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Overview of MRI-based radiomics in breast cancer
FU Qiuyi  SUN Kun  YAN Fuhua 

Cite this article as: FU Q Y, SUN K, YAN F H. Overview of MRI-based radiomics in breast cancer[J]. Chin J Magn Reson Imaging, 2023, 14(4): 166-170, 187. DOI:10.12015/issn.1674-8034.2023.04.029.


[Abstract] The incidence and mortality of breast cancer ranks first among female tumors in most countries around the world. Although great progress has been made in early detection of lesions and timely treatment of the disease, there is still a gap in the realization of precision medicine and personalized diagnosis and treatment. Efforts are needed to reflect overall heterogeneity of tumors by accurate quantitative assessment of the lesion and its surrounding tissue, which will help to formulate personalized diagnosis and treatment plans for breast cancer patients. Radiomics aims to extract high-dimensional data based on images, and analyze these data by establishing reliable models to quantify tumor heterogeneity for disease diagnosis, differential diagnosis and prediction, thereby providing more reliable evidence to support clinical decision. As one of the frontier fields of current research, radiomics has great clinical research value. In this paper, based on the radiomic features of MRI images, the differentiation of benign and malignant breast tumors, distinction of different molecular subtypes of breast cancer, prediction of axillary and sentinel lymph node status, evaluation of the efficacy of neoadjuvant chemotherapy and prognosis prediction will be introduced. The prospects and limitations of current radiomics development are described to improve future research.
[Keywords] breast cancer；radiomics；magnetic resonance imaging；diagnosis；prediction；prognosis；precision medicine

FU Qiuyi   SUN Kun   YAN Fuhua*  

Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200025, China

Corresponding author: Yan FH, E-mail: yfh11655@rjh.com.cn

Conflicts of interest   None.

Received  2022-10-31
Accepted  2023-03-03
DOI: 10.12015/issn.1674-8034.2023.04.029
Cite this article as: FU Q Y, SUN K, YAN F H. Overview of MRI-based radiomics in breast cancer[J]. Chin J Magn Reson Imaging, 2023, 14(4): 166-170, 187. DOI:10.12015/issn.1674-8034.2023.04.029.

[1]
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.
[2]
DERCLE L, AMMARI S, BATESON M, et al. Limits of radiomic-based entropy as a surrogate of tumor heterogeneity: ROI-area, acquisition protocol and tissue site exert substantial influence[J/OL]. Sci Rep, 2017, 7(1): 7952 [2022-09-30]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5554130/. DOI: 10.1038/s41598-017-08310-5.
[3]
PRUD'HOMME C, DESCHAMPS F, ALLORANT A, et al. Image-guided tumour biopsies in a prospective molecular triage study (MOSCATO-01): what are the real risks?[J]. Eur J Cancer, 2018, 103: 108-119. DOI: 10.1016/j.ejca.2018.08.003.
[4]
PESAPANE F, SUTER M B, ROTILI A, et al. Will traditional biopsy be substituted by radiomics and liquid biopsy for breast cancer diagnosis and characterisation?[J/OL]. Med Oncol, 2020, 37(4): 29 [2022-09-30]. https://link.springer.com/article/10.1007/s12032-020-01353-1. DOI: 10.1007/s12032-020-01353-1.
[5]
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.
[6]
LEE G, LEE H Y, PARK H, et al. Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: state of the art[J]. Eur J Radiol, 2017, 86: 297-307. DOI: 10.1016/j.ejrad.2016.09.005.
[7]
PERRE S V, DURON L, MILON A, et al. Radiomic analysis of HTR-DCE MR sequences improves diagnostic performance compared to BI-RADS analysis of breast MR lesions[J]. Eur Radiol, 2021, 31(7): 4848-4859. DOI: 10.1007/s00330-020-07519-9.
[8]
BICKELHAUPT S, JAEGER P F, LAUN F B, et al. Radiomics based on adapted diffusion kurtosis imaging helps to clarify most mammographic findings suspicious for cancer[J]. Radiology, 2018, 287(3): 761-770. DOI: 10.1148/radiol.2017170273.
[9]
PÖTSCH N, DIETZEL M, KAPETAS P, et al. An A.I. classifier derived from 4D radiomics of dynamic contrast-enhanced breast MRI data: potential to avoid unnecessary breast biopsies[J]. Eur Radiol, 2021, 31(8): 5866-5876. DOI: 10.1007/s00330-021-07787-z.
[10]
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 (Basel), 2021, 11(6): 919 [2022-09-30]. https://www.mdpi.com/2075-4418/11/6/919. DOI: 10.3390/diagnostics11060919.
[11]
PAREKH V S, JACOBS M A. Multiparametric radiomics methods for breast cancer tissue characterization using radiological imaging[J]. Breast Cancer Res Treat, 2020, 180(2): 407-421. DOI: 10.1007/s10549-020-05533-5.
[12]
PAREKH V S, JACOBS M A. Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI[J/OL]. NPJ Breast Cancer, 2017, 3: 43 [2022-09-30]. https://www.nature.com/articles/s41523-017-0045-3. DOI: 10.1038/s41523-017-0045-3.
[13]
HAO W, GONG J, WANG S P, et al. Application of MRI radiomics-based machine learning model to improve contralateral BI-RADS 4 lesion assessment[J/OL]. Front Oncol, 2020, 10: 531476 [2022-09-30]. https://www.frontiersin.org/articles/10.3389/fonc.2020.531476/full. DOI: 10.3389/fonc.2020.531476.
[14]
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.
[15]
HU B, XU K, ZHANG Z, et al. A radiomic nomogram based on an apparent diffusion coefficient map for differential diagnosis of suspicious breast findings[J]. Chung Kuo Yen Cheng Yen Chiu, 2018, 30(4): 432-438. DOI: 10.21147/j.issn.1000-9604.2018.04.06.
[16]
XIE T W, WANG Z, ZHAO Q F, et al. Machine learning-based analysis of MR multiparametric radiomics for the subtype classification of breast cancer[J/OL]. Front Oncol, 2019, 9: 505 [2022-09-30]. https://www.frontiersin.org/articles/10.3389/fonc.2019.00505/full. DOI: 10.3389/fonc.2019.00505.
[17]
LEITHNER D, HORVAT J V, MARINO M A, et al. Radiomic signatures with contrast-enhanced magnetic resonance imaging for the assessment of breast cancer receptor status and molecular subtypes: initial results[J/OL]. Breast Cancer Res, 2019, 21(1): 106 [2022-09-30]. https://breast-cancer-research.biomedcentral.com/articles/10.1186/s13058-019-1187-z. DOI: 10.1186/s13058-019-1187-z.
[18]
ZHOU J, TAN H N, LI W, et al. Radiomics signatures based on multiparametric MRI for the preoperative prediction of the HER2 status of patients with breast cancer[J]. Acad Radiol, 2021, 28(10): 1352-1360. DOI: 10.1016/j.acra.2020.05.040.
[19]
NI M, ZHOU X M, LIU J W, et al. Prediction of the clinicopathological subtypes of breast cancer using a fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI[J/OL]. BMC Cancer, 2020, 20(1): 1073 [2022-09-30]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7654148/. DOI: 10.1186/s12885-020-07557-y.
[20]
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 [2022-09-30]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7356091/. DOI: 10.3390/jcm9061853.
[21]
LI C L, SONG L R, YIN J D. Intratumoral and peritumoral radiomics based on functional parametric maps from breast DCE-MRI for prediction of HER-2 and ki-67 status[J]. J Magn Reson Imaging, 2021, 54(3): 703-714. DOI: 10.1002/jmri.27651.
[22]
DONG Y H, FENG Q J, 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.
[23]
ZHANG X, YANG Z H, CUI W J, et al. Preoperative prediction of axillary sentinel lymph node burden with multiparametric MRI-based radiomics nomogram in early-stage breast cancer[J]. Eur Radiol, 2021, 31(8): 5924-5939. DOI: 10.1007/s00330-020-07674-z.
[24]
HAN L, ZHU Y B, LIU Z Y, et al. Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer[J]. Eur Radiol, 2019, 29(7): 3820-3829. DOI: 10.1007/s00330-018-5981-2.
[25]
ZHU Y Q, JI H, ZHU Y F, et al. Predictive value of preoperative MRI-based nomogram for axillary lymph node metastasis in breast cancer[J]. Chin J Magn Reson Imaging, 2022, 13(5): 52-58. DOI: 10.12015/issn.1674-8034.2022.05.010.
[26]
ZHU Y, ZHANG S H, WANG X L, et al. Value of a nomogram based on MRI, mammography and pathology for predicting sentinel lymph node metastasis of mass-type breast invasive ductal carcinoma[J]. Chin J Magn Reson Imaging, 2022, 13(5): 45-51. DOI: 10.12015/issn.1674-8034.2022.05.009.
[27]
YU Y F, HE Z F, OUYANG J, et al. Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: a machine learning, multicenter study[J/OL]. EBioMedicine, 2021, 69: 103460 [2022-09-30]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8261009/. DOI: 10.1016/j.ebiom.2021.103460.
[28]
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 [2022-09-30]. https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2773816. DOI: 10.1001/jamanetworkopen.2020.28086.
[29]
LIU J, SUN D, CHEN L L, et al. Radiomics analysis of dynamic contrast-enhanced magnetic resonance imaging for the prediction of sentinel lymph node metastasis in breast cancer[J/OL]. Front Oncol, 2019, 9: 980 [2022-09-30]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6778833/. DOI: 10.3389/fonc.2019.00980.
[30]
SANTUCCI D, FAIELLA E, CORDELLI E, et al. 3T MRI-radiomic approach to predict for lymph node status in breast cancer patients[J/OL]. Cancers, 2021, 13(9): 2228 [2022-09-30]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8124168/. DOI: 10.3390/cancers13092228.
[31]
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.
[32]
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 [2022-09-30]. https://breast-cancer-research.biomedcentral.com/articles/10.1186/s13058-017-0846-1. DOI: 10.1186/s13058-017-0846-1.
[33]
SUTTON E J, ONISHI N, FEHR D A, et al. A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy[J/OL]. Breast Cancer Res, 2020, 22(1): 57 [2022-09-30]. https://breast-cancer-research.biomedcentral.com/articles/10.1186/s13058-020-01291-w. DOI: 10.1186/s13058-020-01291-w.
[34]
BITENCOURT A G V, 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/OL]. EBioMedicine, 2020, 61: 103042 [2022-09-30]. https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(20)30418-7/fulltext. DOI: 10.1016/j.ebiom.2020.103042.
[35]
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.
[36]
FAN M, CHEN H, YOU C, et al. Radiomics of tumor heterogeneity in longitudinal dynamic contrast-enhanced magnetic resonance imaging for predicting response to neoadjuvant chemotherapy in breast cancer[J/OL]. Front Mol Biosci, 2021, 8: 622219 [2022-09-30]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044916/. DOI: 10.3389/fmolb.2021.622219.
[37]
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.
[38]
ZHOU J L, LU J H, GAO C, et al. Predicting the response to neoadjuvant chemotherapy for breast cancer: wavelet transforming radiomics in MRI[J/OL]. BMC Cancer, 2020, 20(1): 100 [2022-09-30]. https://bmccancer.biomedcentral.com/articles/10.1186/s12885-020-6523-2. DOI: 10.1186/s12885-020-6523-2.
[39]
MONTEMEZZI S, BENETTI G, BISIGHIN M V, et al. 3T DCE-MRI radiomics improves predictive models of complete response to neoadjuvant chemotherapy in breast cancer[J/OL]. Front Oncol, 2021, 11: 630780 [2022-09-30]. https://www.frontiersin.org/articles/10.3389/fonc.2021.630780/full. DOI: 10.3389/fonc.2021.630780.
[40]
CHEN S J, SHU Z Y, LI Y F, et al. Machine learning-based radiomics nomogram using magnetic resonance images for prediction of neoadjuvant chemotherapy efficacy in breast cancer patients[J/OL]. Front Oncol, 2020, 10: 1410 [2022-09-30]. https://www.frontiersin.org/articles/10.3389/fonc.2020.01410/full. DOI: 10.3389/fonc.2020.01410.
[41]
HUANG Y H, CHEN W B, ZHANG X L, et al. Prediction of tumor shrinkage pattern to neoadjuvant chemotherapy using a multiparametric MRI-based machine learning model in patients with breast cancer[J/OL]. Front Bioeng Biotechnol, 2021, 9: 662749 [2022-09-30]. https://www.frontiersin.org/articles/10.3389/fbioe.2021.662749/full. DOI: 10.3389/fbioe.2021.662749.
[42]
GRANZIER R W Y, IBRAHIM A, PRIMAKOV S P, et al. MRI-based radiomics analysis for the pretreatment prediction of pathologic complete tumor response to neoadjuvant systemic therapy in breast cancer patients: a multicenter study[J/OL]. Cancers, 2021, 13(10): 2447 [2022-09-30]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8157878/. DOI: 10.3390/cancers13102447.
[43]
CHITALIA R D, ROWLAND J, MCDONALD E S, et al. Imaging phenotypes of breast cancer heterogeneity in preoperative breast dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) scans predict 10-year recurrence[J]. Clin Cancer Res, 2020, 26(4): 862-869. DOI: 10.1158/1078-0432.CCR-18-4067.
[44]
LEE J Y, LEE K S, SEO B K, et al. Radiomic machine learning for predicting prognostic biomarkers and molecular subtypes of breast cancer using tumor heterogeneity and angiogenesis properties on MRI[J]. Eur Radiol, 2022, 32(1): 650-660. DOI: 10.1007/s00330-021-08146-8.
[45]
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.
[46]
LI Q, XIAO Q, LI J W, 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.
[47]
KIM S, KIM M J, KIM E K, et al. MRI radiomic features: association with disease-free survival in patients with triple-negative breast cancer[J/OL]. Sci Rep, 2020, 10(1): 3750 [2022-09-30]. https://www.nature.com/articles/s41598-020-60822-9. DOI: 10.1038/s41598-020-60822-9.
[48]
MAZUROWSKI M A, SAHA A, HAROWICZ M R, et al. Association of distant recurrence-free survival with algorithmically extracted MRI characteristics in breast cancer[J/OL]. J Magn Reson Imaging, 2019, 49(7): e231-e240 [2022-09-30]. https://onlinelibrary.wiley.com/doi/10.1002/jmri.26648. DOI: 10.1002/jmri.26648.
[49]
XIAO J, RAHBAR H, HIPPE D S, et al. Dynamic contrast-enhanced breast MRI features correlate with invasive breast cancer angiogenesis[J/OL]. NPJ Breast Cancer, 2021, 7(1): 42 [2022-09-30]. https://www.nature.com/articles/s41523-021-00247-3. DOI: 10.1038/s41523-021-00247-3.
[50]
DIETZEL M, SCHULZ-WENDTLAND R, ELLMANN S, et al. Automated volumetric radiomic analysis of breast cancer vascularization improves survival prediction in primary breast cancer[J/OL]. Sci Rep, 2020, 10(1): 3664 [2022-09-30]. https://www.nature.com/articles/s41598-020-60393-9. DOI: 10.1038/s41598-020-60393-9.
[51]
LIU Z S, FENG B, LI C L, et al. Preoperative prediction of lymphovascular invasion in invasive breast cancer with dynamic contrast-enhanced-MRI-based radiomics[J]. J Magn Reson Imaging, 2019, 50(3): 847-857. DOI: 10.1002/jmri.26688.
[52]
OBEID J P, STOYANOVA R, KWON D, et al. Multiparametric evaluation of preoperative MRI in early stage breast cancer: prognostic impact of peri-tumoral fat[J]. Clin Transl Oncol, 2017, 19(2): 211-218. DOI: 10.1007/s12094-016-1526-9.
[53]
AREFAN D, HAUSLER R M, SUMKIN J H, et al. Predicting cell invasion in breast tumor microenvironment from radiological imaging phenotypes[J/OL]. BMC Cancer, 2021, 21(1): 370 [2022-09-30]. https://link.springer.com/article/10.1186/s12885-021-08122-x. DOI: 10.1186/s12885-021-08122-x.
[54]
WAUGH S A, PURDIE C A, JORDAN L B, et al. Magnetic resonance imaging texture analysis classification of primary breast cancer[J]. Eur Radiol, 2016, 26(2): 322-330. DOI: 10.1007/s00330-015-3845-6.
[55]
MING J, CHEN Y, LIU Y, et al. Value of preoperative prediction of Ki-67 expression in breast cancer based on DCE-MRI intratumoral combined with peritumoral radiomics model[J]. Chin J Magn Reson Imaging, 2022, 13(10): 132-137, 149. DOI: 10.12015/issn.1674-8034.2022.10.020.
[56]
KAYADIBI Y, KOCAK B, UCAR N, et al. Radioproteomics in breast cancer: prediction of ki-67 expression with MRI-based radiomic models[J]. Acad Radiol, 2022, 29(Suppl 1): S116-S125. DOI: 10.1016/j.acra.2021.02.001.
[57]
LIANG C S, CHENG Z X, HUANG Y Q, et al. An MRI-based radiomics classifier for preoperative prediction of ki-67 status in breast cancer[J]. Acad Radiol, 2018, 25(9): 1111-1117. DOI: 10.1016/j.acra.2018.01.006.
[58]
XU H, LIU J K, CHEN Z, et al. Intratumoral and peritumoral radiomics based on dynamic contrast-enhanced MRI for preoperative prediction of intraductal component in invasive breast cancer[J]. Eur Radiol, 2022, 32(7): 4845-4856. DOI: 10.1007/s00330-022-08539-3.

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