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Research progress of multiparametric MRI radiomics in breast cancer
HUANG Xiaoni  JIANG Yuanliang  HUANG Wencai 

Cite this article as: HUANG X N, JIANG Y L, HUANG W C. Research progress of multiparametric MRI radiomics in breast cancer[J]. Chin J Magn Reson Imaging, 2023, 14(6): 151-155. DOI:10.12015/issn.1674-8034.2023.06.027.

[Abstract] The incidence of breast cancer is the first among female malignant tumors. Breast magnetic resonance imaging (MRI) is used in the early diagnosis of breast cancer, formulation of preoperative guiding operation plan and evaluation of curative effect because of its advantages of multi-parameter, multi-sequence, multi-direction, high sensitivity and no radiation. Radiomics is a research hotspot in recent years. By converting digital medical images into mineable data, many hidden quantitative information can be extracted from morphological and functional images, in order to reflect the potential pathological and physiological characteristics of tissues to assist precision medicine. The current researches on radiomics in breast has basically covered the entire diagnosis and treatment process of breast diseases. However, there are still many problems to be solved in the process of translating radiomics into clinical practice. We will briefly describe the relationship between radiomics and artificial intelligence, and summarize the use of multiparametric MRI radiomics in differentiating benign and malignant breast lesions, predicting breast cancer molecular subtypes, lymph node status, the efficacy of neoadjuvant chemotherapy, the prognosis and disease-free survival based on published literature, discussing the limitations and challenges of current researches, in order to provide reference for improving the next research.
[Keywords] breast cancer;magnetic resonance imaging;radiomics;machine learning;artificial intelligence;diagnosis;prediction;prognosis

HUANG Xiaoni1   JIANG Yuanliang2   HUANG Wencai1, 2*  

1 The First School of Clinical Medicine, Southern Medical University, Guangzhou 510510, China

2 Department of Radiology, Central Theater General Hospital, Wuhan 430064, China

Corresponding author: Huang WC, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of Hubei Province (No. 2019CFB285); Health Commission of Hubei Province Scientific Research Joint Project (No. WJ2019H113).
Received  2022-01-29
Accepted  2023-05-18
DOI: 10.12015/issn.1674-8034.2023.06.027
Cite this article as: HUANG X N, JIANG Y L, HUANG W C. Research progress of multiparametric MRI radiomics in breast cancer[J]. Chin J Magn Reson Imaging, 2023, 14(6): 151-155. DOI:10.12015/issn.1674-8034.2023.06.027.

SUNG H, FERLAY J, SIEGEL R L, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249. DOI: 10.3322/caac.21660.
ALLEMANI C, MATSUDA T, DI CARLO V, et al. Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries[J]. Lancet, 2018, 391(10125): 1023-1075. DOI: 10.1016/S0140-6736(17)33326-3.
LIU M M, ZHANG F X, LU D X, et al. Research status and potential of multi-parameter breast MRI[J]. Chin J Magn Reson Imaging, 2022, 13(2): 145-147, 151. DOI: 10.12015/issn.1674-8034.2022.02.036.
MARINO M A, HELBICH T, BALTZER P, et al. Multiparametric MRI of the breast: a review[J]. J Magn Reson Imaging, 2018, 47(2): 301-315. DOI: 10.1002/jmri.25790.
MANN R M, CHO N, MOY L. Breast MRI: state of the art[J]. Radiology, 2019, 292(3): 520-536. DOI: 10.1148/radiol.2019182947.
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. DOI: 10.1097/RLI.0000000000000029.
MAYERHOEFER M E, MATERKA A, LANGS G, et al. Introduction to radiomics[J]. J Nucl Med, 2020, 61(4): 488-495. DOI: 10.2967/jnumed.118.222893.
GILLIES R J, ANDERSON A R, GATENBY R A, 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.
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. DOI: 10.1016/j.ejca.2011.11.036.
MA X W, LUO Y H. Application progress of radiomics in breast cancer[J]. Chin J Magn Reson Imaging, 2018, 9(8): 637-640. DOI: 10.12015/issn.1674-8034.2018.08.015
GILLIES R J, KINAHAN P E, HRICAK H. Radiomics: images are more than pictures, they are data[J]. Radiology, 2016, 278(2): 563-577. DOI: 10.1148/radiol.2015151169.
GORE J C. Artificial intelligence in medical imaging[J/OL]. Magn Reson Imaging, 2020, 68: A1-A4 [2022-12-13]. DOI: 10.1016/j.mri.2019.12.006.
VISVIKIS D, CHEZE LE REST C, JAOUEN V, et al. Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications[J]. Eur J Nucl Med Mol Imaging, 2019, 46(13): 2630-2637. DOI: 10.1007/s00259-019-04373-w.
MEYER-BASE A, MORRA L, TAHMASSEBI A, et al. AI-enhanced diagnosis of challenging lesions in breast MRI: a methodology and application primer[J]. J Magn Reson Imaging, 2021, 54(3): 686-702. DOI: 10.1002/jmri.27332.
CHOI R Y, COYNER A S, KALPATHY-CRAMER J, et al. Introduction to machine learning, neural networks, and deep learning[J/OL]. Transl Vis Sci Technol, 2020, 9(2): 14 [2022-12-13]. DOI: 10.1167/tvst.9.2.14.
REIG B, HEACOCK L, GERAS K J, et al. Machine learning in breast MRI[J]. J Magn Reson Imaging, 2020, 52(4): 998-1018. DOI: 10.1002/jmri.26852.
AVANZO M, WEI L S, STANCANELLO J, et al. Machine and deep learning methods for radiomics[J/OL]. Med Phys, 2020, 47(5): e185-e202 [2022-12-13]. DOI: 10.1002/mp.13678.
CONTI A, DUGGENTO A, INDOVINA I, et al. Radiomics in breast cancer classification and prediction[J/OL]. Semin Cancer Biol, 2021, 72: 238-250 [2022-01-28]. DOI: 10.1016/j.semcancer.2020.04.002.
DAIMIEL NARANJO I, GIBBS P, REINER J S, et al. Radiomics and machine learning with multiparametric breast MRI for improved diagnostic accuracy in breast cancer diagnosis[J/OL]. Diagnostics, 2021, 11(6): 919 [2022-01-28]. DOI: 10.3390/diagnostics11060919.
ZHANG Q, PENG Y S, LIU W, et al. Radiomics based on multimodal MRI for the differential diagnosis of benign and malignant breast lesions[J]. J Magn Reson Imaging, 2020, 52(2): 596-607. DOI: 10.1002/jmri.27098.
LI Y, YANG Z L, LV W Z, et al. Non-mass enhancements on DCE-MRI: development and validation of a radiomics-based signature for breast cancer diagnoses[J/OL]. Front Oncol, 2021, 11: 738330 [2022-01-28]. DOI: 10.3389/fonc.2021.738330.
GIBBS P, ONISHI N, SADINSKI M, et al. Characterization of sub-1 cm breast lesions using radiomics analysis[J]. J Magn Reson Imaging, 2019, 50(5): 1468-1477. DOI: 10.1002/jmri.26732.
MARTELOTTO L G, NG C K, PISCUOGLIO S, et al. Breast cancer intra-tumor heterogeneity[J/OL]. Breast Cancer Res, 2014, 16(3): 210 [2022-01-28]. DOI: 10.1186/bcr3658.
GOLDHIRSCH A, WOOD W C, COATES A S, et al. Strategies for subtypes: dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011[J]. Ann Oncol, 2011, 22(8): 1736-1747. DOI: 10.1093/annonc/mdr304.
LEITHNER D, MAYERHOEFER M E, MARTINEZ D F, et al. Non-invasive assessment of breast cancer molecular subtypes with multiparametric magnetic resonance imaging radiomics[J/OL]. J Clin Med, 2020, 9(6): 1853 [2022-01-28]. DOI: 10.3390/jcm9061853.
HUANG Y H, WEI L H, HU Y L, et al. Multi-parametric MRI-based radiomics models for predicting molecular subtype and androgen receptor expression in breast cancer[J/OL]. Front Oncol, 2021, 11: 706733 [2022-01-28]. DOI: 10.3389/fonc.2021.706733.
SONG L R, LI C L, YIN J D. Texture analysis using semiquantitative kinetic parameter maps from DCE-MRI: preoperative prediction of HER2 status in breast cancer[J/OL]. Front Oncol, 2021, 11: 675160 [2022-01-28]. DOI: 10.3389/fonc.2021.675160.
FAN M, ZHANG P, WANG Y, et al. Radiomic analysis of imaging heterogeneity in tumours and the surrounding parenchyma based on unsupervised decomposition of DCE-MRI for predicting molecular subtypes of breast cancer[J]. Eur Radiol, 2019, 29(8): 4456-4467. DOI: 10.1007/s00330-018-5891-3.
CAUDLE A S, CUPP J A, KUERER H M. Management of axillary disease[J]. Surg Oncol Clin N Am, 2014, 23(3): 473-486. DOI: 10.1016/j.soc.2014.03.007.
CHAI R M, MA H, XU M J, et al. Differentiating axillary lymph node metastasis in invasive breast cancer patients: a comparison of radiomic signatures from multiparametric breast MR sequences[J]. J Magn Reson Imaging, 2019, 50(4): 1125-1132. DOI: 10.1002/jmri.26701.
LI L, YU T, SUN J Q, et al. Prediction of the number of metastatic axillary lymph nodes in breast cancer by radiomic signature based on dynamic contrast-enhanced MRI[J]. Acta Radiol, 2022, 63(8): 1014-1022. DOI: 10.1177/02841851211025857.
LIU C L, 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.
ZHAN C A, HU Y Q, WANG X R, et al. Prediction of Axillary Lymph Node Metastasis in Breast Cancer using Intra-peritumoral Textural Transition Analysis based on Dynamic Contrast-enhanced Magnetic Resonance Imaging[J/OL]. Acad Radiol, 2022, 29: S107-S115 [2022-01-28]. DOI: 10.1016/j.acra.2021.02.008.
XIA X D, DUAN C Z, LI M, et al. Prediction of axillary lymph node metastasis in breast cancer based on radiomics nomogram of MRI[J]. Chin J Magn Reson Imaging, 2022, 13(1): 118-122. DOI: 10.12015/issn.1674-8034.2022.01.024
邵志敏, 江泽飞, 李俊杰, 等. 中国乳腺癌新辅助治疗专家共识(2019年版)[J]. 中国癌症杂志, 2019, 29(5): 390-400.SHAO Z M, JIANG Z F, LI J J, et al. 中国乳腺癌新辅助治疗专家共识(2019年版)[J]. China Oncol, 2019, 29(5): 390-400.
STEENBRUGGEN T G, VAN RAMSHORST M S, KOK M, et al. Neoadjuvant therapy for breast cancer: established concepts and emerging strategies[J]. Drugs, 2017, 77(12): 1313-1336. DOI: 10.1007/s40265-017-0774-5.
JERUSS J S, MITTENDORF E A, TUCKER S L, et al. Combined use of clinical and pathologic staging variables to define outcomes for breast cancer patients treated with neoadjuvant therapy[J]. J Clin Oncol, 2008, 26(2): 246-252. DOI: 10.1200/JCO.2007.11.5352.
BRAMAN N M, 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/OL]. Breast Cancer Res, 2017, 19(1): 57 [2022-01-28]. DOI: 10.1186/s13058-017-0846-1.
LIU Z Y, LI Z L, QU J R, 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.
LI Q, XIAO Q, LI J W, et al. Value of machine learning with multiphases CE-MRI radiomics for early prediction of pathological complete response to neoadjuvant therapy in HER2-positive invasive breast cancer[J]. Cancer Manag Res, 2021, 13: 5053-5062. DOI: 10.2147/CMAR.S304547.
DOWSETT M, NIELSEN T O, A'HERN R, et al. Assessment of Ki67 in breast cancer: recommendations from the International Ki67 in Breast Cancer working group[J]. J Natl Cancer Inst, 2011, 103(22): 1656-1664. DOI: 10.1093/jnci/djr393.
GIULIANO A E, CONNOLLY J L, EDGE S B, et al. Breast Cancer-Major changes in the American Joint Committee on Cancer eighth edition cancer staging manual[J]. CA Cancer J Clin, 2017, 67(4): 290-303. DOI: 10.3322/caac.21393.
MA W, JI Y, QI Y, et al. Breast cancer Ki67 expression prediction by DCE-MRI radiomics features[J/OL]. Clin Radiol, 2018, 73(10): 909.e1-909.e5 [2022-01-28]. DOI: 10.1016/j.crad.2018.05.027.
FAN M, LIU Z H, XIE S D, et al. Integration of dynamic contrast-enhanced magnetic resonance imaging and T2-weighted imaging radiomic features by a canonical correlation analysis-based feature fusion method to predict histological grade in ductal breast carcinoma[J/OL]. Phys Med Biol, 2019, 64(21): 215001 [2022-01-28]. DOI: 10.1088/1361-6560/ab3fd3.
FAN M, YUAN W, ZHAO W R, 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.
National Health Commission Of The People's Republic Of China. Guidelines for the diagnosis and treatment of breast cancer (2022 edition)[J]. China Licens Pharm, 2022, 19(10): 1-26. DOI: 10.3969/j.issn.2096-3327.2022.10.001
SIMONS J M, JACOBS J G, ROIJERS J P, et al. Disease-free and overall survival after neoadjuvant chemotherapy in breast cancer: breast-conserving surgery compared to mastectomy in a large single-centre cohort study[J]. Breast Cancer Res Treat, 2021, 185(2): 441-451. DOI: 10.1007/s10549-020-05966-y.
PARK H, LIM Y, KO E S, et al. Radiomics signature on magnetic resonance imaging: association with disease-free survival in patients with invasive breast cancer[J]. Clin Cancer Res, 2018, 24(19): 4705-4714. DOI: 10.1158/1078-0432.CCR-17-3783.
LIAO N, ZHANG X C. Progress in the research on 21-gene Oncotype Dx in breast cancer prognosis[J]. China Oncol, 2009, 19(12): 953-958. DOI: 10.3969/j.issn.1007-3639.2009.12.015.
NAM K J, PARK H, KO E S, et al. Radiomics signature on 3T dynamic contrast-enhanced magnetic resonance imaging for estrogen receptor-positive invasive breast cancers[J/OL]. Medicine, 2019, 98(23): e15871 [2022-01-28]. DOI: 10.1097/md.0000000000015871.
SALA E, MEMA E, HIMOTO Y, et al. Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging[J]. Clin Radiol, 2017, 72(1): 3-10. DOI: 10.1016/j.crad.2016.09.013.
YU Y F, TAN Y J, XIE C M, 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/OL]. JAMA Netw Open, 2020, 3(12): e2028086 [2022-12-13]. DOI: 10.1001/jamanetworkopen.2020.28086.

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