentage was 9.68% (95%CI: -5.28–24.63%, p < 0.001). Figure 5 showed the distribution of triglyceride percentage at each division of histological grading. The biochemical result of each group defined by histology was 0.04-6.95% for grade 0, 2.69-7.09% for grade 1, 5.08-9.86% for grade 2, 11.39-25.85% for grade 3, and 10.2-60.54% for grade 4, respectively. No statistically significant differences of fat percentage among the three sampled regions were detected by MRI-PDFF (p = 0.995), biochemical extraction (p = 0.998), or histological grading (p = 0.416).

DISCUSSION

In previous studies, the correlation coefficient between MRI measured liver fat content and chemically estimated liver fat varied from 0.74 to 0.96 (13-17). Our study demonstrated comparable results regarding to the correlation coefficient between MRI and biochemical extraction (0.949 vs. 0.74–0.96). The MR mDixon-quant technique is complicated and the results can be influenced by many factors. First, MRI-PDFF measures the ratio of number of hydrogen protons in fat comparing to the number in both fat and water, while biochemical extraction measures the triglyceride content in the liver. Another issue is that mDixon MRI (using relatively longer TE than in solid state physics spectroscopy methods) cannot measure the signal from hydrogen protons that are closely connected to large proteins, or in a solid or semi-solid state. The “dry” mass of tissue could account for 3~15% of total mass. These factors generate a systematic shift in the final output, and may explain the slight bigger MRI-PDFF output than biochemical extraction observed in this study.

CONCLUSION

CSE-MRI methods can measure liver fat content accurately and reliably in comparison with chemical methods, and these results justify the use of CSE-MRI in the clinical setting to assess and monitor liver steatosis.

Acknowledgements

We thank our study participants for contributing their time and efforts. We wish to thankPhilips Healthcare for its technical supports.

References

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Figures

Fig. 1: Sampling regions of goose liver for biochemical measurement and pathology grading. (A) The liver is divided into left (L) and right lobes, and the right lobe is cut into upper (RS) and lower (RI) halves. Samples (yellow box) of approximately 60×30×30 mm in size were taken in the middle of each lobe. (B) A small piece of tissue (①) was taken from each sample for pathology grading, and the remaining tissue (②) was used for biochemical measurement.


Fig. 2: Samples of geese livers with different amount of fat content, ranging from healthy liver to liver with severe steatosis. (A) Photographs demonstrate increased size and yellow hue with increasing steatosis (left to right). (B). MRI-PDFF map detected fat fraction of sample ①, ②, ③, and ④ was 2.78%, 12.75%, 34.73%, and 43.08%, respectively. (C) Histological grade was grade 0, grade 2, grade 3, and grade 4, respectively. Triglyceride mass percentage by chemical extraction was 0.25%, 9.2%, 23.36%, and 33.84%, respectively.

Fig. 3: Bland-Altman analysis of Intra-observer agreement for repeated ROI measurements. MRI-PDFF showed a high intra-observer agreement, mean difference between the two ROI measurements by the same observer was 0.05% (95% limits of agreement: -3.73%–3.82%, p = 0.991).

Fig. 4: Passing and Bablok regression of biochemical extraction with MRI. MRI-PDFF can predict biochemical triglyceride mass percentage with the equation: Y = -1.8+ 0.773﹡X (r = 0.949).

Fig. 5: Plots of biochemical triglyceride volume percentage grouped by histological grading. The mean chemical result of each group defined by histology was 1.55% for grade 0, 4.75% for grade 1, 6.88% for grade 2, 21.83% for grade 3, and 42.72% for grade 4, respectively.

Proc. Intl. Soc. Mag. Reson. Med. 26 (2018)
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