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Review
Application of different DWI models in the diagnosis of ovarian tumors
QIAN Luodan  WU Hui  GAO Yang  NIU Guangming 

Cite this article as: Qian LD, Wu H, Gao Y, et al. Application of different DWI models in the diagnosis of ovarian tumors. Chin J Magn Reson Imaging, 2019, 10(10): 797-800. DOI:10.12015/issn.1674-8034.2019.10.017.


[Abstract] Ovarian cancer is the most fatal malignant tumor in female genitalia. The accuracy of diagnosis and differential diagnosis is the key to treatment and prognosis. Conventional MRI is mainly limited to morphological imaging. Different diffusion models include diffusion weighted imaging (DWI), intravoxel incoherent motion (IVIM), stretch index model (Stretched), and diffusion kurtosis Imaging (DKI) can non-invasively improve the accuracy of ovarian tumor diagnosis and differential diagnosis through a series of quantitative and semi-quantitative data analysis, and help clinicians to develop more scientific and reasonable treatment plans. This article reviews the application and value of functional magnetic resonance imaging (fMRI) such as DWI models in ovarian tumors in recent years.
[Keywords] ovarian neoplasms;magnetic resonance imaging;diffusion weighted imaging;intravoxel incoherent motion;diffusion kurtosis imaging

QIAN Luodan Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Huhhot 010000, China

WU Hui* Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Huhhot 010000, China

GAO Yang Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Huhhot 010000, China

NIU Guangming* Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Huhhot 010000, China

*Corresponding to: Niu GM, E-mail: Cjr.niuguangming@vip.163.com *Wu H, E-mail: terrywuhui @sina.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  This work was part of Inner Mongolia Natural Science Foundation No.2017MS (LH)0873
Received  2019-03-04
DOI: 10.12015/issn.1674-8034.2019.10.017
Cite this article as: Qian LD, Wu H, Gao Y, et al. Application of different DWI models in the diagnosis of ovarian tumors. Chin J Magn Reson Imaging, 2019, 10(10): 797-800. DOI:10.12015/issn.1674-8034.2019.10.017.

[1]
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA Cancer J Clin, 2018, 60(5): 277-300.
[2]
Mathieu KB, Bedi DG, Thrower SL, et al. Screening for ovarian cancer: imaging challenges and opportunities for improvement. Ultrasound Obstet Gynecol, 2017, 51(3): 293-303.
[3]
Forstner R, Thomassin-Naggara I, Cunha TM, et al. ESUR recommendations for MR imaging of the sonographically indeterminate adnexal mass: an update. Eur Radiol, 2017, 27(6): 2248-2257.
[4]
Addley H, Moyle P, Freeman S. Diffusion-weighted imaging in gynaecological malignancy. Clin Radiol, 2017, 72(11): 981-990.
[5]
Zhao SH, Qiang JW, Zhang GF, et al. Diffusion-weighted MR imaging for differentiating borderline from malignant epithelial tumours of the ovary: pathological correlation. Eur Radiol, 2014, 24(9): 2292-2299.
[6]
Denewar FA, Takeuchi M, Urano M, et al. Multiparametric MRI for differentiation of borderline ovarian tumors from stage I malignant epithelial ovarian tumors using multivariate logistic regression analysis. Eur J Radiol, 2017, 91: 116-123.
[7]
王丰,周延,王玉湘,等. MR扩散加权成像单指数模型及体素内不相干运动模型参数直方图对上皮性卵巢癌分型的价值.中华放射学杂志, 2016, 50(10): 768-773.
[8]
Oh JW, Rha SE, Oh SN, et al. Diffusion-weighted MRI of epithelial ovarian cancers: Correlation of apparent diffusion coefficient values with histologic grade and surgical stage. Eur J Radiol, 2015, 84(4): 590-595.
[9]
Wang F, Wang Y, Zhou Y, et al. Apparent diffusion coefficient histogram analysis for assessing tumor staging and detection of lymph node metastasis in epithelial ovarian cancer: Correlation with p53 and Ki-67 expression. Mol Imaging Biol, 2019, 21(4): 731-739.
[10]
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. Invest Radiol, 2011, 46(5): 285-291.
[11]
Le Bihan D, Breton E, Lallemand D, et al. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology, 1988, 168(2): 497-505.
[12]
Morita S, Kojima S, Hirata M, et al. Perfusion fraction of diffusion-weighted MRI for predicting the presence of blood supply in ovarian masses. J Magn Reson Imaging, 2011, 34(5): 1131-1136.
[13]
Williams E, Martin S, Moss R, et al. Co-expression of VEGF and CA9 in ovarian high-grade serous carcinoma and relationship to survival. Virchows Archiv, 2012, 461(1): 33-39.
[14]
孟楠,翟战胜,殷慧佳,等.单指数、双指数及拉伸指数模型扩散加权成像在卵巢良恶性肿瘤鉴别中的价值.放射学实践, 2018, 33(7): 713-716.
[15]
申洋,周延,何为,等.基于IVIM模型的扩散加权成像和动态增强核磁共振在卵巢肿瘤良恶性鉴别中的应用价值.临床放射学杂志, 2016, 35(3): 410-414.
[16]
Zhang SX, Jia QJ, Zhang ZP, et al. Intravoxel incoherent motion MRI: emerging applications for nasopharyngeal carcinoma at the primary site. Eur Radiol, 2014, 24(8): 1998-2004.
[17]
Lemke A, Laun FB, Klauss M, et al. Differentiation of pancreas carcinoma from healthy pancreatic tissue using multiple b-values: comparison of apparent diffusion coefficient and intravoxel incoherent motion derived parameters. Invest Radiol, 2009, 44(12): 769-775.
[18]
Winfield JM, Desouza NM, Priest AN, et al. Modelling DW-MRI data from primary and metastatic ovarian tumours. Eur Radioly, 2015, 25(7): 2033-2040.
[19]
Bennett KM, Schmainda KM, Bennett R, et al. Characterization of continuously distributed cortical water diffusion rates with a stretched-exponential model. Magn Reson Med, 2003, 50(4): 727-734.
[20]
Kwee TC, Galbán CJ, Tsien C, et al. Comparison of apparent diffusion coefficients and distributed diffusion coefficients in high-grade gliomas. J Magn Reson Imaging, 2010, 31(3): 531-537.
[21]
Chen X, Jiang J, Shen N, et al. Stretched-exponential model diffusion-weighted imaging as a potential imaging marker in preoperative grading and assessment of proliferative activity of gliomas. Am J Transl Res, 2018, 10(8): 2659-2668.
[22]
陈雨菲,何为,刘剑羽.拉伸指数和单指数模型DWI应用于前列腺癌和前列腺增生鉴别诊断的对照.磁共振成像, 2019, 10(3): 206-211.
[23]
Wang F, Wang Y, Zhou Y, et al. Comparison between types I and II epithelial ovarian cancer using histogram analysis of monoexponential, biexponential, and stretched-exponential diffusion models. J Magn Reson Imaging, 2017, 46(6): 1797-1809.
[24]
任继鹏,孟楠,周凤梅,等.多模型体素不相干运动联合血清CA125对卵巢肿瘤的诊断价值.中国CT和MRI杂志, 2018, 16(3): 79-82.
[25]
Jensen JH, Helpern JA, Ramani A, et al. Diffusional kurtosis imaging: The quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med, 2005, 53(6): 1432-1440.
[26]
Li HM, Zhao SH, Qiang JW, et al. Diffusion kurtosis imaging for differentiating borderline from malignant epithelial ovarian tumors: A correlation with Ki-67 expression. J Magn Reson Imaging, 2017, 46(5): 1499-1506.
[27]
成芳,所世腾,康记文,等. MR扩散峰度成像在浸润性乳腺癌分级及与预后因素的相关性应用研究.磁共振成像, 2017, 8(3): 164-169.
[28]
谢辉,吴光耀.扩散峰度成像及直方图分析在直肠癌术前T分期中的应用价值.磁共振成像, 2018, 9(3): 208-213.
[29]
李海明,赵书会,强金伟,等.多b值DWI鉴别诊断交界性与恶性上皮性卵巢肿瘤.中国医学影像技术, 2018, 34(7): 1050-1054.
[30]
Chen YB, Ren W, Zheng DC, et al. Diffusion kurtosis imaging predicts neo adjuvant chemotherapy responses within 4 days in advanced nasopharyngeal carcinoma patients. J Magn Reson Imaging, 2015, 42(5): 1354-1361.
[31]
Goshima S, Kanematsu M, Noda Y, et al. Diffusion kurtosis imaging to assess response to treatment in hypervascular hepatocellular carcinoma. AJR Am J Roentgenol, 2015, 204(5): DOI: .
[32]
Bai Y, Lin Y, Tian J, et al. Grading of gliomas by using monoexponential, biexponential, and stretched exponential diffusion-weighted MR imaging and diffusion kurtosis MR imaging. Radiology, 2016, 278(2): 496-504.
[33]
Wu CJ, Zhang YD, Bao ML, et al. Diffusion kurtosis imaging helps to predict upgrading in biopsy-proven prostate cancer with a gleason score of 6. AJR Am J Roentgenol, 2017, 209(5): 1-7.

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