Value of stretched exponential model diffusion-weighted imaging in diagnosis of advanced liver fibrosis_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

HU Fubi ¹ , HAN Xueying ² ,  YANG Ru ¹ , LIU Yunbo ¹ , ZHAO Canxin ¹

1. Department of Radiology, The First Affiliated Hospital of Chengdu Medical College, Chengdu 610500, P. R. China;
2. Department of Medical Imaging, Karamay Municipal People’s Hospital, Karamay, Xinjiang 834000, P. R. China;

Corresponding?author：

YANG Ru, Email: yangru0904@163.com

Keywords：

liver fibrosis; magnetic resonance imaging; diffusion-weighted imaging; stretched exponential model

DOI：

10.7507/1007-9424.202006135

Video：

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Objective To investigate the utility of stretched exponential model diffusion-weighted imaging (DWI) for diagnosing of advanced liver fibrosis.Methods The patients with chronic liver disease complicated with vary degrees of fibrosis confirmed by pathological examination underwent DWI using different b-values (0, 50, 600 s/mm²) at the First Affiliated Hospital of Chengdu Medical College from June 2015 to February 2020 were collected. In addition, patients who underwent upper abdominal MRI examination in the same hospital at the same time and had no liver disease or disease affecting liver function were collected as a control group. The apparent diffusion coefficient (ADC) was calculated by using a mono-exponential model. The distributed diffusion coefficient (DDC) and water molecular diffusion heterogeneity index (α) were calculated by using a stretched exponential model. The fibrosis stage was evaluated by using the Metavir scoring system. The ADC, DDC, and α among different fibrosis groups were compared. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic efficacy of these three quantitative parameters for advanced liver fibrosis.Results A total of 42 patients with chronic liver disease were collected in this study, including mild liver fibrosis (S1–S2, n=16) and advanced liver fibrosi (≥S3, n=24); 15 patients in the control group. The values of ADC, DDC, and α of the patients with mild liver fibrosis and advanced liver fibrosis were significantly lower than those of the control patients (P<0.05). The area under the ROC curve of ADC, DCC, and α in diagnosing liver fibrosis (≥S1) was 0.915, 0.974, and 0.835, respectively, which in diagnosing advanced liver fibrosis (≥S3) was 0.744, 0.869, and 0.758, respectively. However, further the area under ROC curve among these three metrics had no statistical differences (P>0.05).Conclusion DDC based on stretched exponential model is valuable for diagnosis of advanced liver fibrosis.

Citation： HU Fubi, HAN Xueying, YANG Ru, LIU Yunbo, ZHAO Canxin. Value of stretched exponential model diffusion-weighted imaging in diagnosis of advanced liver fibrosis. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2020, 27(11): 1431-1435. doi: 10.7507/1007-9424.202006135 Copy

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8.	中華醫學會肝病學分會, 中華醫學會消化病學分會, 中華醫學會感染病學分會. 肝纖維化診斷及治療共識 (2019 年). 實用肝臟病雜志, 2019, 22(6): 793-803.
9.	Rosenkrantz AB, Padhani AR, Chenevert TL, et al. Body diffusion kurtosis imaging: Basic principles, applications, and considerations for clinical practice. J Magn Reson Imaging, 2015, 42(5): 1190-1202.
10.	Wáng YXJ, Wang X, Wu P, et al. Topics on quantitative liver magnetic resonance imaging. Quant Imaging Med Surg, 2019, 9(11): 1840-1890.
11.	王科, 潘婷, 周欣, 等. 基于非高斯分布模型的擴散加權成像在體部疾病中的應用. 磁共振成像, 2016, 7(1): 71-76.
12.	Iima M, Le Bihan D. Clinical intravoxel incoherent motion and diffusion MR imaging: past, present, and future. Radiology, 2016, 278(1): 13-32.
13.	Taouli B, Koh DM. Diffusion-weighted MR imaging of the liver. Radiology, 2010, 254(1): 47-66.
14.	Ichikawa S, Motosugi U, Morisaka H, et al. MRI-based staging of hepatic fibrosis: Comparison of intravoxel incoherent motion diffusion-weighted imaging with magnetic resonance elastography. J Magn Reson Imaging, 2015, 42(1): 204-210.
15.	Jiang H, Chen J, Gao R, et al. Liver fibrosis staging with diffusion-weighted imaging: a systematic review and meta-analysis. Abdom Radiol (NY), 2017, 42(2): 490-501.
16.	Bennett KM, Schmainda KM, Bennett RT, et al. Characterization of continuously distributed cortical water diffusion rates with a stretched-exponential model. Magn Reson Med, 2003, 50(4): 727-734.
17.	Anderson SW, Barry B, Soto J, et al. Characterizing non-gaussian, high b-value diffusion in liver fibrosis: Stretched exponential and diffusional kurtosis modeling. J Magn Reson Imaging, 2014, 39(4): 827-834.
18.	胡根文, 全顯躍, 李欣明, 等. MR 單指數及拉伸指數模型擴散成像診斷大鼠肝纖維化病理分期的價值. 中國醫學影像學雜志, 2018, 26(8): 575-578.

1. Faria SC, Ganesan K, Mwangi I, et al. MR imaging of liver fibrosis: current state of the art. Radiographics, 2009, 29(6): 1615-1635.
2. Horowitz JM, Venkatesh SK, Ehman RL, et al. Evaluation of hepatic fibrosis: a review from the society of abdominal radiology disease focus panel. Abdom Radiol (NY), 2017, 42(8): 2037-2053.
3. Ponziani FR, Gasbarrini A, Pompili M. Use of liver imaging and biopsy in clinical practice. N Engl J Med, 2017, 377(23): 2295-2296.
4. Shiha G, Ibrahim A, Helmy A, et al. Asian-Pacific Association for the Study of the Liver (APASL) consensus guidelines on invasive and non-invasive assessment of hepatic fibrosis: a 2016 update. Hepatol Int, 2017, 11(1): 1-30.
5. 付芳芳, 王梅云, 史大鵬, 等. 多種模型 MRI 擴散加權成像評估慢性乙型病毒性肝炎肝纖維化程度的價值. 中華放射學雜志, 2018, 52(2): 113-118.
6. Seo N, Chung YE, Park YN, et al. Liver fibrosis: stretched exponential model outperforms mono-exponential and bi-exponential models of diffusion-weighted MRI. Eur Radiol, 2018, 28(7): 2812-2822.
7. 余英芳, 溫生寶. 應用磁共振 DWI 單指數、雙指數和拉伸指數模型參數評估慢性乙型肝炎患者肝纖維化程度效能研究. 實用肝臟病雜志, 2019, 22(4): 502-505.
8. 中華醫學會肝病學分會, 中華醫學會消化病學分會, 中華醫學會感染病學分會. 肝纖維化診斷及治療共識 (2019 年). 實用肝臟病雜志, 2019, 22(6): 793-803.
9. Rosenkrantz AB, Padhani AR, Chenevert TL, et al. Body diffusion kurtosis imaging: Basic principles, applications, and considerations for clinical practice. J Magn Reson Imaging, 2015, 42(5): 1190-1202.
10. Wáng YXJ, Wang X, Wu P, et al. Topics on quantitative liver magnetic resonance imaging. Quant Imaging Med Surg, 2019, 9(11): 1840-1890.
11. 王科, 潘婷, 周欣, 等. 基于非高斯分布模型的擴散加權成像在體部疾病中的應用. 磁共振成像, 2016, 7(1): 71-76.
12. Iima M, Le Bihan D. Clinical intravoxel incoherent motion and diffusion MR imaging: past, present, and future. Radiology, 2016, 278(1): 13-32.
13. Taouli B, Koh DM. Diffusion-weighted MR imaging of the liver. Radiology, 2010, 254(1): 47-66.
14. Ichikawa S, Motosugi U, Morisaka H, et al. MRI-based staging of hepatic fibrosis: Comparison of intravoxel incoherent motion diffusion-weighted imaging with magnetic resonance elastography. J Magn Reson Imaging, 2015, 42(1): 204-210.
15. Jiang H, Chen J, Gao R, et al. Liver fibrosis staging with diffusion-weighted imaging: a systematic review and meta-analysis. Abdom Radiol (NY), 2017, 42(2): 490-501.
16. Bennett KM, Schmainda KM, Bennett RT, et al. Characterization of continuously distributed cortical water diffusion rates with a stretched-exponential model. Magn Reson Med, 2003, 50(4): 727-734.
17. Anderson SW, Barry B, Soto J, et al. Characterizing non-gaussian, high b-value diffusion in liver fibrosis: Stretched exponential and diffusional kurtosis modeling. J Magn Reson Imaging, 2014, 39(4): 827-834.
18. 胡根文, 全顯躍, 李欣明, 等. MR 單指數及拉伸指數模型擴散成像診斷大鼠肝纖維化病理分期的價值. 中國醫學影像學雜志, 2018, 26(8): 575-578.

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Value of stretched exponential model diffusion-weighted imaging in diagnosis of advanced liver fibrosis

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