Share：
Share this content in WeChat
X
[Chinese][PDF]975108
• REVIEW •
Research progress of radiomics based on MRI in breast cancer
WANG Zhongyi  MAO Ning  XIE Haizhu 

Cite this article as: Wang ZY, Mao N, Xie HZ. Research progress of radiomics based on MRI in breast cancer[J]. Chin J Magn Reson Imaging, 2021, 12(1): 109-111. DOI:10.12015/issn.1674-8034.2021.01.026.


[Abstract] Breast cancer is one of the most common malignancy in women worldwide, and also the main cause of death from cancer in women. Improving accuracy of its early diagnosis, the early predictions of prognosis and response to therapy are crucial issues of clinical practice. MRI is the common imaging tool in diagnosing breast cancer because of high sensitivity to soft tissues. MRI can provide more comprehensive diagnostic information compared to mammography and ultrasonography. Radiomics, defined as the high throughput extraction and analysis of quantitative features from imaging data, is a relatively new field of research. In recent years, it has become popular and increasingly used in oncology. This article reviews the progress of radiomics based on MRI in breast cancer.
[Keywords] radiomics；breast cancer；oncology；artificial intelligence；magnetic resonance imaging

WANG Zhongyi1, 2   MAO Ning2   XIE Haizhu2*  

1 College of Medical Imaging, Binzhou Medical University, Shandong Province, Yantai 264000, China

2 Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai 264000, China

*Corresponding author: Xie HZ, E-mail: xhz000417@sina.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  This article is supported by the National Natural Science Foundation of China No. 82001775
Received  2020-09-16
Accepted  2020-11-30
DOI: 10.12015/issn.1674-8034.2021.01.026
Cite this article as: Wang ZY, Mao N, Xie HZ. Research progress of radiomics based on MRI in breast cancer[J]. Chin J Magn Reson Imaging, 2021, 12(1): 109-111. DOI:10.12015/issn.1674-8034.2021.01.026.

1
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2017. CA Cancer J Clin, 2017, 67(1): 7-30. DOI: 10.3322/caac.21387
2
Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun, 2014, 5: 4006. DOI: 10.1038/ncomms5006
3
Gillies RJ, Anderson AR, Gatenby RA, et al. The biology underlying molecular imaging in oncology: from genome to anatome and back again. Clin Radiol, 2010, 65(7): 517-521. DOI: 10.1016/j.crad.2010.04.005
4
Khangura S, Konnyu K, Cushman R, et al. Evidence summaries: the evolution of a rapid review approach. Syst Rev, 2012, 1: 10. DOI: 10.1186/2046-4053-1-10
5
Limkin EJ, Sun R, Dercle L, et al. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol, 2017, 24(6): 1191-1206. DOI: 10.1093/annonc/mdx034
6
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036
7
Kumar V, Gu Y, Basu S, et al. Radiomics: the process and the challenges. Magn Reson Imaging, 2012, 30(9): 1234-1248. DOI: 10.1016/j.mri.2012.06.010
8
Li H, Sun H, Liu S, et al. Assessing the performance of benign and malignant breast lesion classification with bilateral TIC differentiation and other effective features in DCE-MRI. J Magn Reson Imaging, 2019, 50(2): 465-473. DOI: 10.1002/jmri.26646
9
Lo Gullo R, Daimiel I, Rossi Saccarelli C, et al. Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers. Eur Radiol, 2020, 30(12): 6721-6731. DOI: 10.1007/s00330-020-06991-7
10
Pinker K, Helbich TH, Morris EA, et al. The potential of multiparametric MRI of the breast. Br J Radiol, 2017, 90(1069): 20160715. DOI: 10.1259/bjr.20160715
11
Zhang L, Tang M, Min Z, et al. Accuracy of combined dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted imaging for breast cancer detection: a meta-analysis. Acta Radiol, 2016, 57(6): 651-660. DOI: 10.1177/0284185115597265
12
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. Invest Radiol, 2014, 49(6): 421-430. DOI: 10.1097/RLI.0000000000000029
13
Nogueira L, Brandao S, Matos E, et al. Application of the diffusion kurtosis model for the study of breast lesions. Eur Radiol, 2014, 24(6): 1197-1203. DOI: 10.1007/s00330-014-3146-5
14
Zhang Q, Peng Y, Liu W, et al. Radiomics based on multimodal MRI for the differential diagnosis of benign and malignant breast lesions. J Magn Reson Imaging, 2020, 52(2): 596-607. DOI: 10.1002/jmri.27098
15
Truhn D, Schrading S, Haarburger C, et al. Radiomic versus convolutional neural networks analysis for classification of contrast- enhancing lesions at multiparametric breast MRI. Radiology, 2019, 290(2): 290-297. DOI: 10.1148/radiol.2018181352
16
Wang QL, Mao N, Shi YH, et al. Texture analysis for differentiating breast invasive ductal carcinoma and breast fibroadenoma on diffusion weighted image:a preliminary study. J Pract Radiol, 2018, 34(9): 1368-1371, 1422. DOI: 10.3969/j.issn.1002-1671.2018.09.015
17
Huber KE, Carey LA, and Wazer DE. Breast cancer molecular subtypes in patients with locally advanced disease: impact on prognosis, patterns of recurrence, and response to therapy. Semin Radiat Oncol, 2009, 19(4): 204-210. DOI: 10.1016/j.semradonc.2009.05.004
18
Xie T, Wang Z, Zhao Q, et al. Machine learning-based analysis of MR multiparametric radiomics for the subtype classification of breast cancer. Front Oncol, 2019, 9: 505. DOI: 10.3389/fonc.2019.00505
19
Zhang Y, Chen JH, Lin Y, et al. Prediction of breast cancer molecular subtypes on DCE-MRI using convolutional neural network with transfer learning between two centers. Eur Radiol, 2020: 10. DOI: 10.1007/s00330-020-07274-x
20
Liu C, Ding J, Spuhler K, et al. Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI. J Magn Reson Imaging, 2019, 49(1): 131-140. DOI: 10.1002/jmri.26224
21
Shan YN, Xu W, Wang R, et al. A Nomogram combined radiomics and kinetic curve pattern as imaging biomarker for detecting metastatic axillary lymph node in invasive breast cancer. Front Oncol, 2020, 10: 1463. DOI: 10.3389/fonc.2020.01463
22
Liu MJ, Mao N, Ma H, et al. Radiomics based on DCE-MRI for the preoperative prediction of SLN metastasis in breast cancer. Chin Imaging J Integr Tradit West Med, 2020, 18(3): 227-231. DOI: 10.3969/j.issn.1672-0512.2020.03.003
23
Giuliano AE, Ballman KV, McCall L, et al. Effect of axillary dissection vs no axillary dissection on 10-year overall survival among women with invasive breast cancer and sentinel node metastasis: the ACOSOG Z0011 (Alliance) randomized clinical trial. JAMA, 2017, 318(10): 918-926. DOI: 10.1001/jama.2017.11470
24
Guo X, Liu Z, Sun C, et al. Deep learning radiomics of ultrasonography: identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer. EBioMedicine, 2020, 60: 103018. DOI: 10.1016/j.ebiom.2020.103018
25
Gradishar WJ, Anderson BO, Balassanian R, et al. Breast cancer, version 4.2017, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw, 2018, 16(3): 310-320. DOI: 10.6004/jnccn.2018.0012
26
Rivenbark AG, O'Connor SM, and Coleman WB. Molecular and cellular heterogeneity in breast cancer: challenges for personalized medicine. Am J Pathol, 2013, 183(4): 1113-1124. DOI: 10.1016/j.ajpath.2013.08.002
27
Liu Z, Li Z, Qu J, et al. Radiomics of multiparametric MRI for pretreatment prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study. Clin Cancer Res, 2019, 25(12): 3538-3547. DOI: 10.1158/1078-0432.CCR-18-3190
28
Sutton EJ, Onishi N, Fehr DA, et al. A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy. Breast Cancer Res, 2020, 22(1): 57. DOI: 10.1186/s13058-020-01291-w
29
Nam KJ, Park H, Ko ES, et al. Radiomics signature on 3T dynamic contrast-enhanced magnetic resonance imaging for estrogen receptor- positive invasive breast cancers: preliminary results for correlation with oncotype DX recurrence scores. Medicine (Baltimore), 2019, 98(23): e15871. DOI: 10.1097/MD.0000000000015871
30
Shao L, Liu Z, Feng L, et al. Multiparametric MRI and whole slide image-based pretreatment prediction of pathological response to neoadjuvant chemoradiotherapy in rectal cancer: a multicenter radiopathomic study. Ann Surg Oncol, 2020, 27(11): 4296-4306. DOI: 10.1245/s10434-020-08659-4
31
Mao N, Yin P, Li Q, et al. Radiomics nomogram of contrast-enhanced spectral mammography for prediction of axillary lymph node metastasis in breast cancer: a multicenter study. Eur Radiol, 2020, 30(12): 6732- 6739. DOI: 10.1007/s00330-020-07016-z
32
Lin F, Wang Z, Zhang K, et al. Contrast-enhanced spectral mammography-based radiomics nomogram for identifying benign and malignant breast lesions of Sub-1 cm. Front Oncol, 2020, 10: 573630. DOI: 10.3389/fonc.2020.573630
33
Mao N, Yin P, Wang Q, et al. Added value of radiomics on mammography for breast cancer diagnosis: a feasibility study. J Am Coll Radiol, 2019, 16(4 Pt A): 485-491. DOI: 10.1016/j.jacr.2018.09.041
34
Sun Q, Lin X, Zhao Y, et al. Deep learning vs. radiomics for predicting axillary lymph node metastasis of breast cancer using ultrasound images: don't forget the peritumoral region. Front Oncol, 2020, 10: 53. DOI: 10.3389/fonc.2020.00053
35
Qu YH, Zhu HT, Cao K, et al. Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (DL) method. Thorac Cancer, 2020, 11(3): 651-658. DOI: 10.1111/1759-7714.13309

PREV Research progress of cardiac magnetic resonance in acute myocardial infarction
NEXT Research progress of 1H-MRS in breast cancer
  


LINK


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