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Correlation between MRI radiomics and neovascularization of breast cancer
XU Kepei  FANG Xiaozheng  LIN Yi  XU Maosheng  WANG Shiwei  ZHANG Ruixin 

Cite this article as: Xu KP, Fang XZ, Lin Y, et al. Correlation between MRI radiomics and neovascularization of breast cancer[J]. Chin J Magn Reson Imaging, 2022, 13(8): 146-149. DOI:10.12015/issn.1674-8034.2022.08.033.

[Abstract] Breast cancer is a malignancy that seriously harms women's health worldwide. The high heterogeneity of breast cancer makes it difficult to accurately assess it, which is not conducive to the practice and development of its personalized treatment. Tumor angiogenesis is involved in the formation of tumor heterogeneity and plays a key role in breast cancer treatment response, prognosis and recurrence. How to better use tumor angiogenesis to evaluate the occurrence and development of breast cancer and promote precise treatment is an urgent clinical problem to be solved. As a new research field, breast MRI radiomics has the advantages of high-throughput extraction and quantitative analysis, which can not only non-invasively extract biologically relevant information of tumors and their new blood vessels, but also further realize the characterization of tumor angiogenesis-related factors and pathways at the molecular and gene levels. Therefore, breast MRI radiomics has great potential in evaluating tumor angiogenesis. In this paper, the relationship between breast MRI radiomics and the occurrence and development of breast cancer and tumor neovascularization are expounded, in order to provide new ideas for clinical accurate diagnosis and treatment.
[Keywords] tumor neovascularization;vascular factors;radiomics;magnetic resonance imaging;breast cancer

XU Kepei1   FANG Xiaozheng1   LIN Yi1   XU Maosheng1, 2   WANG Shiwei1, 2   ZHANG Ruixin1, 2*  

1 First Clinical School of Medicine, Zhengjiang Chinese Medical University, Hangzhou 310053, China

2 Department of Radiology, First Affiliated Hospital of Zhengjiang Chinese Medical University, Hangzhou 310006, China

Zhang RX, E-mail:

Conflicts of interest   None.

Received  2022-04-25
Accepted  2022-08-10
DOI: 10.12015/issn.1674-8034.2022.08.033
Cite this article as: Xu KP, Fang XZ, Lin Y, et al. Correlation between MRI radiomics and neovascularization of breast cancer[J]. Chin J Magn Reson Imaging, 2022, 13(8): 146-149.DOI:10.12015/issn.1674-8034.2022.08.033

Sung H, Ferlay J, Siegel RL, 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.
Groza IM, Braicu C, Jurj A, et al. Cancer-associated stemness and epithelial-to-mesenchymal transition signatures related to breast invasive carcinoma prognostic[J/OL]. Cancers, 2020, 12(10) [2022-04-25]. DOI: 10.3390/cancers12103053.
Wilson MM, Weinberg RA, Lees JA, et al. Emerging mechanisms by which EMT programs control stemness[J]. Trends Cancer, 2020, 6(9): 775-780. DOI: 10.1016/j.trecan.2020.03.011.
Bennani-Baiti B, Pinker K, Zimmermann M, et al. Non-invasive assessment of hypoxia and neovascularization with MRI for identification of aggressive breast cancer[J]. Cancers (Basel), 2020 [2022-04-17]. DOI: 10.3390/cancers12082024.
Vaupel P. Hypoxia and aggressive tumor phenotype: implications for therapy and prognosis[J]. Oncologist, 2008, 13(Suppl 3): 21-26. DOI: 10.1634/theoncologist.13-S3-21.
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.
Liu ZY, Wang S, Dong D, et al. The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges[J]. Theranostics, 2019, 9(5): 1303-1322. DOI: 10.7150/thno.30309.
Guiot J, Vaidyanathan A, Deprez L, et al. A review in radiomics: making personalized medicine a reality via routine imaging[J]. Med Res Rev, 2022, 42(1): 426-440. DOI: 10.1002/med.21846.
Zhang ML, Liu JQ, Liu G, et al. Anti-vascular endothelial growth factor therapy in breast cancer: molecular pathway, potential targets, and current treatment strategies[J]. Cancer Lett, 2021, 520: 422-433. DOI: 10.1016/j.canlet.2021.08.005.
Gillies RJ, Brown JS, Anderson ARA, et al. Eco-evolutionary causes and consequences of temporal changes in intratumoural blood flow[J]. Nat Rev Cancer, 2018, 18(9): 576-585. DOI: 10.1038/s41568-018-0030-7.
Craft PS, Harris AL. Clinical prognostic significance of tumour angiogenesis[J]. Ann Oncol, 1994, 5(4): 305-311. DOI: 10.1093/oxfordjournals.annonc.a058829.
de Heer EC, Jalving M, Harris AL. HIFs, angiogenesis, and metabolism: elusive enemies in breast cancer[J]. J Clin Invest, 2020, 130(10): 5074-5087. DOI: 10.1172/JCI137552.
Niu YL, Bao L, Chen Y, et al. HIF2-induced long noncoding RNA RAB11B-AS1 promotes hypoxia-mediated angiogenesis and breast cancer metastasis[J]. Cancer Res, 2020, 80(5): 964-975. DOI: 10.1158/0008-5472.CAN-19-1532.
Gong K, Jiao JY, Xu CQ, et al. The targetable nanoparticle BAF312@cRGD-CaP-NP represses tumor growth and angiogenesis by downregulating the S1PR1/P-STAT3/VEGFA axis in triple-negative breast cancer[J/OL]. J Nanobiotechnology, 2021, 19(1) [2022-04-25]. DOI: 10.1186/s12951-021-00904-6.
Kong DG, Zhou HB, Neelakantan D, et al. VEGF-C mediates tumor growth and metastasis through promoting EMT-epithelial breast cancer cell crosstalk[J]. Oncogene, 2021, 40(5): 964-979. DOI: 10.1038/s41388-020-01539-x.
Shen Q, Reedijk M. Notch signaling and the breast cancer microenvironment[J]. Adv Exp Med Biol, 2021, 1287: 183-200. DOI: 10.1007/978-3-030-55031-8_12.
Cao JH, Liu XH, Yang Y, et al. Decylubiquinone suppresses breast cancer growth and metastasis by inhibiting angiogenesis via the ROS/p53/BAI1 signaling pathway[J]. Angiogenesis, 2020, 23(3): 325-338. DOI: 10.1007/s10456-020-09707-z.
Weidner N, Semple JP, Welch WR, et al. Tumor angiogenesis and metastasis: correlation in invasive breast carcinoma[J]. N Engl J Med, 1991, 324(1): 1-8. DOI: 10.1056/NEJM199101033240101.
Wei LJ, Zhu SS, Li MH, et al. High indoleamine 2, 3-dioxygenase is correlated with microvessel density and worse prognosis in breast cancer[J/OL]. Front Immunol, 2018, 9 [2022-04-25]. DOI: 10.3389/fimmu.2018.00724.
Qian XL, Xu P, Zhang YQ, et al. Increased number of intratumoral IL-17+ cells, a harbinger of the adverse prognosis of triple-negative breast cancer[J]. Breast Cancer Res Treat, 2020, 180(2): 311-319. DOI: 10.1007/s10549-020-05540-6.
Wang RX, Chen S, Huang L, et al. Monitoring serum VEGF in neoadjuvant chemotherapy for patients with triple-negative breast cancer: a new strategy for early prediction of treatment response and patient survival[J]. Oncologist, 2019, 24(6): 753-761. DOI: 10.1634/theoncologist.2017-0602.
Kiso M, Tanaka S, Saji S, et al. Long isoform of VEGF stimulates cell migration of breast cancer by filopodia formation via NRP1/ARHGAP17/Cdc42 regulatory network[J]. Int J Cancer, 2018, 143(11): 2905-2918. DOI: 10.1002/ijc.31645.
Song Y, Zeng SS, Zheng GP, et al. FOXO3a-driven miRNA signatures suppresses VEGF-A/NRP1 signaling and breast cancer metastasis[J]. Oncogene, 2021, 40(4): 777-790. DOI: 10.1038/s41388-020-01562-y.
Wan XY, Guan SD, Hou YX, et al. FOSL2 promotes VEGF-independent angiogenesis by transcriptionnally activating Wnt5a in breast cancer-associated fibroblasts[J]. Theranostics, 2021, 11(10): 4975-4991. DOI: 10.7150/thno.55074.
Eelen G, Treps L, Li XR, et al. Basic and therapeutic aspects of angiogenesis updated[J]. Circ Res, 2020, 127(2): 310-329. DOI: 10.1161/CIRCRESAHA.120.316851.
Zhong MH, Yang ZQ, Yao C, et al. Correlation between quantitative DCE-MRI parameters, ADC values and the expressions of p53 and CK56 in breast cancer[J]. Int J Med Radiol, 2021, 44(4): 403-407. DOI: 10.19300/j.2021.L18779.
Wang J, Tang WW, Tian ZF, et al. Correlation between DCE-MRI parameters/ADC and pathological molecular prognostic markers of breast cancer[J]. Chin J Magn Reson Imaging, 2021, 12(3): 76-79. DOI: 10.12015/issn.1674-8034.2021.03.017.
Qin K, Ye F, Gu GY, et al. Correlation of DCE-MRI parameters and expression of miR-27 and miR-155 with breast cancer[J]. Imaging Sci Photochem, 2022, 40(2): 263-268.
Kim JJ, Kim JY, Suh HB, et al. Characterization of breast cancer subtypes based on quantitative assessment of intratumoral heterogeneity using dynamic contrast-enhanced and diffusion-weighted magnetic resonance imaging[J]. Eur Radiol, 2022, 32(2): 822-833. DOI: 10.1007/s00330-021-08166-4.
Onishi N, Sadinski M, Hughes MC, et al. Ultrafast dynamic contrast-enhanced breast MRI may generate prognostic imaging markers of breast cancer[J/OL]. Breast Cancer Res, 2020, 22(1) [2022-04-25]. DOI: 10.1186/s13058-020-01292-9.
Keil VC, Gielen GH, Pintea B, et al. DCE-MRI in glioma, infiltration zone and healthy brain to assess angiogenesis: a biopsy study[J]. Clin Neuroradiol, 2021, 31(4): 1049-1058. DOI: 10.1007/s00062-021-01015-3.
Pradillo JM, Hernández-Jiménez M, Fernández-Valle ME, et al. Influence of metabolic syndrome on post-stroke outcome, angiogenesis and vascular function in old rats determined by dynamic contrast enhanced MRI[J]. J Cereb Blood Flow Metab, 2021, 41(7): 1692-1706. DOI: 10.1177/0271678X20976412.
Parekh VS, Jacobs MA. Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI[J/OL]. NPJ Breast Cancer, 2017, 3 [2022-04-25]. DOI: 10.1038/s41523-017-0045-3.
Fan M, Cheng H, Zhang P, et al. DCE-MRI texture analysis with tumor subregion partitioning for predicting Ki-67 status of estrogen receptor-positive breast cancers[J]. J Magn Reson Imaging, 2018, 48(1): 237-247. DOI: 10.1002/jmri.25921.
Zhang B, Song LR, Yin JD. Texture analysis of DCE-MRI intratumoral subregions to identify benign and malignant breast tumors[J/OL]. Front Oncol, 2021 [2022-04-19]. DOI: 10.3389/fonc.2021.688182.
Xu H, Liu JK, 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.
Zhou JJ, Zhang Y, Chang KT, et al. Diagnosis of benign and malignant breast lesions on DCE-MRI by using radiomics and deep learning with consideration of peritumor tissue[J]. J Magn Reson Imaging, 2020, 51(3): 798-809. DOI: 10.1002/jmri.26981.
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.
Ochoa-Albiztegui RE, Sevilimedu V, Horvat JV, et al. Pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging at 7T for breast cancer diagnosis and characterization[J/OL]. Cancers (Basel), 2020 [2022-04-19]. DOI: 10.3390/cancers12123763.
Kang SR, Kim HW, Kim HS. Evaluating the relationship between dynamic contrast-enhanced MRI (DCE-MRI) parameters and pathological characteristics in breast cancer[J]. J Magn Reson Imaging, 2020, 52(5): 1360-1373. DOI: 10.1002/jmri.27241.
Wu J, Cao GH, Sun XL, et al. Intratumoral spatial heterogeneity at perfusion MR imaging predicts recurrence-free survival in locally advanced breast cancer treated with neoadjuvant chemotherapy[J]. Radiology, 2018, 288(1): 26-35. DOI: 10.1148/radiol.2018172462.
Park HS, Lee KS, Seo BK, et al. Machine learning models that integrate tumor texture and perfusion characteristics using low-dose breast computed tomography are promising for predicting histological biomarkers and treatment failure in breast cancer patients[J/OL]. Cancers (Basel), 2021, 13(23) [2022-04-25]. DOI: 10.3390/cancers13236013.
Xiao J, Rahbar H, Hippe DS, et al. Dynamic contrast-enhanced breast MRI features correlate with invasive breast cancer angiogenesis[J/OL]. NPJ Breast Cancer, 2021, 7(1) [2022-04-25]. DOI: 10.1038/s41523-021-00247-3.
Belfiore A, Malaguarnera R, Nicolosi ML, et al. A novel functional crosstalk between DDR1 and the IGF axis and its relevance for breast cancer[J]. Cell Adh Migr, 2018, 12(4): 305-314. DOI: 10.1080/19336918.2018.1445953.
de Francesco EM, Sims AH, Maggiolini M, et al. GPER mediates the angiocrine actions induced by IGF1 through the HIF-1α/VEGF pathway in the breast tumor microenvironment[J/OL]. Breast Cancer Res, 2017, 19(1) [2022-04-25]. DOI: 10.1186/s13058-017-0923-5.
Zhang Q, Li TF, Wang ZC, et al. lncRNA NR2F1-AS1 promotes breast cancer angiogenesis through activating IGF-1/IGF-1R/ERK pathway[J]. J Cell Mol Med, 2020, 24(14): 8236-8247. DOI: 10.1111/jcmm.15499.
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.
Lee JY, Lee KS, Seo BK, 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.
Fan M, Cui YJ, You C, et al. Radiogenomic signatures of oncotype DX recurrence score enable prediction of survival in estrogen receptor-positive breast cancer: a multicohort study[J]. Radiology, 2022, 302(3): 516-524. DOI: 10.1148/radiol.2021210738.
Bismeijer T, van der Velden BHM, Canisius S, et al. Radiogenomic analysis of breast cancer by linking MRI phenotypes with tumor gene expression[J]. Radiology, 2020, 296(2): 277-287. DOI: 10.1148/radiol.2020191453.

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