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Clinical Article
The value of muti-parametric MRI in different molecular subtypes of primary breast cancer
ZHENG Xuan  LI Yancui  PENG Ruchen 

Cite this article as: ZHENG X, LI Y C, PENG R C. The value of muti-parametric MRI in different molecular subtypes of primary breast cancer[J]. Chin J Magn Reson Imaging, 2023, 14(5): 104-109. DOI:10.12015/issn.1674-8034.2023.05.019.

[Abstract] Objective To explore the value of muti-parametric magnetic resonance imaging (mpMRI) in different molecular subtypes of primary breast cancer.Materials and Methods MRI data of 137 patients with primary breast cancer confirmed by pathology were analyzed retrospectively. To compare the differences among different types of breast cancer in age, menopause status, MRI morphological characteristics, diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC) value and time-signal intensity curve (TIC).Results Among 137 cases of breast cancer, including 38 cases of Luminal A breast cancer, 75 cases of Luminal B breast cancer, 10 cases of human epidermal growth factor receptor-2 (HER-2) overexpression breast cancer and 14 cases of triple-negative breast cancer. Among the four molecular subtypes, there were statistically significant differences in the diameter, shape, edge, enhancement mode, maximum ADC value and minimum ADC value of the tumor (P=0.011, 0.010, 0.003, 0.006, 0.017, 0.008, respectively). Among them, the triple-negative breast cancer tumors were large in volume, round in shape, smooth in edge, and ring enhancement. The edges of Luminal A and Luminal B are mostly spicule shaped and the ADC value is lower than the other two groups. The ADC value of HER-2 overexpression type patients' masses is higher than that of other molecular subtypes of masses. There was no significant difference in the margin, T2WI signal, T1WI signal, necrotic cystic change, enhancement degree and TIC curve type of different molecular subtypes of breast cancer (P>0.05).Conclusions The mpMRI features of different molecular subtypes of breast cancer masses are different, which is helpful for non-invasive prediction of molecular subtypes before surgery, especially for the differentiation of triple negative breast cancer.
[Keywords] breast cancer;molecular subtypes;magnetic resonance imaging;multi-parametric magnetic resonance imaging;diffusion weighted imaging;apparent diffusion coefficient

ZHENG Xuan   LI Yancui   PENG Ruchen*  

Department of Medical Imaging, Beijing Luhe Hospital, Capital Medical University, Beijing 101149, China

Corresponding author: Peng RC, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS Special Research on Discipline Construction and Scientific Research Development of Luhe Hospital (No. KJ2021CX008-04).
Received  2023-02-21
Accepted  2023-04-28
DOI: 10.12015/issn.1674-8034.2023.05.019
Cite this article as: ZHENG X, LI Y C, PENG R C. The value of muti-parametric MRI in different molecular subtypes of primary breast cancer[J]. Chin J Magn Reson Imaging, 2023, 14(5): 104-109. DOI:10.12015/issn.1674-8034.2023.05.019.

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">10.3322/caac.21660">10.3322/caac.21660.
ZHANG L, YANG L F, JIAO X. An integrated model based on feature fusion for classifying molecular subtypes of breast cancer[J]. Chin J Magn Reson Imaging, 2023, 14(3): 58-64. DOI: 10.12015/issn.1674-8034.2023.03.011">10.12015/issn.1674-8034.2023.03.011">10.12015/issn.1674-8034.2023.03.011.
GOLDHIRSCH A, WINER E P, COATES A S, et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013[J]. Ann Oncol, 2013, 24(9): 2206-2223. DOI: 10.1093/annonc/mdt303">10.1093/annonc/mdt303">10.1093/annonc/mdt303.
WANG L, PAN H F, LI Y. Analysis of staging and clinicopathological characteristics of different molecular subtypes with prognostic staging[J]. J Clin Surg, 2019, 27(3): 231-234. DOI: 10.3969/j.issn.1005-6483.2019.03.017">10.3969/j.issn.1005-6483.2019.03.017">10.3969/j.issn.1005-6483.2019.03.017.
CHEN H, LI W, WAN C, et al. Correlation of dynamic contrast-enhanced MRI and diffusion-weighted MR imaging with prognostic factors and subtypes of breast cancers[J/OL]. Front Oncol, 2022, 12: 942943 [2023-02-15]. DOI: 10.3389/fonc.2022.942943">10.3389/fonc.2022.942943">10.3389/fonc.2022.942943.
LIAN P, ZHANG Q F, WANG L. Application of DCEGMRI and DWI in the preoperative diagnosis of breast cancer[J]. J Pract Radiol, 2019, 35(10): 1599-1602. DOI: 10.3969/j.issn.1002-1671.2019.10.011">10.3969/j.issn.1002-1671.2019.10.011">10.3969/j.issn.1002-1671.2019.10.011.
ZHANG M, HORVAT J V, BERNARD-DAVILA B, et al. Multiparametric MRI model with dynamic contrast-enhanced and diffusion-weighted imaging enables breast cancer diagnosis with high accuracy[J]. J Magn Reson Imaging, 2019, 49(3): 864-874. DOI: 10.1002/jmri.26285">10.1002/jmri.26285">10.1002/jmri.26285.
DAIMIEL NARANJO I, GULLO R LO, SACCARELLI C, et al. Diagnostic value of diffusion-weighted imaging with synthetic b-values in breast tumors: comparison with dynamic contrast-enhanced and multiparametric MRI[J]. Eur Radiol, 2021, 31(1): 356-367. DOI: 10.1007/s00330-020-07094-z">10.1007/s00330-020-07094-z">10.1007/s00330-020-07094-z.
MONTEMEZZI S, CAMERA L, GIRI M G, et al. Is there a correlation between 3T multiparametric MRI and molecular subtypes of breast cancer?[J]. Eur J Radiol, 2018, 108: 120-127. DOI: 10.1016/j.ejrad.2018.09.024">10.1016/j.ejrad.2018.09.024">10.1016/j.ejrad.2018.09.024.
WANG Z B, ZHOU Z Q, WANG Z, et al. The clinical value of 3.0T MRI dynamic enhanced scan combined with MB-DWI in the diagnosis of breast cancer[J]. Chin J CT MRI, 2022, 20(5): 123-125. DOI: 10.3969/j.issn.1672-5131.2022.05.040">10.3969/j.issn.1672-5131.2022.05.040">10.3969/j.issn.1672-5131.2022.05.040.
LI Z, LI M L, CHENG L Q. DCE-TIC combined with DWI-ADC in the differential diagnosis of benign and malignant breast lesions[J]. Chin J Med Imaging, 2019, 27(9): 654-658. DOI: 10.3969/j.issn.1005-5185.2019.09.005">10.3969/j.issn.1005-5185.2019.09.005">10.3969/j.issn.1005-5185.2019.09.005.
CURIGLIANO G, BURSTEIN H J, WINER E P, et al. De-escalating and escalating treatments for early-stage breast cancer: the St. Gallen International Expert Consensus Conference on the Primary Therapy of Early Breast Cancer 2017[J/OL]. Ann Oncol, 2019, 30(7): 1181 [2023-02-15]. DOI: 10.1093/annonc/mdy537">10.1093/annonc/mdy537">10.1093/annonc/mdy537.
Breast Cancer Professional Committee of Chinese Anti Cancer Association. Guidelines and norms for diagnosis and treatment of breast cancer of China Anti-Cancer Association (2021 edition)[J]. China Oncol, 2021, 31(10): 609-680. DOI: 10.19401/j.cnki.1007-3639.2021.10.013">10.19401/j.cnki.1007-3639.2021.10.013">10.19401/j.cnki.1007-3639.2021.10.013.
LIAN Z Q, HE J H, WANG X, et al. Clinical features and survival analysis of molecular subtyping of breast cancer[J]. Chin J Breast Dis Electron Version, 2009, 3(2): 5-9. DOI: 10.3969/j.issn.1674-0807.2009.02.003">10.3969/j.issn.1674-0807.2009.02.003">10.3969/j.issn.1674-0807.2009.02.003.
SZEP M, PINTICAN R, BIANCA B C, et al. Multiparametric MRI features of breast cancer molecular subtypes[J/OL]. Medicina (Kaunas), 2022, 58(12): 1716 [2023-02-15]. DOI: 10.3390/medicina58121716">10.3390/medicina58121716">10.3390/medicina58121716.
MATSUMOTO M, YAMAGUCHI R, MORITA M, et al. 6. relationships between histomorphology, imaging findings and subtypes in breast cancer[J]. Nihon Hoshasen Gijutsu Gakkai Zasshi, 2022, 78(10): 1224-1229. DOI: 10.6009/jjrt.2022-2091">10.6009/jjrt.2022-2091">10.6009/jjrt.2022-2091.
KAZAMA T, TAKAHARA T, HASHIMOTO J. Breast cancer subtypes and quantitative magnetic resonance imaging: a systemic review[J/OL]. Life (Basel), 2022, 12(4): 490 [2023-02-15]. DOI: 10.3390/life12040490">10.3390/life12040490">10.3390/life12040490.
GALATI F, RIZZO V, MOFFA G, et al. Radiologic-pathologic correlation in breast cancer: do MRI biomarkers correlate with pathologic features and molecular subtypes?[J/OL]. Eur Radiol Exp, 2022, 6(1): 39 [2023-02-15]. DOI: 10.1186/s41747-022-00289-7">10.1186/s41747-022-00289-7">10.1186/s41747-022-00289-7.
DERAKHSHAN F, REIS-FILHO J S. Pathogenesis of triple-negative breast cancer[J]. Annu Rev Pathol, 2022, 17: 181-204. DOI: 10.1146/annurev-pathol-042420-093238">10.1146/annurev-pathol-042420-093238">10.1146/annurev-pathol-042420-093238.
KANG S P, MARTEL M, HARRIS L N. Triple negative breast cancer: current understanding of biology and treatment options[J]. Curr Opin Obstet Gynecol, 2008, 20(1): 40-46. DOI: 10.1097/GCO.0b013e3282f40de9">10.1097/GCO.0b013e3282f40de9">10.1097/GCO.0b013e3282f40de9.
JEH S K, KIM S H, KIM H S, et al. Correlation of the apparent diffusion coefficient value and dynamic magnetic resonance imaging findings with prognostic factors in invasive ductal carcinoma[J]. J Magn Reson Imaging, 2011, 33(1): 102-109. DOI: 10.1002/jmri.22400">10.1002/jmri.22400">10.1002/jmri.22400.
LAMB P M, PERRY N M, VINNICOMBE S J, et al. Correlation between ultrasound characteristics, mammographic findings and histological grade in patients with invasive ductal carcinoma of the breast[J]. Clin Radiol, 2000, 55(1): 40-44. DOI: 10.1053/crad.1999.0333">10.1053/crad.1999.0333">10.1053/crad.1999.0333.
MCANDREW N P. Updates on targeting human epidermal growth factor receptor 2-positive breast cancer: what's to know in 2021[J]. Curr Opin Obstet Gynecol, 2022, 34(1): 41-45. DOI: 10.1097/GCO.0000000000000762">10.1097/GCO.0000000000000762">10.1097/GCO.0000000000000762.
WANG X Y, HU Q, FANG M Y, et al. The correlation between HER-2 expression and the CEUS and ARFI characteristics of breast cancer[J/OL]. PLoS One, 2017, 12(6): e0178692 [2023-02-15]. DOI: 10.1371/journal.pone.0178692.
KIM E J, KIM S H, PARK G E, et al. Histogram analysis of apparent diffusion coefficient at 3.0t: correlation with prognostic factors and subtypes of invasive ductal carcinoma[J]. J Magn Reson Imaging, 2015, 42(6): 1666-1678. DOI: 10.1002/jmri.24934">10.1002/jmri.24934">10.1002/jmri.24934.
MORADI B, GITY M, ETESAM F, et al. Correlation of apparent diffusion coefficient values and peritumoral edema with pathologic biomarkers in patients with breast cancer[J]. Clin Imaging, 2020, 68: 242-248. DOI: 10.1016/j.clinimag.2020.08.020">10.1016/j.clinimag.2020.08.020">10.1016/j.clinimag.2020.08.020.
DONG C C, XU Q. Research progress of tumor-related diffusion-weighted imaging models in the prediction of prognostic factors of breast cancer[J]. Int J Med Radiol, 2023, 46(1): 60-65. DOI: 10.19300/j.2023.Z20034">10.19300/j.2023.Z20034">10.19300/j.2023.Z20034.
LEITHNER D, BERNARD-DAVILA B, MARTINEZ D F, et al. Radiomic signatures derived from diffusion-weighted imaging for the assessment of breast cancer receptor status and molecular subtypes[J]. Mol Imaging Biol, 2020, 22(2): 453-461. DOI: 10.1007/s11307-019-01383-w">10.1007/s11307-019-01383-w">10.1007/s11307-019-01383-w.
MARIC J, BOBAN J, IVKOVIC-KAPICL T, et al. Differentiation of breast lesions and distinguishing their histological subtypes using diffusion-weighted imaging and ADC values[J/OL]. Front Oncol, 2020, 10: 332 [2023-02-15]. DOI: 10.3389/fonc.2020.00332">10.3389/fonc.2020.00332">10.3389/fonc.2020.00332.
SHIN H J, KIM S H, LEE H J, et al. Tumor apparent diffusion coefficient as an imaging biomarker to predict tumor aggressiveness in patients with estrogen-receptor-positive breast cancer[J]. NMR Biomed, 2016, 29(8): 1070-1078. DOI: 10.1002/nbm.3571">10.1002/nbm.3571">10.1002/nbm.3571.
SOLIMAN N A, YUSSIF S M. Ki-67 as a prognostic marker according to breast cancer molecular subtype[J]. Cancer Biol Med, 2016, 13(4): 496-504. DOI: 10.20892/j.issn.2095-3941.2016.0066">10.20892/j.issn.2095-3941.2016.0066">10.20892/j.issn.2095-3941.2016.0066.
SUROV A, CLAUSER P, CHANG Y W, et al. Can diffusion-weighted imaging predict tumor grade and expression of Ki-67 in breast cancer? A multicenter analysis[J/OL]. Breast Cancer Res, 2018, 20(1): 58 [2023-02-15]. DOI: 10.1186/s13058-018-0991-1">10.1186/s13058-018-0991-1">10.1186/s13058-018-0991-1.
MOLINARI C, CLAUSER P, GIROMETTI R, et al. MR mammography using diffusion-weighted imaging in evaluating breast cancer: a correlation with proliferation index[J]. Radiol Med, 2015, 120(10): 911-918. DOI: 10.1007/s11547-015-0527-z">10.1007/s11547-015-0527-z">10.1007/s11547-015-0527-z.
SUNG J S, JOCHELSON M S, BRENNAN S, et al. MR imaging features of triple-negative breast cancers[J]. Breast J, 2013, 19(6): 643-649. DOI: 10.1111/tbj.12182">10.1111/tbj.12182">10.1111/tbj.12182.
NAVARRO VILAR L, ALANDETE GERMÁN S P, MEDINA GARCÍA R, et al. MR imaging findings in molecular subtypes of breast cancer according to BIRADS system[J]. Breast J, 2017, 23(4): 421-428. DOI: 10.1111/tbj.12756">10.1111/tbj.12756">10.1111/tbj.12756.
WANG C Y, WEI W, SANTIAGO L, et al. Can imaging kinetic parameters of dynamic contrast-enhanced magnetic resonance imaging be valuable in predicting clinicopathological prognostic factors of invasive breast cancer?[J]. Acta Radiol, 2018, 59(7): 813-821. DOI: 10.1177/0284185117740746">10.1177/0284185117740746">10.1177/0284185117740746.

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