Share:
Share this content in WeChat
X
Review
Research progress in the preoperative evaluation of lymphovascular invasion of breast cancer by imaging
MA Qinqin  LIU Jia  LU Xingru  JIN Jinlong 

Cite this article as: MA Q Q, LIU J, LU X R, et al. Research progress in the preoperative evaluation of lymphovascular invasion of breast cancer by imaging[J]. Chin J Magn Reson Imaging, 2025, 16(6): 176-181. DOI:10.12015/issn.1674-8034.2025.06.027.


[Abstract] Lymphovascular invasion (LVI) is closely associated with the poor prognosis of breast cancer. Evaluating the preoperative LVI status is of significant clinical importance for understanding the condition of breast cancer patients and their personalized treatment. Traditional imaging features such as tumor size, the tumor boundary, internal enhancement pattern, dynamic enhancement curve on dynamic contrast-enhanced, edge signs on diffusion-weighted imaging, peritumoral interstitial edema, subcutaneous fat blurring, and skin thickening can be utilized for evaluating LVI. Radiomics can calculate high-throughput quantitative features from digital images for research subjects, holding great promise in the preoperative prediction of LVI. This review summarizes the applications of conventional imaging and radiomics in assessing LVI in breast cancer. It outlines current research progress, existing challenges, and future research directions, offering new insights for precise diagnosis treatment decisions in breast cancer.
[Keywords] breast cancer;lymphovascular invasion;imaging, magnetic resonance imaging;radiomics;preoperative evaluation

MA Qinqin1   LIU Jia2#   LU Xingru3   JIN Jinlong1*  

1 Department of Nuclear Medicine, the Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou 730050, China

2 Department of Nuclear Medicine, Chongqing University Cancer Hospital, Chongqing 400030, China

3 Department of Radiology, the First Hospital of Lanzhou University, Lanzhou 730030, China

Corresponding author: JIN J L, E-mail: 422527019@qq.com

Conflicts of interest   None.

Received  2025-03-21
Accepted  2025-06-10
DOI: 10.12015/issn.1674-8034.2025.06.027
Cite this article as: MA Q Q, LIU J, LU X R, et al. Research progress in the preoperative evaluation of lymphovascular invasion of breast cancer by imaging[J]. Chin J Magn Reson Imaging, 2025, 16(6): 176-181. DOI:10.12015/issn.1674-8034.2025.06.027.

[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]
MOHAMMED R A A, MARTIN S G, GILL M S, et al. Improved methods of detection of lymphovascular invasion demonstrate that it is the predominant method of vascular invasion in breast cancer and has important clinical consequences[J]. Am J Surg Pathol, 2007, 31(12): 1825-1833. DOI: 10.1097/PAS.0b013e31806841f6.
[3]
ZHONG Y M, TONG F, SHEN J. Lympho-vascular invasion impacts the prognosis in breast-conserving surgery: a systematic review and meta-analysis[J/OL]. BMC Cancer, 2022, 22(1): 102 [2025-03-20]. https://pubmed.ncbi.nlm.nih.gov/35073848/. DOI: 10.1186/s12885-022-09193-0.
[4]
HOUVENAEGHEL G, COHEN M, CLASSE J M, et al. Lymphovascular invasion has a significant prognostic impact in patients with early breast cancer, results from a large, national, multicenter, retrospective cohort study[J/OL]. ESMO Open, 2021, 6(6): 100316 [2025-03-20]. https://pubmed.ncbi.nlm.nih.gov/34864349/. DOI: 10.1016/j.esmoop.2021.100316.
[5]
TOROUS V F, SIMPSON R W, BALANI J P, et al. College of American pathologists cancer protocols: from optimizing cancer patient care to facilitating interoperable reporting and downstream data use[J/OL]. JCO Clin Cancer Inform, 2021, 5: 47-55 [2025-03-20]. https://pubmed.ncbi.nlm.nih.gov/33439728/. DOI: 10.1200/CCI.20.00104.
[6]
RYU Y J, KANG S J, CHO J S, et al. Lymphovascular invasion can be better than pathologic complete response to predict prognosis in breast cancer treated with neoadjuvant chemotherapy[J/OL]. Medicine (Baltimore), 2018, 97(30): e11647 [2025-03-20]. https://pubmed.ncbi.nlm.nih.gov/30045313/. DOI: 10.1097/md.0000000000011647.
[7]
FREEDMAN G M, LI T Y, POLLI L V, et al. Lymphatic space invasion is not an independent predictor of outcomes in early stage breast cancer treated by breast-conserving surgery and radiation[J]. Breast J, 2012, 18(5): 415-419. DOI: 10.1111/j.1524-4741.2012.01271.x.
[8]
CHEN H Y, MENG X C, HAO X P, et al. Correlation analysis of pathological features and axillary lymph node metastasis in patients with invasive breast cancer[J/OL]. J Immunol Res, 2022, 2022: 7150304 [2025-03-20]. https://pubmed.ncbi.nlm.nih.gov/36249424/. DOI: 10.1155/2022/7150304.
[9]
LIANG G, ZHANG S X, ZHENG Y Q, et al. Establishment of a predictive nomogram for breast cancer lympho-vascular invasion based on radiomics obtained from digital breast tomography and clinical imaging features[J/OL]. BMC Med Imaging, 2025, 25(1): 65 [2025-03-20]. https://pubmed.ncbi.nlm.nih.gov/40011817/. DOI: 10.1186/s12880-025-01607-2.
[10]
AZAM S, ERIKSSON M, SJÖLANDER A, et al. Mammographic density change and risk of breast cancer[J]. J Natl Cancer Inst, 2020, 112(4): 391-399. DOI: 10.1093/jnci/djz149.
[11]
ATAKPA E C, THORAT M A, CUZICK J, et al. Mammographic density, endocrine therapy and breast cancer risk: a prognostic and predictive biomarker review[J/OL]. Cochrane Database Syst Rev, 2021, 10(10): CD013091 [2025-03-20]. https://pubmed.ncbi.nlm.nih.gov/34697802/. DOI: 10.1002/14651858.CD013091.pub2.
[12]
DI COSIMO S, DEPRETTO C, MICELI R, et al. Mammographic density to predict response to neoadjuvant systemic breast cancer therapy[J]. J Cancer Res Clin Oncol, 2022, 148(4): 775-781. DOI: 10.1007/s00432-021-03881-3.
[13]
LIU Z, LI R, LIANG K, et al. Value of digital mammography in predicting lymphovascular invasion of breast cancer[J/OL]. BMC Cancer, 2020, 20(1): 274 [2025-03-20]. https://pubmed.ncbi.nlm.nih.gov/32245448/. DOI: 10.1186/s12885-020-6712-z.
[14]
WU X Z, CHEN B, LI M F, et al. Evaluating value of multimodal MRI combined with digital breast 3D tomography on lymphatic vascular infiltration in patients with invasive breast cancer-non special type mass type[J]. J Pract Radiol, 2025, 41(4): 599-602, 613. DOI: 10.3969/j.issn.1002-1671.2025.04.013.
[15]
GUO Y, HU Y Z, QIAO M Y, et al. Radiomics analysis on ultrasound for prediction of biologic behavior in breast invasive ductal carcinoma[J/OL]. Clin Breast Cancer, 2018, 18(3): e335-e344 [2025-03-20]. https://pubmed.ncbi.nlm.nih.gov/28890183/. DOI: 10.1016/j.clbc.2017.08.002.
[16]
GEMICI A A, OZAL S T, HOCAOGLU E, et al. Relationship between shear wave elastography findings and histologic prognostic factors of invasive breast cancer[J]. Ultrasound Q, 2020, 36(1): 79-83. DOI: 10.1097/RUQ.0000000000000471.
[17]
BOUCHET P, GENNISSON J L, PODDA A, et al. Artifacts and technical restrictions in 2D shear wave elastography[J]. Ultraschall Med, 2020, 41(3): 267-277. DOI: 10.1055/a-0805-1099.
[18]
HUANG Y N, LIU Y, WANG Y, et al. Quantitative analysis of shear wave elastic heterogeneity for prediction of lymphovascular invasion in breast cancer[J/OL]. Br J Radiol, 2021, 94(1127): 20210682 [2025-03-20]. https://pubmed.ncbi.nlm.nih.gov/34478333/. DOI: 10.1259/bjr.20210682.
[19]
BAI L, YOU C, ZHOU J, et al. Quantitative analysis of shear wave elastography and US-guided diffuse optical tomography for evaluating biological characteristics of breast cancer[J]. Acad Radiol, 2024, 31(9): 3489-3498. DOI: 10.1016/j.acra.2024.03.006.
[20]
TSAROUCHI M I, HOXHAJ A, MANN R M. New approaches and recommendations for risk-adapted breast cancer screening[J]. J Magn Reson Imaging, 2023, 58(4): 987-1010. DOI: 10.1002/jmri.28731.
[21]
CHOI B B. Dynamic contrast enhanced-MRI and diffusion-weighted image as predictors of lymphovascular invasion in node-negative invasive breast cancer[J/OL]. World J Surg Oncol, 2021, 19(1): 76 [2025-03-20]. https://pubmed.ncbi.nlm.nih.gov/33722246/. DOI: 10.1186/s12957-021-02189-3.
[22]
XIE Y B, KIM Y J, PANG J N, et al. Coronary atherosclerosis T1-weighed characterization with integrated anatomical reference: comparison with high-risk plaque features detected by invasive coronary imaging[J]. JACC Cardiovasc Imaging, 2017, 10(6): 637-648. DOI: 10.1016/j.jcmg.2016.06.014.
[23]
LAI T, CHEN X, YANG Z, et al. Quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging to predict lymphovascular invasion and survival outcome in breast cancer[J/OL]. Cancer Imaging, 2022, 22(1): 61 [2025-03-20]. https://pubmed.ncbi.nlm.nih.gov/36273200/. DOI: 10.1186/s40644-022-00499-7.
[24]
GROHEUX D, ESPIÉ M, GIACCHETTI S, et al. Performance of FDG PET/CT in the clinical management of breast cancer[J]. Radiology, 2013, 266(2): 388-405. DOI: 10.1148/radiol.12110853.
[25]
SASADA S, MASUMOTO N, SUZUKI E, et al. Prediction of biological characteristics of breast cancer using dual-phase FDG PET/CT[J]. Eur J Nucl Med Mol Imaging, 2019, 46(4): 831-837. DOI: 10.1007/s00259-019-4259-5.
[26]
SHIN D J, CHOI H, OH D K, et al. Correlation between standardized uptake value of 18F-FDG PET/CT and conductivity with pathologic prognostic factors in breast cancer[J/OL]. Sci Rep, 2023, 13: 9844 [2025-03-20]. https://pubmed.ncbi.nlm.nih.gov/37330544/. DOI: 10.1038/s41598-023-36958-9.
[27]
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.
[28]
WANG D Q, LIU M S, ZHUANG Z J, et al. Radiomics analysis on digital breast tomosynthesis: preoperative evaluation of lymphovascular invasion status in invasive breast cancer[J]. Acad Radiol, 2022, 29(12): 1773-1782. DOI: 10.1016/j.acra.2022.03.011.
[29]
ALABOUSI M, ZHA N X, SALAMEH J P, et al. Digital breast tomosynthesis for breast cancer detection: a diagnostic test accuracy systematic review and meta-analysis[J]. Eur Radiol, 2020, 30(4): 2058-2071. DOI: 10.1007/s00330-019-06549-2.
[30]
XU M L, YANG H M, SUN J, et al. Development of an intratumoral and peritumoral radiomics nomogram using digital breast tomosynthesis for preoperative assessment of lymphovascular invasion in invasive breast cancer[J]. Acad Radiol, 2024, 31(5): 1748-1761. DOI: 10.1016/j.acra.2023.11.010.
[31]
DU Y, CAI M J, ZHA H L, et al. Ultrasound radiomics-based nomogram to predict lymphovascular invasion in invasive breast cancer: a multicenter, retrospective study[J]. Eur Radiol, 2024, 34(1): 136-148. DOI: 10.1007/s00330-023-09995-1.
[32]
XU M L, ZENG S E, LI F, et al. Preoperative prediction of lymphovascular invasion in patients with T1 breast invasive ductal carcinoma based on radiomics nomogram using grayscale ultrasound[J/OL]. Front Oncol, 2022, 12: 1071677 [2025-03-20]. https://pubmed.ncbi.nlm.nih.gov/36568215/. DOI: 10.3389/fonc.2022.1071677.
[33]
LIU Z Y, 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.
[34]
LI Y, WU X, YAN Y, et al. Automated breast volume scanner based Radiomics for non-invasively prediction of lymphovascular invasion status in breast cancer[J/OL]. BMC Cancer, 2023, 23(1): 813 [2025-03-20]. https://pubmed.ncbi.nlm.nih.gov/37648970/. DOI: 10.1186/s12885-023-11336-w.
[35]
CONTI A, DUGGENTO A, INDOVINA I, et al. Radiomics in breast cancer classification and prediction[J/OL]. Semin Cancer Biol, 2021, 72: 238-250 [2025-03-20]. https://pubmed.ncbi.nlm.nih.gov/32371013/. DOI: 10.1016/j.semcancer.2020.04.002.
[36]
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.
[37]
ZHANG C L, ZHOU P, LI R B, et al. Prediction of lymphovascular invasion in invasive breast cancer based on clinical-MRI radiomics features[J/OL]. BMC Med Imaging, 2024, 24(1): 277 [2025-03-20]. https://pubmed.ncbi.nlm.nih.gov/39415127/. DOI: 10.1186/s12880-024-01456-5.
[38]
HAN X, GUO Y, YE H, et al. Development of a machine learning-based radiomics signature for estimating breast cancer TME phenotypes and predicting anti-PD-1/PD-L1 immunotherapy response[J/OL]. Breast Cancer Res, 2024, 26(1): 18 [2025-03-20]. https://pubmed.ncbi.nlm.nih.gov/38287356/. DOI: 10.1186/s13058-024-01776-y.
[39]
HUANG Y H, ZHU T, ZHANG X L, et al. Longitudinal MRI-based fusion novel model predicts pathological complete response in breast cancer treated with neoadjuvant chemotherapy: a multicenter, retrospective study[J/OL]. EClinicalMedicine, 2023, 58: 101899 [2025-03-20]. https://pubmed.ncbi.nlm.nih.gov/37007742/. DOI: 10.1016/j.eclinm.2023.101899.
[40]
MA Q Q, LU X R, CHEN Q T, et al. Multiphases DCE-MRI radiomics nomogram for preoperative prediction of lymphovascular invasion in invasive breast cancer[J]. Acad Radiol, 2024, 31(12): 4743-4758. DOI: 10.1016/j.acra.2024.06.007.
[41]
MARCU D C, GRAVA C, MARCU L G. Current role of delta radiomics in head and neck oncology[J/OL]. Int J Mol Sci, 2023, 24(3): 2214 [2025-03-20]. https://pubmed.ncbi.nlm.nih.gov/36768535/. DOI: 10.3390/ijms24032214.
[42]
ZHENG H, JIAN L, LI L, et al. Delta-radiomics based on dynamic contrast-enhanced MRI for predicting lymphovascular invasion in invasive breast cancer[J]. Acad Radiol, 2024, 31(5): 1762-1772. DOI: 10.1016/j.acra.2023.11.017.
[43]
GU J H, TONG T, XU D, et al. Deep learning radiomics of ultrasonography for comprehensively predicting tumor and axillary lymph node status after neoadjuvant chemotherapy in breast cancer patients: a multicenter study[J]. Cancer, 2023, 129(3): 356-366. DOI: 10.1002/cncr.34540.
[44]
IBRAGIMOV B, TOESCA D, CHANG D, et al. Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT[J]. Med Phys, 2018, 45(10): 4763-4774. DOI: 10.1002/mp.13122.
[45]
FENG B, LIU Z S, LIU Y, et al. Predicting lymphovascular invasion in clinically node-negative breast cancer detected by abbreviated magnetic resonance imaging: Transfer learning vs. radiomics[J/OL]. Front Oncol, 2022, 12: 890659 [2025-03-20]. https://pubmed.ncbi.nlm.nih.gov/36185309/. DOI: 10.3389/fonc.2022.890659.
[46]
LIANG R, LI F F, YAO J Y, et al. Predictive value of MRI-based deep learning model for lymphovascular invasion status in node-negative invasive breast cancer[J/OL]. Sci Rep, 2024, 14: 16204 [2025-03-20]. https://pubmed.ncbi.nlm.nih.gov/39003325/. DOI: 10.1038/s41598-024-67217-0.
[47]
YANG X Q, FAN X H, LIN S Y, et al. Assessment of lymphovascular invasion in breast cancer using a combined MRI morphological features, radiomics, and deep learning approach based on dynamic contrast-enhanced MRI[J]. J Magn Reson Imaging, 2024, 59(6): 2238-2249. DOI: 10.1002/jmri.29060.

PREV Research progress on magnetic resonance imaging of trastuzumab-induced cardiotoxicity in HER-2 positive breast cancer
NEXT Research progress of magnetic resonance imaging in predicting immunohistochemical markers in hepatocellular carcinoma
  



Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn