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Application and prospect of preoperative MRI in predicting the prognosis of breast cancer
BIAN Xiaoqian  DU Siyao  ZHANG Lina 

Cite this article as: Bian XQ, Du SY, Zhang LN. Application and prospect of preoperative MRI in predicting the prognosis of breast cancer[J]. Chin J Magn Reson Imaging, 2022, 13(6): 147-150. DOI:10.12015/issn.1674-8034.2022.06.031.


[Abstract] Breast cancer is the most common cancer in women and the top 5 in terms of mortality, and its prognostic factors are complex. In recent years, MRI has actively explored imaging markers related to breast cancer prognosis, including morphology, hemodynamics, functional imaging, radiomics and many other parameters. The study confirmed that tumor size and edge, non-mass-like enhancement, rim enhancement, peritumoral edema, and background enhancement are morphological parameters related to prognosis; hemodynamic time-intensity curves (TIC) and quantitative and semi-quantitative parameters are associated with prognosis to varying degrees; although there are still some controversies, diffusion-weighted imaging (DWI) and its derived techniques have shown great potential in prognosis prediction; MRI-based radiomics has further revealed more high-dimensional parameters related to prognosis, and computer-guided artificial intelligence is emerging. This article reviews the research progress of preoperative MRI in predicting the prognosis of breast cancer, and provides a reference for the next related research in this field.
[Keywords] breast cancer;magnetic resonance imaging;dynamic contrast-enhanced magnetic resonance imaging;diffusion‐weighted imaging;radiomics;artificial intelligence;prognosis;review

BIAN Xiaoqian   DU Siyao   ZHANG Lina*  

Department of Radiology, the First Affiliated Hospital of China Medical University, Shenyang 110001, China

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

Conflicts of interest   None.

Received  2022-01-05
Accepted  2022-06-06
DOI: 10.12015/issn.1674-8034.2022.06.031
Cite this article as: Bian XQ, Du SY, Zhang LN. Application and prospect of preoperative MRI in predicting the prognosis of breast cancer[J]. Chin J Magn Reson Imaging, 2022, 13(6): 147-150.DOI:10.12015/issn.1674-8034.2022.06.031

[1]
Siegel RL, Miller KD, Fuchs HE, et al. Cancer statistics, 2022[J]. CA Cancer J Clin, 2022, 72(1): 7-33. DOI: 10.3322/caac.21708.
[2]
Colleoni M, Sun ZX, Price KN, et al. Annual hazard rates of recurrence for breast cancer during 24 years of follow-up: results from the international breast cancer study group trials I to V[J]. J Clin Oncol, 2016, 34(9): 927-935. DOI: 10.1200/JCO.2015.62.3504.
[3]
Yu YF, Tan YJ, Xie CM, 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.
[4]
Choi WJ, Cha JH, Kim HH, et al. Long-term survival outcomes of primary breast cancer in women with or without preoperative magnetic resonance imaging: a matched cohort study[J]. Clin Oncol (R Coll Radiol), 2017, 29(10): 653-661. DOI: 10.1016/j.clon.2017.06.015.
[5]
Hayashi Y, Satake H, Ishigaki S, et al. Kinetic volume analysis on dynamic contrast-enhanced MRI of triple-negative breast cancer: associations with survival outcomes[J]. Br J Radiol, 2020, 93(1106): 20190712. DOI: 10.1259/bjr.20190712.
[6]
Pickles MD, Lowry M, Gibbs P. Pretreatment prognostic value of dynamic contrast-enhanced magnetic resonance imaging vascular, texture, shape, and size parameters compared with traditional survival indicators obtained from locally advanced breast cancer patients[J]. Invest Radiol, 2016, 51(3): 177-185. DOI: 10.1097/RLI.0000000000000222.
[7]
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.
[8]
Nam SY, Ko ES, Lim Y, et al. Preoperative dynamic breast magnetic resonance imaging kinetic features using computer-aided diagnosis: association with survival outcome and tumor aggressiveness in patients with invasive breast cancer[J]. PLoS One, 2018, 13(4): e0195756. DOI: 10.1371/journal.pone.0195756.
[9]
Lee J, Kim SH, Kang BJ, et al. Imaging characteristics of young age breast cancer (YABC) focusing on pathologic correlation and disease recurrence[J]. Sci Rep, 2021, 11(1): 20205. DOI: 10.1038/s41598-021-99600-6.
[10]
Ryu MJ, Kim YS, Lee SE. Association between imaging features using the BI-RADS and tumor subtype in patients with invasive breast cancer[J]. Curr Med Imaging, 2022, 18(6): 648-657. DOI: 10.2174/1573405617666210520155157.
[11]
Choi BB. Dynamic contrast enhanced-MRI and diffusion-weighted image as predictors of lymphovascular invasion in node-negative invasive breast cancer[J]. World J Surg Oncol, 2021, 19(1): 76. DOI: 10.1186/s12957-021-02189-3.
[12]
Kim HJ, Choi WJ, Kim HH, et al. Association between Oncotype DX recurrence score and dynamic contrast-enhanced MRI features in patients with estrogen receptor-positive HER2-negative invasive breast cancer[J]. Clin Imaging, 2021, 75: 131-137. DOI: 10.1016/j.clinimag.2021.01.021.
[13]
Schmitz AM, Loo CE, Wesseling J, et al. Association between rim enhancement of breast cancer on dynamic contrast-enhanced MRI and patient outcome: impact of subtype[J]. Breast Cancer Res Treat, 2014, 148(3): 541-551. DOI: 10.1007/s10549-014-3170-9.
[14]
Bitencourt AGV, Eugênio DSG, Souza JA, et al. Prognostic significance of preoperative MRI findings in young patients with breast cancer[J]. Sci Rep, 2019, 9(1): 3106. DOI: 10.1038/s41598-019-39629-w.
[15]
Koh J, Park AY, Ko KH, et al. Can enhancement types on preoperative MRI reflect prognostic factors and surgical outcomes in invasive breast cancer?[J]. Eur Radiol, 2019, 29(12): 7000-7008. DOI: 10.1007/s00330-019-06236-2.
[16]
Teifke A, Behr O, Schmidt M, et al. Dynamic MR imaging of breast lesions: correlation with microvessel distribution pattern and histologic characteristics of prognosis[J]. Radiology, 2006, 239(2): 351-360. DOI: 10.1148/radiol.2392050205.
[17]
Song SE, Shin SU, Moon HG, et al. MR imaging features associated with distant metastasis-free survival of patients with invasive breast cancer: a case-control study[J]. Breast Cancer Res Treat, 2017, 162(3): 559-569. DOI: 10.1007/s10549-017-4143-6.
[18]
Cheon H, Kim HJ, Kim TH, et al. Invasive breast cancer: prognostic value of peritumoral edema identified at preoperative MR imaging[J]. Radiology, 2018, 287(1): 68-75. DOI: 10.1148/radiol.2017171157.
[19]
Santucci D, Faiella E, Cordelli E, et al. The impact of tumor edema on T2-weighted 3T-MRI invasive breast cancer histological characterization: a pilot radiomics study[J]. Cancers, 2021, 13(18): 4635. DOI: 10.3390/cancers13184635.
[20]
Costantini M, Belli P, Distefano D, et al. Magnetic resonance imaging features in triple-negative breast cancer: comparison with luminal and HER2-overexpressing tumors[J]. Clin Breast Cancer, 2012, 12(5): 331-339. DOI: 10.1016/j.clbc.2012.07.002.
[21]
Jones EF, Sinha SP, Newitt DC, et al. MRI enhancement in stromal tissue surrounding breast tumors: association with recurrence free survival following neoadjuvant chemotherapy[J]. PLoS One, 2013, 8(5): e61969. DOI: 10.1371/journal.pone.0061969.
[22]
Luo J, Johnston BS, Kitsch AE, et al. Ductal carcinoma in situ: quantitative preoperative breast MR imaging features associated with recurrence after treatment[J]. Radiology, 2017, 285(3): 788-797. DOI: 10.1148/radiol.2017170587.
[23]
Lim Y, Ko ES, Han BK, et al. Background parenchymal enhancement on breast MRI: association with recurrence-free survival in patients with newly diagnosed invasive breast cancer[J]. Breast Cancer Res Treat, 2017, 163(3): 573-586. DOI: 10.1007/s10549-017-4217-5.
[24]
Dietzel M, Zoubi R, Vag T, et al. Association between survival in patients with primary invasive breast cancer and computer aided MRI[J]. J Magn Reson Imaging, 2013, 37(1): 146-155. DOI: 10.1002/jmri.23812.
[25]
Kim JY, Kim JJ, Hwangbo L, et al. Kinetic heterogeneity of breast cancer determined using computer-aided diagnosis of preoperative MRI scans: relationship to distant metastasis-free survival[J]. Radiology, 2020, 295(3): 517-526. DOI: 10.1148/radiol.2020192039.
[26]
Tuncbilek N, Tokatli F, Altaner S, et al. Prognostic value DCE-MRI parameters in predicting factor disease free survival and overall survival for breast cancer patients[J]. Eur J Radiol, 2012, 81(5): 863-867. DOI: 10.1016/j.ejrad.2011.02.021.
[27]
van der Velden BHM, Elias SG, Bismeijer T, et al. Complementary value of contralateral parenchymal enhancement on DCE-MRI to prognostic models and molecular assays in high-risk ER+/HER2- breast cancer[J]. Clin Cancer Res, 2017, 23(21): 6505-6515. DOI: 10.1158/1078-0432.CCR-17-0176.
[28]
Yamaguchi A, Honda M, Ishiguro H, et al. Kinetic information from dynamic contrast-enhanced MRI enables prediction of residual cancer burden and prognosis in triple-negative breast cancer: a retrospective study[J]. Sci Rep, 2021, 11(1): 10112. DOI: 10.1038/s41598-021-89380-4.
[29]
Niukkanen A, Okuma H, Sudah M, et al. Quantitative three-dimensional assessment of the pharmacokinetic parameters of intra- and peri-tumoural tissues on breast dynamic contrast-enhanced magnetic resonance imaging[J]. J Digit Imaging, 2021, 34(5): 1110-1119. DOI: 10.1007/s10278-021-00509-3.
[30]
Liu L, Mei N, Yin B, et al. Correlation of DCE-MRI perfusion parameters and molecular biology of breast infiltrating ductal carcinoma[J]. Front Oncol, 2021, 11: 561735. DOI: 10.3389/fonc.2021.561735.
[31]
Liu F, Wang M, Li HG. Role of perfusion parameters on DCE-MRI and ADC values on DWMRI for invasive ductal carcinoma at 3.0 Tesla[J]. World J Surg Oncol, 2018, 16(1): 239. DOI: 10.1186/s12957-018-1538-8.
[32]
Nagasaka K, Satake H, Ishigaki S, et al. Histogram analysis of quantitative pharmacokinetic parameters on DCE-MRI: correlations with prognostic factors and molecular subtypes in breast cancer[J]. Breast Cancer, 2019, 26(1): 113-124. DOI: 10.1007/s12282-018-0899-8.
[33]
Yi B, Kang DK, Yoon D, et al. Is there any correlation between model-based perfusion parameters and model-free parameters of time-signal intensity curve on dynamic contrast enhanced MRI in breast cancer patients?[J]. Eur Radiol, 2014, 24(5): 1089-1096. DOI: 10.1007/s00330-014-3100-6.
[34]
Song SE, Cho KR, Seo BK, et al. Intravoxel incoherent motion diffusion-weighted MRI of invasive breast cancer: correlation with prognostic factors and kinetic features acquired with computer-aided diagnosis[J]. J Magn Reson Imaging, 2019, 49(1): 118-130. DOI: 10.1002/jmri.26221.
[35]
Kim JY, Kim JJ, Kim S, et al. Diffusion tensor magnetic resonance imaging of breast cancer: associations between diffusion metrics and histological prognostic factors[J]. Eur Radiol, 2018, 28(8): 3185-3193. DOI: 10.1007/s00330-018-5429-8.
[36]
Yang ZL, Li Y, Zhan CA, et al. Evaluation of suspicious breast lesions with diffusion kurtosis MR imaging and connection with prognostic factors[J]. Eur J Radiol, 2021, 145: 110014. DOI: 10.1016/j.ejrad.2021.110014.
[37]
Kim JY, Kim JJ, Hwangbo L, et al. Diffusion-weighted imaging of invasive breast cancer: relationship to distant metastasis-free survival[J]. Radiology, 2019, 291(2): 300-307. DOI: 10.1148/radiol.2019181706.
[38]
Okuma H, Sudah M, Kettunen T, et al. Peritumor to tumor apparent diffusion coefficient ratio is associated with biologically more aggressive breast cancer features and correlates with the prognostication tools[J]. PLoS One, 2020, 15(6): e0235278. DOI: 10.1371/journal.pone.0235278.
[39]
Kettunen T, Okuma H, Auvinen P, et al. Peritumoral ADC values in breast cancer: region of interest selection, associations with hyaluronan intensity, and prognostic significance[J]. Eur Radiol, 2020, 30(1): 38-46. DOI: 10.1007/s00330-019-06361-y.
[40]
Baltzer PAT, Yang F, Dietzel M, et al. Sensitivity and specificity of unilateral edema on T2w-TSE sequences in MR-Mammography considering 974 histologically verified lesions[J]. Breast J, 2010, 16(3): 233-239. DOI: 10.1111/j.1524-4741.2010.00915.x.
[41]
Iima M, Reynaud O, Tsurugizawa T, et al. Characterization of glioma microcirculation and tissue features using intravoxel incoherent motion magnetic resonance imaging in a rat brain model[J]. Invest Radiol, 2014, 49(7): 485-490. DOI: 10.1097/RLI.0000000000000040.
[42]
Lee YJ, Kim SH, Kang BJ, et al. Intravoxel incoherent motion (IVIM)-derived parameters in diffusion-weighted MRI: associations with prognostic factors in invasive ductal carcinoma[J]. J Magn Reson Imaging, 2017, 45(5): 1394-1406. DOI: 10.1002/jmri.25514.
[43]
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. DOI: 10.1259/bjr.20160140.
[44]
Ozal ST, Inci E, Gemici AA, et al. Can 3.0 Tesla diffusion tensor Imaging parameters be prognostic indicators in breast cancer?[J]. Clin Imaging, 2018, 51: 240-247. DOI: 10.1016/j.clinimag.2018.03.022.
[45]
Onaygil C, Kaya, Ugurlu MU, et al. Diagnostic performance of diffusion tensor imaging parameters in breast cancer and correlation with the prognostic factors[J]. J Magn Reson Imaging, 2017, 45(3): 660-672. DOI: 10.1002/jmri.25481.
[46]
Huang Y, Lin Y, Hu W, et al. Diffusion kurtosis at 3.0T as an in vivo imaging marker for breast cancer characterization: correlation with prognostic factors[J]. J Magn Reson Imaging, 2019, 49(3): 845-856. DOI: 10.1002/jmri.26249.
[47]
Sun K, Chen XS, Chai WM, et al. Breast cancer: diffusion kurtosis MR imaging-diagnostic accuracy and correlation with clinical-pathologic factors[J]. Radiology, 2015, 277(1): 46-55. DOI: 10.1148/radiol.15141625.
[48]
Suo ST, Cheng F, Cao MQ, et al. Multiparametric diffusion-weighted imaging in breast lesions: association with pathologic diagnosis and prognostic factors[J]. J Magn Reson Imaging, 2017, 46(3): 740-750. DOI: 10.1002/jmri.25612.
[49]
Chitalia RD, Rowland J, McDonald ES, 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.
[50]
Kim JH, Ko ES, Lim Y, et al. Breast cancer heterogeneity: MR imaging texture analysis and survival outcomes[J]. Radiology, 2017, 282(3): 665-675. DOI: 10.1148/radiol.2016160261.
[51]
Gatenby RA, Grove O, Gillies RJ. Quantitative imaging in cancer evolution and ecology[J]. Radiology, 2013, 269(1): 8-15. DOI: 10.1148/radiol.13122697.
[52]
Cho HH, Kim H, Nam SY, et al. Measurement of perfusion heterogeneity within tumor habitats on magnetic resonance imaging and its association with prognosis in breast cancer patients[J]. Cancers (Basel), 2022, 14(8): 1858. DOI: 10.3390/cancers14081858.
[53]
Yoon HJ, Kim Y, Chung J, et al. Predicting neo-adjuvant chemotherapy response and progression-free survival of locally advanced breast cancer using textural features of intratumoral heterogeneity on F-18 FDG PET/CT and diffusion-weighted MR imaging[J]. Breast J, 2019, 25(3): 373-380. DOI: 10.1111/tbj.13032.
[54]
Song SE, Cho KR, Cho Y, et al. Machine learning with multiparametric breast MRI for prediction of Ki-67 and histologic grade in early-stage luminal breast cancer[J]. Eur Radiol, 2022, 32(2): 853-863. DOI: 10.1007/s00330-021-08127-x.
[55]
Rawat W, Wang ZH. Deep convolutional neural networks for image classification: a comprehensive review[J]. Neural Comput, 2017, 29(9): 2352-2449. DOI: 10.1162/NECO_a_00990.
[56]
Liu GB, Mitra D, Jones EF, et al. Mask-guided convolutional neural network for breast tumor prognostic outcome prediction on 3D DCE-MR images[J]. J Digit Imaging, 2021, 34(3): 630-636. DOI: 10.1007/s10278-021-00449-y.
[57]
Li J, Fan M, Zhang J, et al. Discriminating between benign and malignant breast tumors using 3D convolutional neural network in dynamic contrast enhanced-MR images[C]//Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications. Orlando, Florida, USA: SPIE, 2017, 10138: 44-51. DOI: 10.1117/12.2254716.
[58]
Nguyen S, Polat D, Karbasi P, et al. Preoperative prediction of lymph node metastasis from clinical DCE MRI of the primary breast tumor using a 4D CNN[J]. Med Image Comput Comput Assist Interv, 2020, 12262: 326-334. DOI: 10.1007/978-3-030-59713-9_32.
[59]
Banegas-Luna AJ, Peña-García J, Iftene A, et al. Towards the interpretability of machine learning predictions for medical applications targeting personalised therapies: a cancer case survey[J]. Int J Mol Sci, 2021, 22(9): 4394. DOI: 10.3390/ijms22094394.
[60]
Ou WC, Polat D, Dogan BE. Deep learning in breast radiology: current progress and future directions[J]. Eur Radiol, 2021, 31(7): 4872-4885. DOI: 10.1007/s00330-020-07640-9.

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