The value of diffusion-weighted imaging based on monoexponential and biexponential model in#br# differentiating benign and malignant hepatic hypervascular lesions
YING Ming-liang, SHU Jin-er, PAN Jiang-feng, LU Jin-hua, PAN Yong-hao, JIANG Yang, FU Jian-fei
Jinhua Hospital, Zhejiang University, Jinhua Zhejiang 321000, China
Abstract:Objective: To investigate the value of monoexponential and biexponential model diffusion-weighted imaging in differentiating benign and malignant hepatic hypervascular lesions. Materials and Methods: Forty-four patients(48 lesions) with pathologically or clinically confirmed hepatic hypervascular lesions, were analyzed retrospectively and categorized into benign and malignant groups. All patients underwent routine MR scan and parallel multi b value DWI(b=0, 50, 100, 150, 200, 400, 600, 800, 1 000, 1 200 s/mm2) to obtain the monoexponential modeling value ADC(b=0, 800 s/mm2) and IVIM parameters of biexponential modeling: fast diffusion coefficient Dfast value, slow diffusion coefficient Dslow value and fast diffusion component percentage of F value. Independent two-samples t test was used to compare ADC value, Dfast value, Dslow value and F value between hypervascular benign and malignant groups. Receiver operating characteristic(ROC) curve was used to evaluate those parameters in differentiating benign and malignant lesions and identifying hyper- or hypovascular. Results: The ADC, Dslow value of benign group((1.75±0.68)×10-3 mm2/s, (1.61±0.39)×10-3 mm2/s) were statistically higher than those of malignant group((1.21±0.21)×10-3 mm2/s, (0.99±0.19)×10-3 mm2/s). The Dfast and F values of benign group((30.93±20.00)×10-3 mm2/s, (34.01±11.48)%) were higher than those of malignant group((28.56±18.56)×10-3 mm2/s, (31.37±9.86)%) with no statistical significance. The sensitivity, specificity, accuracy and the area under ROC in differentiating benign and malignant hypervascular lesions were 80.76%, 86.36%, 83.33%, 0.875; 92.30%, 95.45%, 95.83%, 0.945; 46.12%, 72.72%, 60.87%, 0.534, 53.84%, 68.18%, 60.41%, 0.545 by using a threshold ADC, Dslow, Dfast, F values of 1.35×10-3 mm2/s, 1.25×10-3 mm2/s, 20.61×10-3 mm2/s, 32.36% respectively. Conclusion: The ADC value obtained with monoexponential modeling and Dslow obtained with biexponential modeling are useful in differentiating benign and malignant hepatic hypervascular lesions, and Dslow has the highest diagnostic efficacy.
应明亮,舒锦尔,潘江峰,卢金花,潘勇浩,蒋 杨,傅建飞. 单、双指数模型扩散加权成像鉴别诊断肝脏富血供病变良恶性的价值[J]. 中国临床医学影像杂志, 2017, 28(1): 39-43.
YING Ming-liang, SHU Jin-er, PAN Jiang-feng, LU Jin-hua, PAN Yong-hao, JIANG Yang, FU Jian-fei. The value of diffusion-weighted imaging based on monoexponential and biexponential model in#br# differentiating benign and malignant hepatic hypervascular lesions. JOURNAL OF CHINA MEDICAL IMAGING, 2017, 28(1): 39-43.
[1]Yoon JH, Lee JM, Yu MH, et al. Evaluation of hepatic focal lesions using diffusion-weighted MR imaging: comparison of apparent diffusion coefficient and intravoxel incoherent motion-derived parameters[J]. J Magn Reson Imaging, 2014, 39(2): 276-285.
[2]Koh DM, Collins DJ, Orton MR. Intravoxel incoherent motion in body diffusion-weighted MRI: reality and challenges[J]. AJR, 2011, 196(6): 1351-1361.
[3]Moteki T, Horikoshi H. Evaluation of hepatic lesions and hepatic parenchyma using diffusion-weighted echo-planar MR with three values of gradient b-factor[J]. J Magn Reson Imaging, 2006, 24(3): 637-645.
[4]鲁果果,高雪梅,程敬亮,等. 单、双指数模型扩散加权成像鉴别诊断肝脏良、恶性肿瘤的价值[J]. 中华放射学杂志,2015,49(1):47-51.
[5]Doblas S, Wagner M, Leitao HS, et al. Determination of malignancy and characterization of hepatic tumor type with diffusion-weighted magnetic resonance imaging: comparison of apparent diffusion coefficient and intravoxel incoherent motion-derived measurements[J]. Invest Radiol, 2013, 48(10): 722-728.
[6]Ichikawa S, Motosugi U, Ichikawa T, et al. Intravoxel incoherent motion imaging of focal hepatic lesions[J]. J Magn Reson Imaging, 2013, 37(6): 1371-1376.
[7]Le Bihan D, Breton E, Lallemand D, et al. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders[J]. Radiology, 1986, 161(2): 401-407.
[8]Federau C, Meuli R, O’Brien K, et al. Perfusion measurement in brain gliomas with intravoxel incoherent motion MRI[J]. Am J Neuroradiol, 2014, 35(2): 256-262.
[9]Wirestam R, Borg M, Brockstedt S, et al. Perfusion-related parameters in intravoxel incoherent motion MR imaging compared with CBV and CBF measured by dynamic susceptibility-contrast MR technique[J]. Acta Radiol, 2001, 42(2): 123-128.
[10]Sumi M, Van Cauteren M, Sumi T, et al. Salivary gland tumors: use of intravoxel incoherent motion MR imaging for assessment of diffusion and perfusion for the differentiation of benign from malignant tumors[J]. Radiology, 2012, 263(3): 770-777.
[11]Sigmund EE, Cho GY, Kim S, et al. Intravoxel incoherent motion imaging of tumor microenvironment in locally advanced breast cancer[J]. Magn Reson Med, 2011, 65(5): 1437-1447.
[12]Chandarana H, Lee VS, Hecht E, et al. Comparison of biexponential and monoexponential model of diffusion weighted imaging in evaluation of renal lesions: preliminary experience[J]. Invest Radiol, 2011, 46(5): 285-291.
[13]鲁果果,王斯嘉,高雪梅,等. 单双指数模型扩散加权成像在肝脏病变评价中的应用进展[J]. 中华肝脏病杂志,2015,23(7):557-560.
[14]Pang Y, Turkbey B, Bernardo M, et al. Intravoxel incoherent motion MR imaging for prostate cancer: an evaluation of perfusion fraction and diffusion coefficient derived from different b-value combinations[J]. Magn Reson Med, 2013, 69(2): 553-562.