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Clinical application and progress of synthetic MRI in breast cancer
HUANG Yue  LI Feng 

Cite this article as: HUANG Y, LI F. Clinical application and progress of synthetic MRI in breast cancer[J]. Chin J Magn Reson Imaging, 2024, 15(11): 209-215. DOI:10.12015/issn.1674-8034.2024.11.033.


[Abstract] Synthetic MRI (SyMRI) is an increasingly mature quantitative magnetic resonance imaging technology, which can obtain a variety of contrast weighted image reconstruction in a short scan, and can directly obtain quantitative parameters reflecting the biophysical properties of tissues. The T1, T2 and proton density (PD) values obtained by this technique play an important role in the differential diagnosis of benign and malignant breast, molecular typing evaluation, histological grading prediction and prognosis evaluation, and are now more and more widely practiced in clinical practice. This paper reviews the principle of SyMRI technology and the research progress and prospects in breast diseases, hoping to help radiologists to have a more comprehensive understanding of breast cancer-related image manifestations and provide objective and accurate imaging information for clinical diagnosis and treatment.
[Keywords] magnetic resonance imaging;breast cancer;synthetic magnetic resonance imaging;molecular typing;lymph node metastasis;neoadjuvant chemotherapy;artificial intelligence

HUANG Yue1, 2   LI Feng3*  

1 Xiangyang Central Hospital of Wuhan University of Science and Technology, Xiangyang441021, China

2 School of Medicine, Wuhan University of Science and Technology, Wuhan430081, China

3 Department of Radiology, Xiangyang Central Hospital, Hubei University of Arts and Sciences, Xiangyang441021, China

Corresponding author: LI F, E-mail: xfkite@163.com

Conflicts of interest   None.

Received  2024-09-09
Accepted  2024-11-10
DOI: 10.12015/issn.1674-8034.2024.11.033
Cite this article as: HUANG Y, LI F. Clinical application and progress of synthetic MRI in breast cancer[J]. Chin J Magn Reson Imaging, 2024, 15(11): 209-215. DOI:10.12015/issn.1674-8034.2024.11.033.

[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]
RAHIB L, WEHNER M R, MATRISIAN L M, et al. Estimated projection of US cancer incidence and death to 2040[J/OL]. JAMA Netw Open, 2021, 4(4): e214708 [2024-09-08]. https://pubmed.ncbi.nlm.nih.gov/33825840/. DOI: 10.1001/jamanetworkopen.2021.4708.
[3]
JI S, YANG D J, LEE J, et al. Synthetic MRI: technologies and applications in neuroradiology[J]. J Magn Reson Imaging, 2022, 55(4): 1013-1025. DOI: 10.1002/jmri.27440.
[4]
ANDICA C, HAGIWARA A, HORI M, et al. Review of synthetic MRI in pediatric brains: basic principle of MR quantification, its features, clinical applications, and limitations[J]. J De Neuroradiol, 2019, 46(4): 268-275. DOI: 10.1016/j.neurad.2019.02.005.
[5]
HWANG K P, FUJITA S. Synthetic MR: physical principles, clinical implementation, and new developments[J]. Med Phys, 2022, 49(7): 4861-4874. DOI: 10.1002/mp.15686.
[6]
HAGIWARA A, HORI M, COHEN-ADAD J, et al. Linearity, bias, intrascanner repeatability, and interscanner reproducibility of quantitative multidynamic multiecho sequence for rapid simultaneous relaxometry at 3 T: a validation study with a standardized phantom and healthy controls[J]. Invest Radiol, 2019, 54(1): 39-47. DOI: 10.1097/RLI.0000000000000510.
[7]
FUJIOKA T, MORI M, OYAMA J, et al. Investigating the image quality and utility of synthetic MRI in the breast[J]. Magn Reson Med Sci, 2021, 20(4): 431-438. DOI: 10.2463/mrms.mp.2020-0132.
[8]
RYU K H, BAEK H J, MOON J I, et al. Initial clinical experience of synthetic MRI as a routine neuroimaging protocol in daily practice: a single-center study[J]. J Neuroradiol, 2020, 47(2): 151-160. DOI: 10.1016/j.neurad.2019.03.002.
[9]
WANG D, LI C M, CHEN M. Progress of synthetic MRI in evaluating myelin in central nervous system[J]. Chin J Radiol, 2021, 55(7): 769-772. DOI: 10.3760/cma.j.cn112149-20200727-00961.
[10]
GE X, SUN S Y, LIU W X, et al. Prediction of diffuse glioma grade and tumor cell proliferative activity by synthetic MRI combined with three dimensional arterial spin labeling imaging[J]. Chin J Radiol, 2022, 56(5): 524-529. DOI: 10.3760/cma.j.cn112149-20210617-00569.
[11]
GAO W B, YANG Q X, CHEN X, et al. Value of synthetic MRI in differential diagnosis of benign and malignant breast lesions[J]. Chin J Radiol, 2021, 55(6): 605-608. DOI: 10.3760/cma.j.cn112149-20200831-01043.
[12]
XU M Y, CHEN Q Q, LIU J R, et al. Clinical value of synthetic MRI and dynamic contrast-enhanced MRI in the differentiation of benign and malignant breast lesions[J]. Chin J Radiol, 2022, 56(7): 766-771. DOI: 10.3760/cma.j.cn112149-20210819-00582.
[13]
SUN S Y, LI Z L, NIE L S, et al. Value of synthetic MRI combined with ADC in differential diagnosis of benign and malignant breast lesions[J]. Chin J Radiol, 2021, 55(6): 597-604. DOI: 10.3760/cma.j.cn112149-20200717-00927.
[14]
MENG T B, HE N, HE H Q, et al. The diagnostic performance of quantitative mapping in breast cancer patients: a preliminary study using synthetic MRI[J/OL]. Cancer Imaging, 2020, 20(1): 88 [2024-09-08]. https://pubmed.ncbi.nlm.nih.gov/33317609/. DOI: 10.1186/s40644-020-00365-4.
[15]
MATSUDA M, TSUDA T, EBIHARA R, et al. Enhanced masses on contrast-enhanced breast: differentiation using a combination of dynamic contrast-enhanced MRI and quantitative evaluation with synthetic MRI[J]. J Magn Reson Imaging, 2021, 53(2): 381-391. DOI: 10.1002/jmri.27362.
[16]
MENG L S, ZHAO X, GUO J X, et al. Evaluation of the differentiation of benign and malignant breast lesions using synthetic relaxometry and the Kaiser score[J/OL]. Front Oncol, 2022, 12: 964078 [2024-09-08]. https://pubmed.ncbi.nlm.nih.gov/36303839/. DOI: 10.3389/fonc.2022.964078.
[17]
LIU J R, XU M Y, REN J L, et al. Synthetic MRI, multiplexed sensitivity encoding, and BI-RADS for benign and malignant breast cancer discrimination[J/OL]. Front Oncol, 2022, 12: 1080580 [2024-09-08]. https://pubmed.ncbi.nlm.nih.gov/36818669/. DOI: 10.3389/fonc.2022.1080580.
[18]
GAO W B, ZHANG S Q, GUO J X, et al. Investigation of synthetic relaxometry and diffusion measures in the differentiation of benign and malignant breast lesions as compared to BI-RADS[J]. J Magn Reson Imaging, 2021, 53(4): 1118-1127. DOI: 10.1002/jmri.27435.
[19]
LIU L, YIN B, SHEK K, et al. Role of quantitative analysis of T2 relaxation time in differentiating benign from malignant breast lesions[J]. J Int Med Res, 2018, 46(5): 1928-1935. DOI: 10.1177/0300060517721071.
[20]
ZHANG L Y, ZHAO X, YIN X. Differential diagnosis of benign and malignant breast lesions using quantitative synthetic magnetic resonance imaging[J]. Nan Fang Yi Ke Da Xue Xue Bao, 2022, 42(4): 457-462. DOI: 10.12122/j.issn.1673-4254.2022.04.01.
[21]
LI X J, FAN Z C, JIANG H N, et al. Synthetic MRI in breast cancer: differentiating benign from malignant lesions and predicting immunohistochemical expression status[J/OL]. Sci Rep, 2023, 13(1): 17978 [2024-09-08]. https://pubmed.ncbi.nlm.nih.gov/37864025/. DOI: 10.1038/s41598-023-45079-2.
[22]
SONG M N, DONG L, HE H, et al. Value of syMRI and DWI quantitative parameters measured using different regions of interest method in differentiating benign and malignant breast lesions[J]. Chin J Magn Reson Imag, 2022, 13(6): 17-22, 27. DOI: 10.12015/issn.1674-8034.2022.06.004.
[23]
GAO W B, YANG Q X, LI X H, et al. Quantitative assessment of breast tumor: comparison of four methods of positioning region of interest for synthetic relaxometry and diffusion measurement[J]. Acad Radiol, 2024, 31(8): 3096-3105. DOI: 10.1016/j.acra.2024.02.045.
[24]
LI Q, LI F Z, YU H T, et al. The value of quantitative parameters from synthetic MRI in differentiating triple negative breast cancer from other types[J]. J China Clin Med Imag, 2024, 35(2): 90-95. DOI: 10.12117/jccmi.2024.02.004.
[25]
ZHENG R, XIE Y, XUE K, et al. Correlation of synthetic MRI quantitative parameters with molecular subtypes and cell proliferation activity in breast invasive ductal carcinoma[J]. Chin J Clin Oncol, 2023, 50(14): 728-732. DOI: 10.12354/j.issn.1000-8179.2023.20230342.
[26]
LI Q, XIAO Q, YANG M, et al. Histogram analysis of quantitative parameters from synthetic MRI: correlations with prognostic factors and molecular subtypes in invasive ductal breast cancer[J/OL]. Eur J Radiol, 2021, 139: 109697 [2024-09-08]. https://pubmed.ncbi.nlm.nih.gov/33857828/. DOI: 10.1016/j.ejrad.2021.109697.
[27]
KAZAMA T, TAKAHARA T, KWEE T C, et al. Quantitative values from synthetic MRI correlate with breast cancer subtypes[J/OL]. Life, 2022, 12(9): 1307 [2024-09-08]. https://pubmed.ncbi.nlm.nih.gov/36143344/. DOI: 10.3390/life12091307.
[28]
ZHANG Q, ZHAO Y, NIE J, et al. Pretreatment synthetic MRI features for triple-negative breast cancer[J/OL]. Clin Radiol, 2024, 79(2): e219-e226 [2024-09-08]. https://pubmed.ncbi.nlm.nih.gov/37935611/. DOI: 10.1016/j.crad.2023.10.015.
[29]
GAO W B, YANG Q X, LI X H, et al. Synthetic MRI with quantitative mappings for identifying receptor status, proliferation rate, and molecular subtypes of breast cancer[J/OL]. Eur J Radiol, 2022, 148: 110168 [2024-09-08]. https://pubmed.ncbi.nlm.nih.gov/35078137/. DOI: 10.1016/j.ejrad.2022.110168.
[30]
WANG L M, ZHOU Z P, XU L Y, et al. Value of synthetic MRI and conventional MRI in identifying triple negative and non-triple negative breast cancer[J]. Chin J Magn Reson Imag, 2024, 15(7): 112-117. DOI: 10.12015/issn.1674-8034.2024.07.019.
[31]
DU S Y, GAO S, ZHANG L N, et al. Improved discrimination of molecular subtypes in invasive breast cancer: comparison of multiple quantitative parameters from breast MRI[J/OL]. Magn Reson Imaging, 2021, 77: 148-158 [2024-09-08]. https://pubmed.ncbi.nlm.nih.gov/33309922/. DOI: 10.1016/j.mri.2020.12.001.
[32]
MATSUDA M, TSUDA T, EBIHARA R, et al. Triple-negative breast cancer on contrast-enhanced MRI and synthetic MRI: a comparison with non-triple-negative breast carcinoma[J/OL]. Eur J Radiol, 2021, 142: 109838 [2024-09-08]. https://pubmed.ncbi.nlm.nih.gov/34217136/. DOI: 10.1016/j.ejrad.2021.109838.
[33]
KRISHNAMURTI U, SILVERMAN J F. HER2 in breast cancer: a review and update[J]. Adv Anat Pathol, 2014, 21(2): 100-107. DOI: 10.1097/PAP.0000000000000015.
[34]
Breast Cancer Expert Committee of Chinese Society of Clinical Oncology, Breast Cancer Professional Committee of Chinese Anti-Cancer Association. Expert consensus on clinical diagnosis and treatment of human epidermal growth factor receptor 2 positive breast cancer (2021 edition)[J]. Natl Med J China, 2021, 101(17): 1226-1231. DOI: 10.3760/cma.j.cn112137-20210318-00679.
[35]
LI Q, HUANG Y, YANG M, et al. The histogram features of quantitative parameters from synthetic MRI in predicting the expression of human epithelial growth factor receptor 2 in breast invasive ductual carcinoma[J]. Chin J Radiol, 2021, 55(12): 1294-1300. DOI: 10.3760/cma.j.cn112149-20210620-00584.
[36]
MAMOUNAS E P, UNTCH M, MANO M S, et al. Adjuvant T-DM1 versus trastuzumab in patients with residual invasive disease after neoadjuvant therapy for HER2-positive breast cancer: subgroup analyses from KATHERINE[J]. Ann Oncol, 2021, 32(8): 1005-1014. DOI: 10.1016/j.annonc.2021.04.011.
[37]
ZHAN T, DAI J K, LI Y. Noninvasive identification of HER2-zero, -low, or-overexpressing breast cancers: Multiparametric MRI-based quantitative characterization in predicting HER2-low status of breast cancer[J/OL]. Eur J Radiol, 2024, 177: 111573 [2024-09-08]. https://pubmed.ncbi.nlm.nih.gov/38905803/. DOI: 10.1016/j.ejrad.2024.111573.
[38]
RAMTOHUL T, DJERROUDI L, LISSAVALID E, et al. Multiparametric MRI and radiomics for the prediction of HER2-zero, -low, and-positive breast cancers[J/OL]. Radiology, 2023, 308(2): e222646 [2024-09-08]. https://pubmed.ncbi.nlm.nih.gov/37526540/. DOI: 10.1148/radiol.222646.
[39]
GUO Y, XIE X T, TANG W J, et al. Noninvasive identification of HER2-low-positive status by MRI-based deep learning radiomics predicts the disease-free survival of patients with breast cancer[J]. Eur Radiol, 2024, 34(2): 899-913. DOI: 10.1007/s00330-023-09990-6.
[40]
MENON S S, GURUVAYOORAPPAN C, SAKTHIVEL K M, et al. Ki-67 protein as a tumour proliferation marker[J/OL]. Clin Chim Acta, 2019, 491: 39-45 [2024-09-08]. https://pubmed.ncbi.nlm.nih.gov/30653951/. DOI: 10.1016/j.cca.2019.01.011.
[41]
LI F Z, LI Q, WU S S, et al. Histogram features of quantitative parameters from synthetic MRI and ADC map in predicting the expression of Ki-67 in breast cancer[J]. Chin J Magn Reson Imag, 2022, 13(7): 29-34, 67. DOI: 10.12015/issn.1674-8034.2022.07.006.
[42]
ZHANG L Y, HAO J S, GUO J, et al. Predicting of ki-67 expression level using diffusion-weighted and synthetic magnetic resonance imaging in invasive ductal breast cancer[J/OL]. Breast J, 2023, 2023: 6746326 [2024-09-08]. https://pubmed.ncbi.nlm.nih.gov/37063453/. DOI: 10.1155/2023/6746326.
[43]
MATSUDA M, KIDO T, TSUDA T, et al. Utility of synthetic MRI in predicting the Ki-67 status of oestrogen receptor-positive breast cancer: a feasibility study[J/OL]. Clin Radiol, 2020, 75(5): 398.e1-398398.e8 [2024-09-08]. https://pubmed.ncbi.nlm.nih.gov/32019671/. DOI: 10.1016/j.crad.2019.12.021.
[44]
WANG X L, XUE Y. Clinicopathological characteristics and prognostic analysis of breast cancer with a hormone receptor status of ER (-)/PR(+)[J/OL]. Front Endocrinol, 2023, 14: 1193592 [2024-09-08]. https://pubmed.ncbi.nlm.nih.gov/37538790/. DOI: 10.3389/fendo.2023.1193592.
[45]
GRADISHAR W J, MORAN M S, ABRAHAM J, et al. Breast cancer, version 3.2022, NCCN clinical practice guidelines in oncology[J]. J Natl Compr Canc Netw, 2022, 20(6): 691-722. DOI: 10.6004/jnccn.2022.0030.
[46]
ZHAO R M, DU S Y, GAO S, et al. Time course changes of synthetic relaxation time during neoadjuvant chemotherapy in breast cancer: the optimal parameter for treatment response evaluation[J]. J Magn Reson Imaging, 2023, 58(4): 1290-1302. DOI: 10.1002/jmri.28597.
[47]
MATSUDA M, FUKUYAMA N, MATSUDA T, et al. Utility of synthetic MRI in predicting pathological complete response of various breast cancer subtypes prior to neoadjuvant chemotherapy[J]. Clin Radiol, 2022, 77(11): 855-863. DOI: 10.1016/j.crad.2022.06.019.
[48]
JIANG W H, DU S Y, GAO S, et al. Correlation between synthetic MRI relaxometry and apparent diffusion coefficient in breast cancer subtypes with different neoadjuvant therapy response[J/OL]. Insights Imaging, 2023, 14(1): 162 [2024-09-08]. https://pubmed.ncbi.nlm.nih.gov/37775610/. DOI: 10.1186/s13244-023-01492-9.
[49]
YANG X, LU Z, TAN X Y, et al. Evaluating the added value of synthetic magnetic resonance imaging in predicting sentinel lymph node status in breast cancer[J]. Quant Imaging Med Surg, 2024, 14(6): 3789-3802. DOI: 10.21037/qims-24-1.
[50]
YU X Y, ZHOU Z P, TONG Q Y, et al. Application value of synthetic MRI in the differential diagnosis of denign and malignant breast lesions and prognosis lymph node metastasis of breast cancer[J]. J Clin Radiol, 2023, 42(2): 244-251. DOI: 10.13437/j.cnki.jcr.2023.02.014.
[51]
QU M M, FENG W, LIU X R, et al. Investigation of synthetic MRI with quantitative parameters for discriminating axillary lymph nodes status in invasive breast cancer[J/OL]. Eur J Radiol, 2024, 175: 111452 [2024-09-08]. https://pubmed.ncbi.nlm.nih.gov/38604092/. DOI: 10.1016/j.ejrad.2024.111452.
[52]
HUANG W P, WANG F, LIU H L, et al. A preliminary clinical application of T2 mapping-based radiomics on MRI in breast diseases[J]. Chin J Magn Reson Imag, 2023, 14(2): 50-55. DOI: 10.12015/issn.1674-8034.2023.02.009.
[53]
GAO W B, DENG P F, YANG Q X, et al. A feasibility study of radiomics based on dynamic contrast enhanced magnetic resonance imaging in identifying benign and malignant breast mass[J]. J Clin Radiol, 2020, 39(4): 674-679. DOI: 10.13437/j.cnki.jcr.2020.04.011.
[54]
NIU H D, XU H, ZHU W F, et al. Value of multimodal radiomicsnomogram in predicting benign and malignant breast masses[J]. Chin Imag J Integr Tradit West Med, 2023, 21(3): 252-258. DOI: 10.3969/j.issn.1672-0512.2023.03.005.
[55]
LI J, WU L H, XU M Y, et al. Improving image quality and reducing scan time for synthetic MRI of breast by using deep learning reconstruction[J/OL]. Biomed Res Int, 2022, 2022: 3125426 [2024-09-08]. https://pubmed.ncbi.nlm.nih.gov/36060133/. DOI: 10.1155/2022/3125426.

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