Share:
Share this content in WeChat
X
Review
Progress in the application of different DWI exponential models for breast lesions
LU Dong-mei  WANG Jia-mei  LIU Yu-lin  YANG Xiao-ping 

DOI:10.12015/issn.1674-8034.2018.04.014.


[Abstract] DWI is widely used in clinic because of its noninvasive, visual and quantitative reflection of tissue microstructure. With the development of DWI exponential model, including monoexponential model, biexponential model and stretched exponential model, scanning scheme optimization and development of post-processing function, it provides more imaging evidence for early detection, differential diagnosis, adjuvant therapy and predictive evaluation of breast cancer. The purpose of this article is to review the current status of the index models in the mammary gland, in order to improve the understanding of the value of DWI in the diagnosis of breast lesions.
[Keywords] Breast neoplasms;Diffusion weighted imaging;Monoexponetial model;Biexponetial model;Stretched-exponential model;Magnetic resonance imaging

LU Dong-mei Department of Radiology, the Second Clinical Hospital of Lanzhou University, Lanzhou 730000, China; Department of Imaging Diagnostic Center, Lanzhou General Hospital, Lanzhou 730050, China

WANG Jia-mei Department of Radiology, the Second Clinical Hospital of Lanzhou University, Lanzhou 730000, China; Department of Imaging Diagnostic Center, Lanzhou General Hospital, Lanzhou 730050, China

LIU Yu-lin Department of Radiology, the Second Clinical Hospital of Lanzhou University, Lanzhou 730000, China; Department of Imaging Diagnostic Center, Lanzhou General Hospital, Lanzhou 730050, China

YANG Xiao-ping* Department of Imaging Diagnostic Center, Lanzhou General Hospital, Lanzhou 730050, China

*Corresponding to: Yang XP, E-mail: lwyxp_zxl@sohu.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  This work was part of Gansu Province Natural Sciences Foundation No.1606RJZA160
Received  2017-12-26
Accepted  2018-01-17
DOI: 10.12015/issn.1674-8034.2018.04.014
DOI:10.12015/issn.1674-8034.2018.04.014.

[1]
Jin G, An N, Jacobs MA, et al. The role of parallel diffusion-weighted imaging and apparent diffusion coefficient (ADC) map values for evaluating breast lesions: preliminary results. Acad Radiol, 2010, 17(4): 456-463.
[2]
Partridge SC, Nissan N, Rahbar H, et al. Diffusion-weighted breast MRI: Clinical applications and emerging techniques. J Magn Reson Imaging, 2017, 45(2): 337-355.
[3]
Partridge SC, Amornsiripanitch N. DWI in the assessment of breast lesions. Top Magn Reson Imaging, 2017, 26(5): 201-209.
[4]
Razek AAKA, Gaballa G, Denewer A, et al. Invasive ductal carcinoma: correlation of apparent diffusion coefficient value with pathological prognostic factors. NMR Biomed, 2010, 23(6): 619-623.
[5]
Martincich L, Deantoni V, Bertotto I, et al. Correlations between diffusion-weighted imaging and breast cancer biomarkers. Eur Radiol, 2012, 22(7): 1519-1528.
[6]
Kim EJ, Kim SH, Park GE, et al. Histogram analysis of apparent diffusion coefficient at 3.0 T: Correlation with prognostic factors and subtypes of invasive ductal carcinoma. J Magn Reson Imaging, 2015, 42(6): 1666-1678.
[7]
Kawashima H, Miyati T, Ohno N, et al. Differentiation between luminal-A and luminal-B breast cancer using intravoxel incoherent motion and dynamic contrast-enhanced magnetic resonance imaging. Acad Radiol, 2017, 24(12): 1575-1581.
[8]
Dorrius MD, Dijkstra H, Oudkerk M, et al. Effect of b value and pre-admission of contrast on diagnostic accuracy of 1.5 T breast DWI: a systematic review and meta-analysis. Eur Radiol, 2014, 24(11): 2835-2847.
[9]
Chen W, Zhang J, Long D, et al. Optimization of intra-voxel incoherent motion measurement in diffusion-weighted imaging of breast cancer. J Appl Clin Med Phys, 2017, 18(3): 191-199.
[10]
Bokacheva L, Kaplan JB, Giri DD, et al. Intravoxel incoherent motion diffusion-weighted MRI at 3.0 T differentiates malignant breast lesions from benign lesions and breast parenchyma. J Magn Reson Imaging, 2014, 40(4): 813-823.
[11]
Borlinhas F. Intravoxel incoherent motion (IVIM) analysis of breast cancer lesions. European Congress of Radiology 2013, Vienna, 2013.
[12]
Iima M, Yano K, Kataoka M, et al. Quantitative non-Gaussian diffusion and intravoxel incoherent motion magnetic resonance imaging: differentiation of malignant and benign breast lesions. Invest Radiol, 2015, 50(4): 205-211.
[13]
Thompson AM, Moulder-Thompson SL. Neoadjuvant treatment of breast cancer. Ann Oncol, 2012, 23(Suppl 10): 231-236.
[14]
Cho GY, Moy L, Kim SG, et al. Evaluation of breast cancer using intravoxel incoherent motion (IVIM) histogram analysis: comparison with malignant status, histological subtype, and molecular prognostic factors. Eur Radiol, 2016, 26(8): 2547-2558.
[15]
Iima M, Le Bihan D. Clinical intravoxel incoherent motion and diffusion MR imang: past, present, and future. Radiology, 2015, 278(1): 13-32.
[16]
Che S, Zhao X, Yanghan OU, et al. Role of the intravoxel incoherent motion diffusion weighted imaging in the pre-treatment prediction and early response monitoring to neoadjuvant chemotherapy in locally advanced breast cancer. Medicine, 2016, 95(4): e2420.
[17]
车树楠,李静,欧阳汉,等.扩散加权成像体素内不相干运动模型参数与乳腺癌预后因素及分子亚型的相关性.中国医学影像技术, 2016, 32(3): 367-371.
[18]
Liu C, Wang K, Chan Q, et al. Intravoxel incoherent motion MR imaging for breast lesions: comparison and correlation with pharmacokinetic evaluation from dynamic contrast-enhanced MR imaging. Eur Radiol, 2016, 26(11): 3888-3898.
[19]
Bedair R, Priest AN, Patterson AJ, et al. Assessment of early treatment response to neoadjuvant chemotherapy in breast cancer using non-mono-exponential diffusion models: a feasibility study comparing the baseline and mid-treatment MRI examinations. Eur Radiol, 2017, 27(7): 2726-2736.
[20]
Liu C, Wang K, Li X, et al. Breast lesion characterization using whole-lesion histogram analysis with stretched-exponential diffusion model. J Magn Reson Imaging, 2017 DOI: . DOI: 10.1002/jmri.25904.
[21]
Suo S, Cheng F, Cao M, et al. Multiparametric diffusion-weighted imaging in breast lesions: Association with pathologic diagnosis and prognostic factors. J Magn Reson Imaging, 2017, 46(3): 740-750.
[22]
Mazaheri Y, Afaq A, Rowe DB, et al. Diffusion-weighted magnetic resonance imaging of the prostate: improved robustness with stretched exponential modeling. J Comput Assist Tomogr, 2012, 36(6): 695-703.
[23]
Panek R, Borri M, Orton M, et al. Evaluation of diffusion models in breast cancer. Med Phys, 2015, 42(8): 4833-4839.

PREV Research and progress of magnetic resonance imaging on coronary microembolization using animal models
NEXT Advances in application of IVIM-DWI in liver focal lesions
  



Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn