Application and development of multiparametric MRI in diagnosis and treatment of breast cancer

doi:10.3877/cma.j.issn.1674-0785.2020.11.004

Abstract

Abstract:

Multiparametric magnetic resonance imaging (MRI) plays an important role in the diagnosis and treatment of breast cancer. With the development of breast MRI, more quantitative indicators from functional sequences have emerged to facilitate breast cancer screening, diagnosis, and evaluation of treatment and prognosis. This paper will review the requirements and protocol of multiparametric breast MRI, indications for clinical examination, and new promising innovations in breast MRI.

Key words: Magnetic resonance imaging, Breast cancer, Diagnosis and treatment

Yuan Jiang, Naishan Qin. Application and development of multiparametric MRI in diagnosis and treatment of breast cancer[J]. Chinese Journal of Clinicians(Electronic Edition), 2020, 14(11): 872-876.

/ / Recommend

Add to citation manager EndNote|Ris|BibTeX

URL: https://zhlcyszz.cma-cmc.com.cn/EN/10.3877/cma.j.issn.1674-0785.2020.11.004

https://zhlcyszz.cma-cmc.com.cn/EN/Y2020/V14/I11/872

References 53

1	Mann RM, Balleyguier C, Baltzer PA, et al. Breast MRI: EUSOBI recommendations for women's information [J]. Eur Radiol, 2015, 25(12): 3669-3678.
2	Sardanelli F, Boetes C, Borisch B, et al. Magnetic resonance imaging of the breast: recommendations from the EUSOMA working group [J]. Eur J Cancer, 2010, 46(8): 1296-1316.
3	Morrow M, Waters J, Morris E. MRI for breast cancer screening, diagnosis, and treatment [J]. Lancet, 2011, 378(9805): 1804-1811.
4	Leithner D, Wengert GJ, Helbich TH, et al. Clinical role of breast MRI now and going forward [J]. Clinical Radiology, 2018, 73(8): 700-714.
5	Mann RM, Cho N, Moy L. Breast MRI: state of the art [J]. Radiology, 2019, 292(3): 520-536.
6	Yeh ED, Georgian-Smith D, Raza S, et al. Positioning in breast MR imaging to optimize image quality [J]. Radiographics, 2014, 34(1): E1-E17.
7	Pinker K, Bogner W, Baltzer P, et al. Improved diagnostic accuracy with multiparametric magnetic resonance imaging of the breast using dynamic contrast-enhanced magnetic resonance imaging, diffusion-weighted imaging, and 3-dimensional proton magnetic resonance spectroscopic imaging [J]. Invest Radiol, 2014, 49(6): 421-430.
8	Marino MA, Helbich T, Baltzer P, et al. Multiparametric MRI of the breast: A review [J]. J Magn Reson Imaging, 2018, 47(2): 301-315.
9	Westra C, Dialani V, Mehta TS, et al. Using T2-weighted sequences to more accurately characterize breast masses seen on MRI [J]. AJR Am J Roentgenol, 2014, 202(3): W183-W190.
10	Cheon H, Kim H J, Kim TH, et al. Invasive breast cancer: prognostic value of peritumoral edema identified at preoperative MR imaging [J]. Radiology, 2018, 287(1): 68-75.
11	Arponen O, Masarwah A, Sutela A, et al. Incidentally detected enhancing lesions found in breast MRI: analysis of apparent diffusion coefficient and T2 signal intensity significantly improves specificity [J]. Eur Radiol, 2016, 26(12): 4361-4370.
12	Kuhl CK, Mielcareck P, Klaschik S, et al. Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions?[J]. Radiology, 1999, 211(1): 101-110.
13	Liu F, Kornecki A, Shmuilovich O, et al. Optimization of time-to-peak analysis for differentiating malignant and benign breast lesions with dynamic contrast-enhanced MRI [J]. Acad Radiol, 2011, 18(6): 694-704.
14	Kim JY, Kim SH, Kim YJ, et al. Enhancement parameters on dynamic contrast enhanced breast MRI: do they correlate with prognostic factors and subtypes of breast cancers?[J]. Magn Reson Imaging, 2015, 33(1): 72-80.
15	American College of Radiology. BI-RADS Atlas [M]. Reston: American College of Radiology, 2013.
16	Partridge SC, Nissan N, Rahbar H, et al. Diffusion-weighted breast MRI: Clinical applications and emerging techniques [J]. J Magn Reson Imaging, 2017, 45(2): 337-355.
17	Shi RY, Yao QY, Wu LM, et al. Breast lesions: diagnosis using diffusion weighted imaging at 1.5T and 3.0T-systematic review and Meta-analysis [J]. Clin Breast Cancer, 2018, 18(3): e305-e320.
18	Lee JM, Abraham L, Lam DL, et al. Cumulative risk distribution for interval invasive second breast cancers after negative surveillance mammography [J]. J Clin Oncol, 2018, 36(20): 2070-2077.
19	Morrogh M, Morris EA, Liberman L, et al. MRI identifies otherwise occult disease in select patients with Paget disease of the nipple [J]. J Am Coll Surg, 2008, 206(2): 316-321.
20	Kuhl CK, Strobel K, Bieling H, et al. Impact of preoperative breast MR Imaging and mr-guided surgery on diagnosis and surgical outcome of women with invasive breast cancer with and without DCIS component [J]. Radiology, 2017, 284(3): 645-655.
21	Lee CI, Chen LE, Elmore JG. Risk-based breast cancer screening: implications of breast density [J]. Med Clin North Am, 2017, 101(4): 725-741.
22	Thomassin-Naggara I, Siles P, Trop I, et al. How to measure breast cancer tumoral size at MR imaging [J]. Eur J Radiol, 2013, 82(12): e790-e800.
23	Romeo V, Picariello V, Pignata A, et al. Influence of different post-contrast time points on dynamic contrast-enhanced (DCE) MRI T staging in breast cancer [J]. Eur J Radiol, 2020, 124: 108819.
24	Choi WJ, Cha JH, Kim HH, et al. The accuracy of breast MR imaging for measuring the size of a breast cancer: analysis of the histopathologic factors [J]. Clin Breast Cancer, 2016, 16(6): e145-e152.
25	Plana MN, Carreira C, Muriel A, et al. Magnetic resonance imaging in the preoperative assessment of patients with primary breast cancer: systematic review of diagnostic accuracy and meta-analysis [J]. Eur Radiol, 2012, 22(1): 26-38.
26	Ecanow JS, Abe H, Newstead GM, et al. Axillary staging of breast cancer: what the radiologist should know [J]. Radio Graphics, 2013, 33(6): 1589-1612.
27	Hyun SJ, Kim EK, Moon HJ, et al. Preoperative axillary lymph node evaluation in breast cancer patients by breast magnetic resonance imaging (MRI): Can breast MRI exclude advanced nodal disease?[J]. Eur Radiol, 2016, 26(11): 3865-3873.
28	van Nijnatten TJA, Ploumen EH, Schipper RJ, et al. Routine use of standard breast MRI compared to axillary ultrasound for differentiating between no, limited and advanced axillary nodal disease in newly diagnosed breast cancer patients [J]. Eur J Radiol, 2016, 85(12): 2288-2294.
29	Han L, Zhu Y, Liu Z, et al. Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer [J]. Eur Radiol, 2019, 29(7): 3820-3829.
30	Ha R, Chang P, Karcich J, et al. Axillary lymph node evaluation utilizing convolutional neural networks using MRI dataset [J]. J Digit Imaging, 2018, 31(6): 851-856.
31	King TA, Morrow M. Surgical issues in patients with breast cancer receiving neoadjuvant chemotherapy [J]. Nat Rev Clin Oncol, 2015, 12(6): 335-343.
32	Cortazar P, Zhang L, Untch M, et al. Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis [J]. Lancet, 2014, 384(9938): 164-172.
33	Eisenhauer EA, Therasse P, Bogaerts J, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1) [J]. Eur J Cancer, 2009, 45(2): 228-247.
34	Scheel JR, Kim E, Partridge SC, et al. MRI, clinical examination, and mammography for preoperative assessment of residual disease and pathologic complete response after neoadjuvant chemotherapy for breast cancer: ACRIN 6657 Trial [J]. AJR Am J Roentgenol, 2018, 210(6): 1376-1385.
35	Padhani AR, Hayes C, Assersohn L, et al. Prediction of clinicopathologic response of breast cancer to primary chemotherapy at contrast-enhanced MR imaging: initial clinical results [J]. Radiology, 2006, 239(2): 361-374.
36	McLaughlin R, Hylton N. MRI in breast cancer therapy monitoring [J]. NMR Biomed, 2011, 24(6): 712-720.
37	Kim Y, Kim SH, Song BJ, et al. Early prediction of response to neoadjuvant chemotherapy using dynamic contrast-enhanced MRI and ultrasound in breast cancer [J]. Korean J Radiol, 2018, 19(4): 682-691.
38	Dogan BE, Yuan Q, Bassett R, et al. Comparing the performances of magnetic resonance imaging size vs pharmacokinetic parameters to predict response to neoadjuvant chemotherapy and survival in patients with breast cancer [J]. Curr Probl Diagn Radiol, 2019, 48(3): 235-240.
39	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.
40	Comstock CE, Gatsonis C, Newstead GM, et al. Comparison of abbreviated breast MRI vs digital breast tomosynthesis for breast cancer detection among women with dense breasts undergoing screening [J]. JAMA, 2020, 323(8): 746-756.
41	Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images are more than pictures, they are data [J]. Radiology, 2016, 278(2): 563-577.
42	Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: Extracting more information from medical images using advanced feature analysis [J]. Eur J Cancer, 2012, 48(4): 441-446.
43	Valdora F, Houssami N, Rossi F, et al. Rapid review: radiomics and breast cancer [J]. Breast Cancer Res Treat, 2018, 169(2): 217-229.
44	Parekh VS, Jacobs MA. Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI [J]. NPJ Breast Cancer, 2017, 3(1): 43-49.
45	Li H, Zhu Y, Burnside ES, et al. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set [J]. NPJ Breast Cancer, 2016, 2(1): 16012.
46	Cui Y, Yang X, Shi Z, et al. Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer [J]. European Radiology, 2019, 29(3): 1211-1220.
47	Pinker K, Bogner W, Baltzer P, et al. Clinical application of bilateral high temporal and spatial resolution dynamic contrast-enhanced magnetic resonance imaging of the breast at 7 T [J]. Eur Radiol, 2014, 24(4): 913-920.
48	Gruber S, Pinker K, Zaric O, et al. Dynamic contrast-enhanced magnetic resonance imaging of breast tumors at 3 and 7 T: a comparison [J]. Invest Radiol, 2014, 49(5): 354-362.
49	Ma D, Lu F, Zou X, et al. Intravoxel incoherent motion diffusion-weighted imaging as an adjunct to dynamic contrast-enhanced MRI to improve accuracy of the differential diagnosis of benign and malignant breast lesions [J]. Magn Reson Imaging, 2017, 36: 175-179.
50	Kim Y, Ko K, Kim D, et al. Intravoxel incoherent motion diffusion-weighted MR imaging of breast cancer: association with histopathological features and subtypes [J]. Br J Radiol, 2016, 89(1063): 20160140.
51	Sun K, Chen X, Chai W, et al. Breast cancer: Diffusion kurtosis MR imaging-diagnostic accuracy and correlation with clinical-pathologic factors [J]. Radiology, 2015, 277(1): 46-55.
52	Zaric O, Pinker K, Zbyn S, et al. Quantitative sodium MR imaging at 7 T: Initial results and comparison with diffusion-weighted imaging in patients with breast tUMORS [J]. Radiology, 2016, 280(1): 39-48.
53	Zhang S, Seiler S, Wang X, et al. CEST-Dixon for human breast lesion characterization at 3 T: A preliminary study [J]. Magn Reson Med, 2018, 80(3): 895-903.

[1]	Lianlian Zhang, Pinjing Hui, Yafang Ding. Clinical value of carotid Doppler ultrasonography in assessing vulnerability of atherosclerotic plaques [J]. Chinese Journal of Medical Ultrasound (Electronic Edition), 2023, 20(08): 816-821.
[2]	Hua Shao, Ziyue Na, Hui Jing, Bo Li, Qiucheng Wang, Wen Cheng. Value of preoperative percutaneous contrast-enhanced ultrasound in diagnosis of axillary sentinel lymph node metastasis and burden in breast cancer [J]. Chinese Journal of Medical Ultrasound (Electronic Edition), 2023, 20(08): 849-853.
[3]	Xu Zhang, Jianping Xu, Dongming Su, Caifen Wang, Dali Wang, Wenzhi Zhang. Contrast-enhanced ultrasound features of male breast masses [J]. Chinese Journal of Medical Ultrasound (Electronic Edition), 2023, 20(08): 854-859.
[4]	Xiaoyan Dong, Qi Zhao, Jun Tang, Li Zhang, Xiaoyan Yang, Jiao Li. Clinical characteristics of neonates infected with the Omicron variant of severe acute respiratory syndrome coronavirus 2 [J]. Chinese Journal of Obstetrics & Gynecology and Pediatrics(Electronic Edition), 2023, 19(05): 595-603.
[5]	Yi He, Dongsheng Yu. Advances in diagnosis and treatment of dislocation injury of young permanent teeth [J]. Chinese Journal of Stomatological Research(Electronic Edition), 2023, 17(06): 400-406.
[6]	bing Feng, Qiuguo Zou, Zhenbo Liang, Yanming Lu, Yi Zeng, Shumiao Wu. Clinical study of ultrasonic signs and molecular biological indicators of senile non-specific invasive breast cancer [J]. Chinese Journal of Operative Procedures of General Surgery(Electronic Edition), 2024, 18(01): 48-51.
[7]	Yansong Li, Huimin Feng, Mingchao Liu, Zepeng Liu, Qiuxia Jiang. Expression and clinical significance of STIP1 in triple negative breast cancer [J]. Chinese Journal of Operative Procedures of General Surgery(Electronic Edition), 2024, 18(01): 52-56.
[8]	Yu Han, Wu Zhang, Anqi Li, Wenying Chen, Sidong Xie. Diagnostic value of MRI Liver Image Reporting and Data System (LI-RADS) for hepatocellular carcinoma in patients with non-cirrhotic hepatitis B [J]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2023, 12(06): 669-673.
[9]	Manshi Lei, Sisi Deng, Xinrong Wang, Jinbin Huang, Qing Xiang, Anni Xiong, Zhan'ao Meng. Application of artificial intelligence-assisted compression sensing technology in upper abdominal fat-suppressed T2WI sequence [J]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2023, 12(05): 551-556.
[10]	Guangjun Zhong, Chunhua Liu, Wansen Zhu, Xiaolei Xu, Zhaojun Wang. Application of conventional MRI combined with different scanning sequences in preoperative staging diagnosis, chemotherapy efficacy and prognosis evaluation of gastric cancer [J]. Chinese Journal of Digestion and Medical Imageology(Electronic Edition), 2023, 13(06): 378-382.
[11]	Baoru Hu, Naijian Shang, Di Gao. DCE-MRI and DWI findings of advanced hepatocellular carcinoma and their correlation with prognosis after immunotherapy [J]. Chinese Journal of Digestion and Medical Imageology(Electronic Edition), 2023, 13(06): 399-403.
[12]	Yuxian Wu, Yayuan Feng, Lei Huo, Ningyang Jia, Juan Zhang. Study on the diagnostic value of imaging in primary hepatic lymphoma [J]. Chinese Journal of Digestion and Medical Imageology(Electronic Edition), 2023, 13(05): 349-353.
[13]	Haitao Feng, Tao Xu, Wenyang Liu, Chen Sun, Shangchao Cao. Value of three-dimensional arterial spin labeling combined with DCE-MRI in the diagnosis of postoperative recurrence and radiation brain necrosis of glioma [J]. Chinese Journal of Digestion and Medical Imageology(Electronic Edition), 2023, 13(04): 262-265.
[14]	Fei Liu, Yingxin Wang, Xing Ma, Ling Xin, Yuanjia Cheng, Qian Liu, Yue Wang, Junjun Zhang. Different conductive media for intracavitary electrocardiogram-guided arm port implantation among breast cancer patients: a randomized controlled trial [J]. Chinese Journal of Clinicians(Electronic Edition), 2023, 17(07): 760-764.
[15]	Ruixue Yue, Lingxin Kong, Xin Hao, Jinqiang Yang, Meng Han, Guozhong Cui, Jianjun Wang, Zhisheng Zhang, Fanting Kong, Wei Zhang, Wenbo He, Xianqiao Li, Xinping Zhou, Donghong Xu, Chongzhu Hu. HER2 protein expression levels predict efficacy of neoadjuvant therapy in breast cancer: A real-world study [J]. Chinese Journal of Clinicians(Electronic Edition), 2023, 17(07): 765-770.

Please choose a citation manager

Content to export