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Research progress on the application value of apparent diffusion coefficient of magnetic resonance imaging in the diagnosis and treatment of breast cancer
ZHOU Yan  ZHU Xu’na  LIU Lidong 

Cite this article as: Zhou Y, Zhu XN, Liu LD. Research progress on the application value of apparent diffusion coefficient of magnetic resonance imaging in the diagnosis and treatment of breast cancer[J]. Chin J Magn Reson Imaging, 2021, 12(6): 111-113, 117. DOI:10.12015/issn.1674-8034.2021.06.023.


[Abstract] Diffusion weighted imaging (DWI) is one of the common functional imaging techniques in the diagnosis of breast cancer. It has the characteristics of non-invasive, visible and accurate, and also simple operation, no injection of contrast agent and good contrast. Apparent diffusion coefficient (ADC), as a quantitative parameter of DWI, can assess the biological characteristics of tumor, such as tissue cells quantity, water content, cell membrane integrity and the degree of blood vessels, etc, there are important clinical value for the diagnosis of clinical work to provide quantitative reference information in the early detection, diagnosis and treatment of breast disease. This review is about to introduce the application value of apparent diffusion coefficient in the diagnosis, pathological grade, molecular subtype, evaluation of chemotherapy, peritumoral invasion and axillary lymph node metastasis of breast cancer.
[Keywords] breast cancer；apparent diffusion coefficient；diffusion weighted imaging；magnetic resonance imaging

ZHOU Yan   ZHU Xu’na   LIU Lidong*  

Department of Radiology, the Affiliated Cancer Hospital of Guangxi Medical University, Guangxi Key Clinical Specialty (Department of Medical Imaging), Dominant Cultivation Discipline of Guangxi Medical University Cancer Hospital (Department of Medical Imaging), Nanning 530021, China

Liu LD, E-mail: evanlld@sina.com

Conflicts of interest   None.

This work was part of the Major Research and Development Project of Guangxi (No. Guike AB18126041); The Project Contract for Development and Popularization and Application of Appropriate Medical and Health Technology in Guangxi (No.S2019046); Guangxi Clinical Research Center for Medical Imaging Construction (No. Guike AD20238096).
Received  2020-12-16
Accepted  2021-01-28
DOI: 10.12015/issn.1674-8034.2021.06.023
Cite this article as: Zhou Y, Zhu XN, Liu LD. Research progress on the application value of apparent diffusion coefficient of magnetic resonance imaging in the diagnosis and treatment of breast cancer[J]. Chin J Magn Reson Imaging, 2021, 12(6): 111-113, 117. DOI:10.12015/issn.1674-8034.2021.06.023.

1
DeSantis CE, Ma J, Gaudet MM, et al. Breast cancer statistics, 2019[J]. CA Cancer J Clin, 2019, 69(6): 438-451. DOI: 10.3322/caac.21583.
2
Tseng J, Kyrillos A, Liederbach E, et al. Clinical accuracy of preoperative breast MRI for breast cancer[J]. J Surg Oncol, 2017, 115(8): 924-931. DOI: 10.1002/jso.24616.
3
Cheng C, Zhou SL, Zhou J, et al. Analysis of diagnostic value of DCE-MRI and DWI in breast cancer[J]. J Med Imaging, 2017, 27(11): 2122-2126.
4
Partridge SC, Nissan N, Rahbar H, et al. Diffusion-weighted breast MRI: Clinical applications and emerging techniques[J]. J Magne Reson Imaging, 2017, 45(2): 337-355. DOI: 10.1002/jmri.25479.
5
Tamura T, Usui S, Murakami S, et al. Comparisons of multi b-value DWI signal analysis with pathological specimen of breast cancer[J]. Magne Reson Med, 2012, 68(3): 890-897. DOI: 10.1002/mrm.23277.
6
Yilmaz E, Sari O, Yilmaz A, et al. Diffusion-weighted imaging for the discrimination of benign and malignant breast masses; Utility of ADC and relative ADC[J]. J Belg Soc Radiol, 2018, 102(1): 24. DOI: 10.5334/jbsr.1258.
7
Bozkurt Bostan T, Koç G, Sezgin G, et al. Value of apparent diffusion coefficient values in differentiating malignant and benign breast lesions[J]. Balkan Med J, 2016, 33(3): 294-300. DOI: 10.5152/balkanmedj.2016.141007.
8
Zhang L, Han LX, Cao HX, et al. The research on distinguishing adenosis of the breast from ductal carcinoma by DWI and vibrant imaging at 3.0 T MR[J]. J Clin Radiol, 2017, 36(3): 342-346.
9
Surov A, Meyer HJ, Wienke A. Can apparent diffusion coefficient (ADC) distinguish breast cancer from benign breast findings? A meta-analysis based on 13 847 lesions[J]. BMC Cancer, 2019, 19(1): 955. DOI: 10.1186/s12885-019-6201-4.
10
Bickel H, Pinker K, Polanec S, et al. Diffusion-weighted imaging of breast lesions: Region-of-interest placement and different ADC parameters influence apparent diffusion coefficient values[J]. Eur Radiol, 2017, 27(5): 1883-1892. DOI: 10.1007/s00330-016-4564-3.
11
Xiao YB, Zhang X, Huang XX. Histological and molecular pathology classification in the pathologic diagnosis of breast cancer[J]. Pract J Cancer, 2015(3): 341-344.
12
Li WH, Xu L, Yang H, et al. Correlation between MRI apparent diffusion coefficient and histological grade of breast invasive ductal carcinoma[J]. Chin J Cancer Prevention Treatment.2015, 22(13): 1028-1031.
13
Zhao S, Shao G, Chen P, et al. Diagnostic performance of minimum apparent diffusion coefficient value in differentiating the invasive breast cancer and ductal carcinoma in situ[J]. J Cancer Res Ther, 2019, 15(4): 871-875. DOI: 10.4103/jcrt.JCRT_607_18.
14
Zhang W, Su DK, Luo NB, et al. Correlations between apparent diffusion coefficient values and histopathologic classification of breast invasive ductal carcinoma[J]. Chin J Magn Reson Imaging, 2015, 6(2): 131-135. DOI: 10.3969/j.issn.1674-8034.2015.02.010.
15
Horvat JV, Bernard-Davila B, Helbich TH, et al. Diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping as a quantitative imaging biomarker for prediction of immunohistochemical receptor status, proliferation rate, and molecular subtypes of breast cancer[J]. J Magn Reson Imaging, 2019, 50(3): 836-846. DOI: 10.1002/jmri.26697.
16
Meng L, Ma P. Apparent diffusion coefficient value measurements with diffusion magnetic resonance imaging correlated with the expression levels of estrogen and progesterone receptor in breast cancer: A meta-analysis[J]. J Cancer Res Ther, 2016, 12(1): 36-42. DOI: 10.4103/0973-1482.150418.
17
Zhang Y, Liu WH, Wang R, et al. Correlation of apparent diffusion coefficient for different b values with prognostic factors and subtypes of breast carcinoma[J]. Chin J Magn Reson Imaging, 2018, 9(6): 422-426.
18
Xie ZY, Ma YC, Yao M, et al. Correlation between apparent diffusion coefficient value on diffusion weighted imaging and molecular subtypes of breast invasive ductal carcinomas[J]. Chin J Med Imaging, 2016, 24(4): 277-280.
19
Santamaría G, Bargalló X, Fernández PL, et al. Neoadjuvant systemic therapy in breast cancer: Association of contrast-enhanced MRI findings, diffusion-weighted imaging findings, and tumor subtype with tumor response[J]. Radiology, 2017, 283: 663-672.
20
Ramírez-Galván YA, Cardona-Huerta S, Elizondo-Riojas G, et al. Apparent diffusion coefficient value to evaluate tumor response after neoadjuvant chemotherapy in patients with breast cancer[J]. Acad Radiol, 2018, 25(2): 179-187. DOI: 10.1016/j.acra.2017.08.009.
21
Liu Y, Ding YY, Li ZL, et al. Value of baseline apparent diffusion coefficient in predicting the response of neoadjuvant chemotherapy in different subtypes of breast cancer[J]. Radiol Pract, 2016, 31(11): 1057-1061.
22
Shen L, Tang F, Lin Y, et al. Clinical evaluation of significance of ADC in diffusion-weighted MR imaging for locally advanced breast cancer after neo-adjuvant chemotherapy and its correlation with pathological response[J]. J Clin Exper Med, 2017, 16(7): 717-720.
23
Negrão EMS, Bitencourt AGV, de Souza JA, et al. Accuracy of breast magnetic resonance imaging in evaluating the response to neoadjuvant chemotherapy: a study of 310 cases at a cancer center[J]. Radiol Bras, 2019, 52(5): 299-304. DOI: 10.1590/0100-3984.2018.0149.
24
Shin JK, Kim JY. Dynamic contrast-enhanced and diffusion-weighted MRI of estrogen receptor-positive invasive breast cancers: Associations between quantitative MR parameters and Ki-67 proliferation status[J]. J Magn Reson Imaging, 2017, 45(1): 94-102. DOI: 10.1002/jmri.25348.
25
Mori N, Ota H, Mugikura S, et al. Luminal-type breast cancer: correlation of apparent diffusion coefficients with the Ki-67 labeling index[J]. Radiology, 2015, 274(1): 66-73. DOI: 10.1148/radiol.14140283.
26
Luo N, Ji Y, Huang X, et al. Changes in apparent diffusion coefficient as surrogate marker for changes in Ki-67 index due to neoadjuvant chemotherapy in patients with invasive breast cancer[J]. Acad Radiol, 2019, 26(10): 1352-1357. DOI: 10.1016/j.acra.2019.01.007.
27
Guvenc I, Whitman GJ, Liu P, et al. Diffusion-weighted MR imaging increases diagnostic accuracy of breast MR imaging for predicting axillary metastases in breast cancer patients[J]. Breast J, 2019, 25(1): 47-55. DOI: 10.1111/tbj.13151.
28
Luo N, Su D, Jin G, et al. Apparent diffusion coefficient ratio between axillary lymph node with primary tumor to detect nodal metastasis in breast cancer patients[J]. J Magn Reson Imaging, 2013, 38(4): 824-828. DOI: 10.1002/jmri.24031.
29
Igarashi T, Furube H, Ashida H, et al. Breast MRI for prediction of lymphovascular invasion in breast cancer patients with clinically negative axillary lymph nodes[J]. Eur J Radiol, 2018, 107: 111-118. DOI: 10.1016/j.ejrad.2018.08.024.
30
Choi EJ, Youk JH, Choi H, et al. Dynamic contrast-enhanced and diffusion-weighted MRI of invasive breast cancer for the prediction of sentinel lymph node status[J]. J Magn Reson Imaging, 2020, 51(2): 615-626. DOI: 10.1002/jmri.26865.
31
Shin HJ, Kim SH, Lee HJ, et al. Tumor apparent diffusion coefficient as an imaging biomarker to predict tumor aggressiveness in patients with estrogen-receptor-positive breast cancer[J]. NMR Biomed, 2016, 29(8): 1070-1078. DOI: 10.1002/nbm.3571.
32
Fan M, Yuan W, Zhao W, et al. Joint prediction of breast cancer histological grade and Ki-67 expression level based on DCE-MRI and DWI radiomics[J]. IEEE J Biomed Health Inform, 2020, 24(6): 1632-1642. DOI: 10.1109/JBHI.2019.2956351.
33
Fan M, He T, Zhang P, et al. Diffusion-weighted imaging features of breast tumours and the surrounding stroma reflect intrinsic heterogeneous characteristics of molecular subtypes in breast cancer[J]. NMR Biomed, 2018, 31(2): 101-102. DOI: 10.1002/nbm.3869.
34
Partridge SC, Zhang Z, Newitt DC, et al. Diffusion-weighted MRI findings predict pathologic response in neoadjuvant treatment of breast cancer: The ACRIN 6698 multicenter trial[J]. Radiology, 2018, 289(3): 618-627. DOI: 10.1148/radiol.2018180273.
35
Fan M, He T, Zhang P, et al. Heterogeneity of diffusion-weighted imaging in tumours and the surrounding stroma for prediction of Ki-67 proliferation status in breast cancer[J]. Sci Rep, 2017, 7(1): 2875. DOI: 10.1038/s41598-017-03122-z.
36
Dong Y, Feng Q, Yang W, et al. Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI[J]. Eur Radiol, 2018, 28(2): 582-591. DOI: 10.1007/s00330-017-5005-7.

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