Share:
Share this content in WeChat
X
Review
Application progress of MR radiomics in neoadjuvant chemotherapy for breast cancer
LIU Tan  SHENG Xiaolong  GAO Yu  ZHANG Lina  LIU Ailian 

Cite this article as: Liu T, Sheng XL, Gao Y, et al. Application progress of MR radiomics in neoadjuvant chemotherapy for breast cancer[J]. Chin J Magn Reson Imaging, 2021, 12(7): 117-120. DOI:10.12015/issn.1674-8034.2021.07.028.


[Abstract] Individual differences and tumor heterogeneity affect the efficacy of neoadjuvant chemotherapy for breast cancer, so it is necessary to seek accurate and reliable non-invasive imaging modalities to early evaluate the efficacy of neoadjuvant chemotherapy for breast cancer. As a new diagnostic tool, radiomics is applied to the high-order feature analysis of tumors, which makes up for the deficiency of traditional MRI in evaluating tumor heterogeneity, and combines imaging, clinical and pathological data to improve the accuracy of diagnosis. This article mainly reviews the application value and challenges of MR radiomics in neoadjuvant chemotherapy for breast cancer.
[Keywords] breast cancer;neoadjuvant chemotherapy;radiomics;magnetic resonance imaging

LIU Tan1, 2   SHENG Xiaolong1, 2   GAO Yu1, 2   ZHANG Lina2*   LIU Ailian2  

1 Dalian Medical University, Dalian 116044, China

2 The First Hospital of Dalian Medical University, Dalian 116011, China

Zhang LN, E-mail: zln201045@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS This article is supported by the Liaoning Province Natural Science Found (No. 2019-ZD-0907). This article is supported by the General Project of Teaching Reform Research of Dalian Medical University (No. DYLX20043).
Received  2021-03-23
Accepted  2021-04-22
DOI: 10.12015/issn.1674-8034.2021.07.028
Cite this article as: Liu T, Sheng XL, Gao Y, et al. Application progress of MR radiomics in neoadjuvant chemotherapy for breast cancer[J]. Chin J Magn Reson Imaging, 2021, 12(7): 117-120. DOI:10.12015/issn.1674-8034.2021.07.028.

1
Shao ZM, Jiang ZF, Li JJ, et al. Consensus of experts on neoadjuvant therapy for breast cancer in China (2019 version)[J]. Chin J Oncol, 2019, 29(5): 390-400. DOI: 10.19401/j.cnki.1007-3639.2019.05.009.
2
Chen H, He Y, Zhao C, et al. Reproducibility of radiomics features derived from intravoxel incoherent motion diffusion-weighted MRI of cervical cancer[J]. Acta Radiol, 2020: 284185120934471. DOI: 10.1177/0284185120934471.
3
Khawaja A, Bartholmai BJ, Rajagopalan S, et al. Do we need to see to believe?-radiomics for lung nodule classification and lung cancer risk stratification[J]. J Thorac Dis, 2020, 12(6): 3303-3316. DOI: 10.21037/jtd.2020.03.105.
4
Gillies RJ, Anderson AR, Gatenby RA, et al. The biology underlying molecular imaging in oncology: from genome to anatome and back again[J]. Clin Radiol, 2010, 65(7): 517-521. DOI: 10.1016/j.crad.2010.04.005.
5
Limkin EJ, Sun R, Dercle L. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology[J]. Ann Oncol, 2017, 28(6): 1191-1206. DOI: 10.1093/annonc/mdx034.
6
Guo Y, Zhou SC, Yu JH, et al. Advanced researches and future challenges of radiomics[J]. Oncoradiology, 2017, 26(2): 81-90.
7
Castellano G, Bonilha L, Li LM, et al. Texture analysis of medical images[J]. Clin Radiol, 2004, 59(12): 1061-1069. DOI: 10.1016/j.crad.2004.07.008.
8
Waugh SA, Purdie CA, Jordan LB, et al. Magnetic resonance imaging texture analysis classification of primary breast cancer[J]. Eur Radiol, 2015, 26(2): 322-330. DOI: 10.1007/s00330-015-3845-6.
9
Woodard GA, Ray KM, Joe BN, et al. Qualitative radiogenomics: association between oncotype DX test recurrence score and BI-RADS mammographic and breast MR imaging features[J]. Radiology, 2018, 286(1): 60-70. DOI: 10.1148/radiol.2017162333.
10
Choudhery S, Gomez-Cardona D, Favazza CP, et al. MRI radiomics for assessment of molecular subtype, pathological complete response, and residual cancer burden in breast cancer patients treated with neoadjuvant chemotherapy[J]. Acad Radiol, 2020, S1076-6332(20): 30607-3. DOI: 10.1016/j.acra.2020.10.020.
11
Wang CH, Luo HB, Liu YY, et al. Radiomics features based on pharmacokinetic dynamic contrast-enhanced magnetic resonance imaging for identifying triple negative breast cancer[J]. Chin J Magn Reson Imaging, 2021, 12(2): 29-33. DOI: 10.12015/issn.1674-8034.2021.02.007.
12
Xue K, Li ZL, Li ZH, et al. Identify HER-2 over expression breast on radiomics of multi-oarametricMRI[J]. Radiol Pract, 2020, 35(2): 186-189.
13
Holli-Helenius K, Salminen A, Rinta-Kiikka I, et al. MRI texture analysis in differentiating luminal A and luminal B breast cancer molecular subtypes: a feasibility study[J]. BMC Med Imaging, 2017, 17(1): 69. DOI: 10.1186/s12880-017-0239-z.
14
Braman NM, Etesami M, Prasanna P, et al. Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI[J]. Breast Cancer Res, 2017, 19(1): 57. DOI: 10.1186/s13058-017-0846-1.
15
Xie T, Wang Z, Zhao Q, et al. Machine learning-based analysis of mr multiparametric radiomics for the subtype classification of breast cancer[J]. Front Oncol, 2019, 9: 505. DOI: 10.3389/fonc.2019.00505.
16
Chamming's F, Ueno Y, Ferre R, et al. Features from computerized texture analysis of breast cancers at pretreatment MR imaging are associated with response to neoadjuvant chemotherapy[J]. Radiology, 2018, 286(2): 412-420. DOI: 10.1148/radiol.2017170143.
17
Gampenrieder SP, Peer A, Weismann C, et al. Radiologic complete response (rCR) in contrast-enhanced magnetic resonance imaging (CE-MRI) after neoadjuvant chemotherapy for early breast cancer predicts recurrence-free survival but not pathologic complete response (pCR)[J]. Breast Cancer Res, 2019, 21(1): 19. DOI: 10.1186/s13058-018-1091-y.
18
Fan M, Li H, Wang S, et al. Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer[J]. PLoS One, 2017, 12(2): e0171683. DOI: 10.1371/journal.pone.0171683.
19
Zhou J, Lu J, Gao C, et al. Predicting the response to neoadjuvant chemotherapy for breast cancer: wavelet transforming radiomics in MRI[J]. BMC Cancer, 2020, 20(1): 100. DOI: 10.1186/s12885-020-6523-2.
20
Xiong Q, Zhou X, Liu Z, et al. Multiparametric MRI-based radiomics analysis for prediction of breast cancers insensitive to neoadjuvant chemotherapy[J]. Clin Transl Oncol, 2020, 22(1): 50-59. DOI: 10.1007/s12094-019-02109-8.
21
Chen X, Chen X, Yang J, et al. Combining dynamic contrast-enhanced magnetic resonance imaging and apparent diffusion coefficient maps for a radiomics nomogram to predict pathological complete response to neoadjuvant chemotherapy in breast cancer patients[J]. J Comput Assist Tomogr, 2020, 44(2): 275-283. DOI: 10.1097/RCT.0000000000000978.
22
Yang ZQ, Chen XF, Yang JD,et al. The value of DCE-MRI based radiomics modal in predicting pathological complete remission of breast cancer after neoadjuvant chemotherapy[J].Chin J Radiol, 201, 53(09): 733-736. DOI: 10.3760/cma.j.issn.1005-1201.2019.09.004.
23
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[J]. Clin Cancer Res, 2019, 25(12): 3538-3547. DOI: 10.1158/1078-0432.CCR-18-3190.
24
Drukker K, Li H, Antropova N, et al. Most-enhancing tumor volume by MRI radiomics predicts recurrence-free survival "early on" in neoadjuvant treatment of breast cancer[J]. Cancer Imaging, 2018, 18(1): 12. DOI: 10.1186/s40644-018-0145-9.
25
Jae-Hun K, Sook KE, Yaeji L, et al. Breast cancer heterogeneity: MR imaging texture analysis and survival outcomes[J]. Radiology, 2017(3): 665-675. DOI: 10.1148/radiol.2016160261.
26
Li Q, Xiao Q, Li J, et al. MRI-based radiomic signature as a prognostic biomarker for HER2-positive invasive breast cancer treated with NAC[J]. Cancer Manag Res, 2020, 12: 10603-10613. DOI: 10.2147/CMAR.S271876.
27
Liu M, Mao N, Ma H, et al. Pharmacokinetic parameters and radiomics model based on dynamic contrast enhanced MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer[J]. Cancer Imaging, 2020, 20(1): 65. DOI: 10.1186/s40644-020-00342-x.
28
Yu Y, Tan Y, Xie C, et al. Development and validation of a preoperative magnetic resonance imaging radiomics-based signature to predict axillary lymph node metastasis and disease-free survival in patients with early-stage breast cancer[J]. JAMA Netw Open, 2020, 3(12): e2028086. DOI: 10.1001/jamanetworkopen.2020.28086.
29
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]. J Magn Reson Imaging, 2019, 49(1): 131-140. DOI: 10.1002/jmri.26224.
30
Liu J, Sun D, Chen L, et al. Radiomics analysis of dynamic contrast-enhanced magnetic resonance imaging for the prediction of sentinel lymph node metastasis in breast cancer[J]. Front Oncol, 2019, 9: 980. DOI: 10.3389/fonc.2019.00980.
31
Sutton EJ, Onishi N, Fehr DA, et al. A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy[J]. Breast Cancer Res, 2020, 22(1): 57. DOI: 10.1186/s13058-020-01291-w.
32
Chen S, Shu Z, Li Y, et al. Machine learning-based radiomics nomogram using magnetic resonance images for prediction of neoadjuvant chemotherapy efficacy in breast cancer patients[J]. Front Oncol, 2020, 10: 1410. DOI: 10.3389/fonc.2020.01410.
33
Bitencourt AGV, Gibbs P, Rossi Saccarelli C, et al. MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer[J]. EBioMedicine, 2020, 61: 103042. DOI: 10.1016/j.ebiom.2020.103042.
34
Cain EH, Saha A, Harowicz MR, et al. Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set[J]. Breast Cancer Res Treat, 2018, 173(2): 455-463. DOI: 10.1007/s10549-018-4990-9.
35
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data[J]. Radiology, 2016, 278(2): 563-577. DOI: 10.1148/radiol.2015151169.
36
Valdora F, Houssami N, Rossi F, et al. Rapid review: radiomics and breast cancer[J]. Breast Cancer Res Treat, 2018, 169(2): 217-229. DOI: 10.1007/s10549-018-4675-4.

PREV Advances in the application of artificial intelligence in cardiovascular imaging
NEXT Current status of whole-body magnetic resonance imaging in prostate cancer
  



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