Is there a correlation between signal intensity ratio of 3T MRI and molecular subtypes of breast cancer?
By: Yuanping Tao, Hongyan Chao, Yue Ren, Wanjun Zheng, Yuyou Chen, Liang Du, Huanguo Li and Feng Cui
References
- Orel SG, Schnall MD. MR imaging of the breast for the detection, diagnosis, and staging of breast cancer. Radiology 2001; 220: 13-30. doi: 10.1148/radiology.220.1.r01jl3113
- Telegrafo M, Rella L, Stabile Ianora AA, Angelelli G, Moschetta M. Unenhanced breast MRI (STIR, T2-weighted TSE, DWIBS): an accurate and alternative strategy for detecting and differentiating breast lesions. Magn Reson Imaging 2015; 33: 951-5. doi: 10.1016/j.mri.2015.06.002
- Du S, Gao S, Zhang L, Yang X, Qi X, Li S. Improved discrimination of molecular subtypes in invasive breast cancer: comparison of multiple quantitative parameters from breast MRI. Magn Reson Imaging 2021; 77: 148-58. doi: 10.1016/j.mri.2020.12.001
- Heacock L, Lewin AA, Toth HK, Moy L, Reig B. Abbreviated MR imaging for breast cancer. Radiol Clin North Am 2021; 59: 99-111. doi: 10.1016/j.rcl.2020.09.001
- Yamaguchi K, Ichinohe K, Iyadomi M, Fujiki K, Yoshinaga Y, Egashira R, et al. Abbreviated and ultrafast dynamic contrast-enhanced (DCE) MR imaging. Magn Reson Med Sci 2025; 24: 315-31. doi: 10.2463/mrms.rev.2024-0158
- Kousi E, O’Flynn EAM, Borri M, Morgan VA, deSouza NM, Schmidt MA. Pre-treatment functional MRI of breast cancer: T2* evaluation at 3 T and relationship to dynamic contrast-enhanced and diffusion-weighted imaging. Magn Reson Imaging 2018; 52: 53-61. doi: 10.1016/j.mri.2018.05.014
- Meng T, He N, He H, Liu K, Ke L, Liu H, et al. The diagnostic performance of quantitative mapping in breast cancer patients: a preliminary study using synthetic MRI. Cancer Imaging 2020; 20: 88. doi: 10.1186/s40644-020-00365-4
- Malikova MA, Tkacz JN, Slanetz PJ, Guo CY, Aakil A, Jara H. Evaluation of T1/T2 ratios in a pilot study as a potential biomarker of biopsy: proven benign and malignant breast lesions in correlation with histopathological disease stage. Future Sci OA 2017; 3: FSO197. doi: 10.4155/fsoa-2016-0063
- Li Q, Xiao Q, Yang M, Chai Q, Huang Y, Wu PY, et al. Histogram analysis of quantitative parameters from synthetic MRI: correlations with prognostic factors and molecular subtypes in invasive ductal breast cancer. Eur J Radiol 2021; 139: 109697. doi: 10.1016/j.ejrad.2021.109697
- Matsuda M, Kido T, Tsuda T, Okada K, Shiraishi Y, Suekuni H, et al. Utility of synthetic MRI in predicting the Ki-67 status of oestrogen receptor-positive breast cancer: a feasibility study. Clin Radiol 2020; 75: 398 e1-398 e8. doi: 10.1016/j.crad.2019.12.021
- Kim TH, Ha Y, Shin JJ, Cho YE, Lee JH, Cho WH. Signal intensity ratio on magnetic resonance imaging as a prognostic factor in patients with cervical compressive myelopathy. Medicine (Baltimore) 2016; 95: e4649. doi: 10.1097/MD.0000000000004649
- Duvnjak S, Ravn P, Green A, Andersen PE. Magnetic resonance signal intensity ratio measurement before uterine artery embolization: ability to predict fibroid size reduction. Cardiovasc Intervent Radiol 2017; 40: 1839-44. doi: 10.1007/s00270-017-1721-2
- Shetty AS, Sipe AL, Zulfiqar M, Tsai R, Raptis DA, Raptis CA, et al. In-phase and opposed-phase imaging: applications of chemical shift and magnetic susceptibility in the chest and abdomen. Radiographics 2019; 39: 115-35. doi: 10.1148/rg.2019180043
- Priola AM, Priola SM, Ciccone G, Evangelista A, Cataldi A, Gned D, et al. Differentiation of rebound and lymphoid thymic hyperplasia from anterior mediastinal tumors with dual-echo chemical-shift MR imaging in adulthood: reliability of the chemical-shift ratio and signal intensity index. Radiology 2015; 274: 238-49. doi: 10.1148/radiol.14132665
- Durhan G, Poker A, Settarzade E, Karakaya J, Kösemehmetoğlu K, Akpınar MG, et al. Magnetic resonance imaging findings of invasive breast cancer in different histological grades and different histopathological types. Clin Imaging 2021; 76: 98-103. doi: 10.1016/j.clinimag.2021.01.039
- Curigliano G, Burstein HJ, Winer EP, Gnant M, Dubsky P, Loibl S, et al. Deescalating and escalating treatments for early-stage breast cancer: the St. Gallen International Expert Consensus Conference on the Primary Therapy of Early Breast Cancer 2017. Ann Oncol 2017; 28: 1700-12. doi: 10.1093/annonc/mdx308
- Guvenc I, Akay S, Ince S, Yildiz R, Kilbas Z, Oysul FG, et al. Apparent diffusion coefficient value in invasive ductal carcinoma at 3.0 Tesla: is it correlated with prognostic factors? Br J Radiol 2016; 89: 20150614. doi: 10.1259/bjr.20150614
- Suo S, Cheng F, Cao M, Kang J, Wang M, Hua J, et al. Multiparametric diffusion-weighted imaging in breast lesions: association with pathologic diagnosis and prognostic factors. J Magn Reson Imaging 2017; 46: 740-50. doi: 10.1002/jmri.25612
- Montemezzi S, Camera L, Giri MG, Pozzetto A, Caliò A, Meliadò G, et al. Is there a correlation between 3T multiparametric MRI and molecular subtypes of breast cancer? Eur J Radiol 2018; 108: 120-7. doi: 10.1016/j.ejrad.2018.09.024
- Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 2009; 45: 228-47. doi: 10.1016/j.ejca.2008.10.026
- Yin H, Bai L, Jia H, Lin G. Noninvasive assessment of breast cancer molecular subtypes on multiparametric MRI using convolutional neural network with transfer learning. Thorac Cancer 2022; 13: 3183-91. doi: 10.1111/1759-7714.14673
- Leithner D, Mayerhoefer ME, Martinez DF, Jochelson MS, Morris EA, Thakur SB, et al. Non-invasive assessment of breast cancer molecular subtypes with multiparametric magnetic resonance imaging radiomics. J Clin Med 2020; 9: 1853. doi: 10.3390/jcm9061853
- Linderholm BK, Hellborg H, Johansson U, Elmberger G, Skoog L, Lehtiö J, et al. Significantly higher levels of vascular endothelial growth factor (VEGF) and shorter survival times for patients with primary operable triple-negative breast cancer. Ann Oncol 2009; 20: 1639-46. doi: 10.1093/annonc/mdp062
- Mohammed RA, Ellis IO, Mahmmod AM, Hawkes EC, Green AR, Rakha EA, et al. Lymphatic and blood vessels in basal and triple-negative breast cancers: characteristics and prognostic significance. Mod Pathol 2011; 24: 774-85. doi: 10.1038/modpathol.2011.4
- Ozturk VS, Polat YD, Soyder A, Tanyeri A, Karaman CZ, Taskin F. The Relationship between MRI findings and molecular subtypes in women with breast cancer. Curr Probl Diagn Radiol 2020; 49: 417-21. doi: 10.1067/j.cpradiol.2019.07.003
- Nguyen VT, Duong DH, Nguyen QT, Nguyen DT, Tran TL, Duong TG. The association of magnetic resonance imaging features with five molecular subtypes of breast cancer. Eur J Radiol Open 2024; 13:100585. doi: 10.1016/j.ejro.2024.100585
- Yuen S, Monzawa S, Yanai S, Matsumoto H, Yata Y, Ichinose Y, et al. The association between MRI findings and breast cancer subtypes: focused on the combination patterns on diffusion-weighted and T2-weighted images. Breast Cancer 2020; 27: 1029-37. doi: 10.1007/s12282-020-01105-z
- Surov A, Chang YW, Li L, Partridge SC, Kim JY, Wienke A. Apparent diffusion coefficient cannot predict molecular subtype and lymph node metastases in invasive breast cancer: a multicenter analysis. BMC Cancer 2019; 19: 1043. doi: 10.1186/s12885-019-6298-5
- Ab Mumin N, Ramli Hamid MT, Wong JHD, Rahmat K, Ng KH. Magnetic resonance imaging phenotypes of breast cancer molecular subtypes: a systematic review. Acad Radiol 2022; 29 (Sppl 1): S89-106. doi: 10.1016/j.acra.2021.07.017
- Suo S, Zhang D, Cheng F, Cao M, Hua J, Lu J, et al. Added value of mean and entropy of apparent diffusion coefficient values for evaluating histologic phenotypes of invasive ductal breast cancer with MR imaging. Eur J Radiol 2018; 29: 1425-34. doi: 10.1007/s00330-018-5667-9
- Navarro Vilar L, Alandete German SP, Medina Garcia R, Blanc Garcia E, Camarasa Lillo N, Vilar Samper J. MR imaging findings in molecular subtypes of breast cancer according to BIRADS system. Breast J 2017; 23: 421-8. doi: 10.1111/tbj.12756
- Kazama T, Takahara T, Hashimoto J. Breast cancer subtypes and quantitative magnetic resonance imaging: a systemic review. Life (Basel) 2022; 12: 490. doi: 10.3390/life12040490
- Coskun Bilge A, Yaltirik Bilgin E, Bulut ZM, Esen Bostanci I, Bilgin E. Preoperative dynamic contrast-enhanced and diffusion-weighted breast magnetic resonance imaging findings for prediction of lymphovascular invasion of the lesions in node-negative invasive breast cancer. Can Assoc Radiol J 2024; 75: 386-96. doi: 10.1177/08465371231212893
- Chen K, Yu C, Pan J, Xu Y, Luo Y, Yang T, et al. Prediction of the Nottingham prognostic index and molecular subtypes of breast cancer through multimodal magnetic resonance imaging. Magn Reson Imaging 2024; 108: 168-75. doi: 10.1016/j.mri.2024.02.012
- Shangguan J, Shchukina E, Monov D, Larina S. Integrating deep learning and radiomics for precise identification of luminal A/B breast cancer subtypes on dynamic contrast-enhanced MRI. Cancer Imaging 2026; 26: 36. doi: 10.1186/s40644-026-00996-z
- Fogarty MJ. Age influences the specific force and fatigability of the external abdominal obliques but not pectoralis major muscles. Resp Physiol Neurobiol 2024; 320: 104187. doi: 10.1016/j.resp.2023.104187
Language: English
Submitted on: Jan 23, 2026
Accepted on: Apr 11, 2026
Published on: Jul 4, 2026
Published by: Association of Radiology and Oncology
In partnership with: Paradigm Publishing Services
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© 2026 Yuanping Tao, Hongyan Chao, Yue Ren, Wanjun Zheng, Yuyou Chen, Liang Du, Huanguo Li, Feng Cui, published by Association of Radiology and Oncology
This work is licensed under the Creative Commons Attribution 4.0 License.