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Summary of mammography of breast MRI and progress in its prognostic application
LI Zhongyuan  OUYANG Aimei 

LI Z Y, OUYANG A M. Summary of mammography of breast MRI and progress in its prognostic application[J]. Chin J Magn Reson Imaging, 2023, 14(9): 141-147. DOI:10.12015/issn.1674-8034.2023.09.026.


[Abstract] Breast cancer is the most prevalent malignant tumor in women. MRI has played a great role in the diagnosis, treatment, and prognosis of breast cancer due to its good tissue resolution and absence of radiation, and a lot of research has been conducted. With the increasing availability of high-precision diagnosis and treatment data, breast MRI imaging histology has shown the potential to be used more and more. The study searched PubMed, China Knowledge Network, and Wanfang database for literature on MRI imaging histology in breast cancer prognosis research, and summarized and analyzed the software used, the research process, and the research results. This paper focuses on the research methods, such as platform selection, image segmentation, feature extraction, verification queue selection, and image selection, used in MRI imaging histology in breast cancer prognosis and the prognostic application of combined models, to provide valuable imaging information to clinicians focusing on the prognosis of breast cancer patients and to assist in the precision treatment of breast cancer.
[Keywords] breast cancer；radiomics；prognosis；radiology；magnetic resonance imaging；feature extraction；joint model

LI Zhongyuan1   OUYANG Aimei2*  

1 School of Medical Imaging, Weifang Medical University, Weifang 261053, China

2 Department of Imaging, Central Hospital Affiliated to Shandong First Medical University, Jinan 250014, China

Corresponding author: Ouyang AM, E-mail: 13370582510@163.com.

Conflicts of interest   None.

ACKNOWLEDGMENTS Jinan City Science and Technology Innovation Development Plan (No. 202019036).
Received  2022-10-09
Accepted  2023-07-21
DOI: 10.12015/issn.1674-8034.2023.09.026
LI Z Y, OUYANG A M. Summary of mammography of breast MRI and progress in its prognostic application[J]. Chin J Magn Reson Imaging, 2023, 14(9): 141-147. DOI:10.12015/issn.1674-8034.2023.09.026.

[1]
SIEGEL R L, MILLER K D, JEMAL A. Cancer statistics, 2017[J]. CA Cancer J Clin, 2017, 67(1): 7-30. DOI: 10.3322/caac.21387. DOI:
[2]
SIEGEL R, MILLER K, FUCHS H E, et al. Cancer statistics, 2022[J]. CA Cancer J Clin, 2022,72(1): 7-33. DOI: 10.3322/caac.21708.
[3]
KANG Z M, YANG D L, SUN J N. Progress of breast MRI image texture analysis in breast cancer[J]. Pract Oncol J, 2022, 36(5): 472-476. DOI: 10.11904/j.issn.1002-3070.2022.05.015.
[4]
AERTS H J W L, VELAZQUEZ E R, LEIJENAAR R T H, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach[J/OL]. Nat Commun, 2014, 5: 4006 [2022-10-08]. https://pubmed.ncbi.nlm.nih.gov/24892406/. DOI: 10.1038/ncomms5006.
[5]
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.
[6]
LIMKIN E J, SUN R, DERCLE L, et al. 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.
[7]
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.
[8]
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]. Front Bioeng Biotechnol, 2021, 9: 662749 [2022-11-26]. https://pubmed.ncbi.nlm.nih.gov/34295877/. DOI: 10.3389/fbioe.2021.662749.
[9]
WANG X Y, XIE T S, LUO J R, et al. Radiomics predicts the prognosis of patients with locally advanced breast cancer by reflecting the heterogeneity of tumor cells and the tumor microenvironment[J/OL]. Breast Cancer Res, 2022, 24(1): 20 [2022-09-30]. https://doi.org/10.186/s13058-022-01516-0. DOI: 10.1186/s13058-022-01516-0.
[10]
HAN X R, CAO W T, WU L, et al. Radiomics assessment of the tumor immune microenvironment to predict outcomes in breast cancer[J/OL]. Front Immunol, 2021, 12: 773581 [2022-09-30]. https://www.frontiersin.org/articles/10.3389/fimmu.2021.773581. DOI: 10.3389/fimmu.2021.773581.
[11]
LI H, ZHU Y T, BURNSIDE E S, et al. MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, oncotype DX, and PAM50 gene assays[J]. Radiology, 2016, 281(2): 382-391. DOI: 10.1148/radiol.2016152110.
[12]
CHO H H, KIM H, NAM S Y, et al. Measurement of perfusion heterogeneity within tumor habitats on magnetic resonance imaging and its association with prognosis in breast cancer patients[J/OL]. Cancers, 2022, 14(8): 1858 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/35454768/. DOI: 10.3390/cancers14081858.
[13]
WAN T, BLOCH B N, PLECHA D, et al. A radio-genomics approach for identifying high risk estrogen receptor-positive breast cancers on DCE-MRI: preliminary results in predicting OncotypeDX risk scores[J/OL]. Sci Rep, 2016, 6: 21394 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/26887643/. DOI: 10.1038/srep21394.
[14]
MA M M, GAN L Y, LIU Y H, et al. Radiomics features based on automatic segmented MRI images: prognostic biomarkers for triple-negative breast cancer treated with neoadjuvant chemotherapy[J/OL]. Eur J Radiol, 2022, 146: 110095 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/34890936/. DOI: 10.1016/j.ejrad.2021.110095.
[15]
DORAN S J, KUMAR S, ORTON M, et al. "Real-world" radiomics from multi-vendor MRI: an original retrospective study on the prediction of nodal status and disease survival in breast cancer, as an exemplar to promote discussion of the wider issues[J/OL]. Cancer Imaging, 2021, 21(1): 37 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/34016188/. DOI: 10.1186/s40644-021-00406-6.
[16]
MONGKOLWAT P, CHANNIN D S, KLEPER V, et al. Informatics in radiology: an open-source and open-access cancer biomedical informatics grid annotation and image markup template builder[J]. Radiographics, 2012, 32(4): 1223-1232. DOI: 10.1148/rg.324115080.
[17]
HUANG S Y, FRANC B L, HARNISH R J, et al. Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis[J/OL]. NPJ Breast Cancer, 2018, 4: 24 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/30131973/. DOI: 10.1038/s41523-018-0078-2.
[18]
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.
[19]
XIA B Q, WANG H, WANG Z, et al. A combined nomogram model to predict disease-free survival in triple-negative breast cancer patients with neoadjuvant chemotherapy[J/OL]. Front Genet, 2021, 12: 783513 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/34868273/. DOI: 10.3389/fgene.2021.783513.
[20]
BHARDWAJ D, DASGUPTA A, DICENZO D, et al. Early changes in quantitative ultrasound imaging parameters during neoadjuvant chemotherapy to predict recurrence in patients with locally advanced breast cancer[J/OL]. Cancers, 2022, 14(5): 1247 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/35267555/. DOI: 10.3390/cancers14051247.
[21]
LIU Y. Research on the method of image texture feature extraction and classification[D]. Wuhan: Huazhong University of Science and Technology, 2013. DOI: 10.7666/d.D413840.
[22]
MALLAT S G. A theory for multiresolution signal decomposition: the wavelet representation[J]. IEEE Trans Pattern Anal Mach Intell, 1989, 11(7): 674-693. DOI: 10.1109/34.192463.
[23]
WANG C. Radiomics based tumor lesion segmentation and prognosis analysis in breast cancer[D]. Harbin: Harbin University of Science and Technology, 2017. DOI: 10.7666/d.Y3187159.
[24]
ZHANG L, JIANG X, XIE X, et al. The impact of preoperative radiomics signature on the survival of breast cancer patients with residual tumors after NAC[J/OL]. Front Oncol, 2020, 10: 523327 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/33614472/. DOI: 10.3389/fonc.2020.523327.
[25]
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.
[26]
JO J H, CHUNG H W, SO Y, et al. FDG PET/CT to predict recurrence of early breast invasive ductal carcinoma[J/OL]. Diagnostics, 2022, 12(3): 694 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/35328247/. DOI: 10.3390/diagnostics12030694.
[27]
YOON H J, 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.
[28]
MOLINA-GARCÍA D, GARCÍA-VICENTE A M, PÉREZ-BETETA J, et al. Intratumoral heterogeneity in 18F-FDG PET/CT by textural analysis in breast cancer as a predictive and prognostic subrogate[J]. Ann Nucl Med, 2018, 32(6): 379-388. DOI: 10.1007/s12149-018-1253-0.
[29]
NICOLASJILWAN M, HU Y, YAN C H, et al. Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients[J]. J De Neuroradiol, 2015, 42(4): 212-221. DOI: 10.1016/j.neurad.2014.02.006.
[30]
PARMAR C, GROSSMANN P, BUSSINK J, et al. Machine learning methods for quantitative radiomic biomarkers[J/OL]. Sci Rep, 2015, 5: 13087 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/26278466/. DOI: 10.1038/srep13087.
[31]
XIA B Q, LI C P, QIAN Z X, et al. Radiomics based on machine learning in predicting the long-term prognosis for triple-negative breast cancer after neoadjuvant chemotherapy[J]. Chin J Radiol, 2021, 55(10): 1059-1064.
[32]
KOH J, LEE E, HAN K, et al. Three-dimensional radiomics of triple-negative breast cancer: prediction of systemic recurrence[J/OL]. Sci Rep, 2020, 10(1): 2976 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/32076078/. DOI: 10.1038/s41598-020-59923-2.
[33]
YOUSAF T, DERVENOULAS G, POLITIS M. Advances in MRI methodology[J/OL]. Int Rev Neurobiol, 2018, 141: 31-76 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/30314602/. DOI: 10.1016/bs.irn.2018.08.008.
[34]
ZHANG L L, ZHANG L, CHEN T F, et al. Features of cerebrovascular lesions in patients with posterior circulation ischemia[J]. J Clin Neurol, 2011, 24(5):375-377.
[35]
KIM J H, KO E S, 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.
[36]
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.
[37]
ZHAO L L, PIAO C H. Analysis of the application value of MRI multi-sequence imaging technology in the diagnosis and differentiation of breast cancer[J]. China Med Device Inf, 2021, 27(12): 115-116. DOI: 10.15971/j.cnki.cmdi.2021.12.053.
[38]
CHOI B B. Associations between apparent diffusion coefficient values and the prognostic factors of breast cancer[J]. J Comput Assist Tomogr, 2019, 43(6): 931-936. DOI: 10.1097/RCT.0000000000000936.
[39]
TEZCAN Ş, USLU N, ÖZTÜRK F U, et al. Diffusion-weighted imaging of breast cancer: correlation of the apparent diffusion coefficient value with pathologic prognostic factors[J]. Eur J Breast Health, 2019, 15(4): 262-267. DOI: 10.5152/ejbh.2019.4860.
[40]
RABASCO P, CAIVANO R, SIMEON V, et al. Can diffusion-weighted imaging and related apparent diffusion coefficient be a prognostic value in women with breast cancer?[J]. Cancer Invest, 2017, 35(2): 92-99. DOI: 10.1080/07357907.2016.1267740.
[41]
ZHAO S H, SHAO G R, CHEN P P, et al. Diagnostic performance of minimum apparent diffusion coefficient value in differentiating the invasive breast cancer and ductal carcinoma in situ[J]. J Cancer Res Ther, 2019, 15(4): 871-875. DOI: 10.4103/jcrt.JCRT_607_18.
[42]
FENG W, GAO Y, LU X R, et al. Correlation between molecular prognostic factors and magnetic resonance imaging intravoxel incoherent motion histogram parameters in breast cancer[J/OL]. Magn Reson Imaging, 2022, 85: 262-270 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/34740800/. DOI: 10.1016/j.mri.2021.10.027.
[43]
SONG S E, CHO K R, SEO B K, 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.
[44]
BIAN X Q, DU S Y, ZHANG L N. Application and prospect of preoperative MRI in predicting the prognosis of breast cancer[J]. Chin J Magn Reson Imag, 2022, 13(6): 147-150. DOI: 10.12015/issn.1674-8034.2022.06.031.
[45]
WANG Y, LI H P, LI C Y, et al. Advances in diagnosis of breast cancer by multi-modal magnetic resonance imaging[J]. Med Recapitul, 2020, 26(10): 2004-2008. DOI: 10.3969/j.issn.1006-2084.2020.10.025.
[46]
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.
[47]
ZHANG J J, WANG G H, REN J L, et al. Multiparametric MRI-based radiomics nomogram for preoperative prediction of lymphovascular invasion and clinical outcomes in patients with breast invasive ductal carcinoma[J]. Eur Radiol, 2022, 32(6): 4079-4089. DOI: 10.1007/s00330-021-08504-6.
[48]
HEI H H, FENG Z B, WANG M X. Predictive value of pre-treatment assessment based on multimodal MRI nomogram for postoperative recurrence of breast cancer[J]. J Clin Radiol, 2022, 41(7): 1276-1281. DOI: 10.13437/j.cnki.jcr.2022.07.009.
[49]
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://pubmed.ncbi.nlm.nih.gov/32111957/. DOI: 10.1038/s41598-020-60822-9.
[50]
WANG H Y, LI X K, YUAN Y, et al. Association of machine learning ultrasound radiomics and disease outcome in triple negative breast cancer[J]. Am J Cancer Res, 2022, 12(1): 152-164.
[51]
XIONG L, CHEN H L, TANG X F, et al. Ultrasound-based radiomics analysis for predicting disease-free survival of invasive breast cancer[J/OL]. Front Oncol, 2021, 11: 621993 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/33996546/. DOI: 10.3389/fonc.2021.621993.
[52]
BARETTA Z, MOCELLIN S, GOLDIN E, et al. Effect of BRCA germline mutations on breast cancer prognosis: a systematic review and meta-analysis[J/OL]. Medicine, 2016, 95(40): e4975 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/27749552/. DOI: 10.1097/MD.0000000000004975.
[53]
CHEN W X, CHENG L, XU L Y, et al. Bioinformatics analysis of prognostic value of TRIM13 gene in breast cancer[J/OL]. Biosci Rep, 2019, 39(3): BSR20190285 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/30837324/. DOI: 10.1042/BSR20190285.
[54]
WANG Q Y, ZHAO S, GAN L, et al. Bioinformatics analysis of prognostic value of PITX1 gene in breast cancer[J/OL]. Biosci Rep, 2020, 40(9): BSR20202537 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/32830857/. DOI: 10.1042/BSR20202537.
[55]
ZHANG M D, CHEN H L, WANG M L, et al. Bioinformatics analysis of prognostic significance of COL10A1 in breast cancer[J/OL]. Biosci Rep, 2020, 40(2): BSR20193286 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/32043519/. DOI: 10.1042/BSR20193286.
[56]
CHEN W Q, YANG S J, XU W X, et al. Bioinformatics analysis revealing prognostic significance of TIMP2 gene in breast cancer[J/OL]. Medicine, 2021, 100(42): e27489 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/34678879/. DOI: 10.1097/MD.0000000000027489.
[57]
ZHANG Y J, YU C R. Prognostic characterization of OAS1/OAS2/OAS3/OASL in breast cancer[J/OL]. BMC Cancer, 2020, 20(1): 575 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/32560641/. DOI: 10.1186/s12885-020-07034-6.
[58]
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: preliminary results for correlation with Oncotype DX recurrence scores[J]. Medicine, 2019, 98(23): e15871. DOI: 10.1097/MD.0000000000015871.
[59]
LIU J J, WANG X C, HU M S, et al. Development of an ultrasound-based radiomics nomogram to preoperatively predict Ki-67 expression level in patients with breast cancer[J/OL]. Front Oncol, 2022, 12: 963925 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/36046035/. DOI: 10.3389/fonc.2022.963925.
[60]
JIANG T, SONG J D, WANG X Y, et al. Intratumoral and peritumoral analysis of mammography, tomosynthesis, and multiparametric MRI for predicting ki-67 level in breast cancer: a radiomics-based study[J]. Mol Imaging Biol, 2022, 24(4): 550-559. DOI: 10.1007/s11307-021-01695-w.
[61]
CUI H, ZHANG D D, PENG F H, et al. Identifying ultrasound features of positive expression of Ki67 and P53 in breast cancer using radiomics[J/OL]. Asia Pac J Clin Oncol, 2021, 17(5): e176-e184 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/32779399/. DOI: 10.1111/ajco.13397.
[62]
ZANFARDINO M, CASTALDO R, PANE K, et al. MuSA: a graphical user interface for multi-OMICs data integration in radiogenomic studies[J/OL]. Sci Rep, 2021, 11(1): 1550 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/33452365/. DOI: 10.1038/s41598-021-81200-z.
[63]
PARK J E, KIM D, KIM H S, et al. Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement[J]. Eur Radiol, 2020, 30(1): 523-536. DOI: 10.1007/s00330-019-06360-z.
[64]
TAGLIAFICO A S, BIGNOTTI B, ROSSI F, et al. Local recurrence of soft tissue sarcoma: a radiomic analysis[J]. Radiol Oncol, 2019, 53(3): 300-306. DOI: 10.2478/raon-2019-0041.
[65]
MAO N, YIN P, ZHANG H C, et al. Mammography-based radiomics for predicting the risk of breast cancer recurrence: a multicenter study[J/OL]. Br J Radiol, 2021, 94(1127): 20210348 [2022-09-30]. https://pubmed.ncbi.nlm.nih.gov/34520235/. DOI: 10.1259/bjr.20210348.

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