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Research progress of multi-parameter MRI and radiomics in distinguishing HER-2-low and HER-2-zero expressing breast cancer
ZHANG Zhimeng  GUO Lantian 

Cite this article as: ZHANG Z M, GUO L T. Research progress of multi-parameter MRI and radiomics in distinguishing HER-2-low and HER-2-zero expressing breast cancer[J]. Chin J Magn Reson Imaging, 2026, 17(4): 184-191. DOI:10.12015/issn.1674-8034.2026.04.026.


[Abstract] The human epidermal growth factor receptor 2 (HER-2) gene is a crucial prognostic factor for breast cancer patients. Breast cancer is classified into three subtypes, namely HER-2-overexpressing, HER-2-low expressing, and HER-2-zero expressing, using immunohistochemistry (IHC) and fluorescence in situ hybridization (ISH) techniques. Anti-HER-2 targeted therapy can improve the prognosis of patients with HER-2-overexpressing and HER-2-low expressing breast cancer, whereas patients with HER-2-zero expression are not eligible for this therapeutic strategy. Therefore, early identification of breast cancer with different HER-2 phenotypes can assist clinicians in formulating individualized treatment regimens and enhance patients' quality of survival. Multi-parameter magnetic resonance imaging (mpMRI) integrates information from multiple imaging sequences to characterize lesions in a multi-dimensional manner and enable quantitative assessment. Radiomics extracts quantitative imaging features through high-throughput computing and further mines the heterogeneous characteristics of tumors in depth. However, several challenges remain to be overcome for mpMRI and radiomics in differentiating low HER-2 expression from zero HER-2 expression, including inconsistent MRI scanning parameters and non-uniform standards for radiomic feature extraction, insufficient model generalization, unclear associations between imaging phenotypes and molecular mechanisms, and lagged clinical translation. Therefore, this review summarizes and compares the latest research progress on the application of mpMRI and radiomics in distinguishing between HER-2-low expressing and HER-2-zero expressing breast cancer, aiming to provide insights for future research and facilitate precise clinical diagnosis and treatment.
[Keywords] multi-parameter magnetic resonance imaging;radiomics;breast cancer;human epidermal growth factor receptor 2;artificial intelligence

ZHANG Zhimeng   GUO Lantian*  

Department of Radiology, Binzhou Medical University Hospital, Binzhou 256603, China

Corresponding author: GUO L T, E-mail: byfyglt@163.com

Conflicts of interest   None.

Received  2025-12-23
Accepted  2026-03-25
DOI: 10.12015/issn.1674-8034.2026.04.026
Cite this article as: ZHANG Z M, GUO L T. Research progress of multi-parameter MRI and radiomics in distinguishing HER-2-low and HER-2-zero expressing breast cancer[J]. Chin J Magn Reson Imaging, 2026, 17(4): 184-191. DOI:10.12015/issn.1674-8034.2026.04.026.

[1]
ZHAO Y, JIANG X Y, ZHAO N, et al. Non-invasive prediction of HER-2 overexpression and low expression in NME-type breast cancer using multiparametric MRI radiomics combined with MRI features[J]. Chin J Magn Reson Imaging, 2025, 16(10): 41-47, 97. DOI: 10.12015/issn.1674-8034.2025.10.007.
[2]
NOLAN E, LINDEMAN G J, VISVADER J E. Deciphering breast cancer: from biology to the clinic[J]. Cell, 2023, 186(8): 1708-1728. DOI: 10.1016/j.cell.2023.01.040.
[3]
QI Y J, SU G H, YOU C, et al. Radiomics in breast cancer: Current advances and future directions[J/OL]. Cell Rep Med, 2024, 5(9): 101719 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/39293402/. DOI: 10.1016/j.xcrm.2024.101719.
[4]
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 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/37526540/. DOI: 10.1148/radiol.222646.
[5]
BITENCOURT A G V, GIBBS P, ROSSI SACCARELLI C, et al. MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer[J/OL]. EBioMedicine, 2020, 61: 103042 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/33039708/. DOI: 10.1016/j.ebiom.2020.103042.
[6]
WOLFF A C, HAMMOND M E H, ALLISON K H, et al. Human epidermal growth factor receptor 2 testing in breast cancer: American society of clinical oncology/college of American pathologists clinical practice guideline focused update[J]. J Clin Oncol, 2018, 36(20): 2105-2122. DOI: 10.1200/JCO.2018.77.8738.
[7]
MARCHIÒ C, ANNARATONE L, MARQUES A, et al. Evolving concepts in HER2 evaluation in breast cancer: Heterogeneity, HER2-low carcinomas and beyond[J/OL]. Semin Cancer Biol, 2021, 72: 123-135 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/32112814/. DOI: 10.1016/j.semcancer.2020.02.016.
[8]
DENKERT C, SEITHER F, SCHNEEWEISS A, et al. Clinical and molecular characteristics of HER2-low-positive breast cancer: pooled analysis of individual patient data from four prospective, neoadjuvant clinical trials[J]. Lancet Oncol, 2021, 22(8): 1151-1161. DOI: 10.1016/S1470-2045(21)00301-6.
[9]
CHOONG G M, CULLEN G D, O'SULLIVAN C C. Evolving standards of care and new challenges in the management of HER2-positive breast cancer[J]. CA Cancer J Clin, 2020, 70(5): 355-374. DOI: 10.3322/caac.21634.
[10]
MODI S N, JACOT W, YAMASHITA T, et al. Trastuzumab deruxtecan in previously treated HER2-low advanced breast cancer[J]. N Engl J Med, 2022, 387(1): 9-20. DOI: 10.1056/NEJMoa2203690.
[11]
MODI, PARK H, MURTHY R K, et al. Antitumor activity and safety of trastuzumab deruxtecan in patients with HER2-low-expressing advanced breast cancer: results from a phase ib study[J]. J Clin Oncol, 2020, 38(17): 1887-1896. DOI: 10.1200/JCO.19.02318.
[12]
PENG Y Q, ZHANG X, QIU Y, et al. Development and validation of MRI radiomics models to differentiate HER2-zero, -low, and-positive breast cancer[J/OL]. Am J Roentgenol, 2024, 222(4): e2330603 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/38265001/. DOI: 10.2214/ajr.23.30603.
[13]
FU Q Y, SUN K, YAN F H. Overview of MRI-based radiomics in breast cancer[J]. Chin J Magn Reson Imaging, 2023, 14(4): 166-170, 187. DOI: 10.12015/issn.1674-8034.2023.04.029.
[14]
TARANTINO P, VIALE G, PRESS M F, et al. ESMO expert consensus statements (ECS) on the definition, diagnosis, and management of HER2-low breast cancer[J]. Ann Oncol, 2023, 34(8): 645-659. DOI: 10.1016/j.annonc.2023.05.008.
[15]
FANG C Y, ZHANG J T, LI J Z, et al. Clinical-radiomics nomogram for identifying HER2 status in patients with breast cancer: a multicenter study[J/OL]. Front Oncol, 2022, 12: 922185 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/36158700/. DOI: 10.3389/fonc.2022.922185.
[16]
CHEN T T, ZHAO H F, ZHANG X, et al. Advances in mpMRI and radiomics for differentiating granulomatous mastitis from non-mass-like breast cancer[J]. Chin J Magn Reson Imaging, 2025, 16(10): 171-176. DOI: 10.12015/issn.1674-8034.2025.10.027.
[17]
LI X G, TIAN J, ZHANG C L, et al. Overview of MRI-based radiomics in breast cancer diagnosis and treatment[J]. Chin J Magn Reson Imaging, 2024, 15(7): 196-203. DOI: 10.12015/issn.1674-8034.2024.07.033.
[18]
LIU Q, CHANG C, LI J W. Research progress on the correlation between imaging features and the molecular subtype, histopathology, clinical prognosis of ductal carcinoma in situ of the breast[J]. China Oncol, 2024, 34(2): 201-209. DOI: 10.19401/j.cnki.1007-3639.2024.02.008.
[19]
WITOWSKI J, HEACOCK L, REIG B, et al. Improving breast cancer diagnostics with deep learning for MRI[J/OL]. Sci Transl Med, 2022, 14(664): eabo4802 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/36170446/. DOI: 10.1126/scitranslmed.abo4802.
[20]
CHEN Y X, CHEN S Y, TANG W J, et al. Multiparametric MRI radiomics with machine learning for differentiating HER2-zero, -low, and-positive breast cancer: model development, testing, and interpretability analysis[J/OL]. AJR Am J Roentgenol, 2025, 224(1): e2431717 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/39413232/. DOI: 10.2214/AJR.24.31717.
[21]
YANG Z Q, CHEN X F, ZHANG T H, et al. Quantitative multiparametric MRI as an imaging biomarker for the prediction of breast cancer receptor status and molecular subtypes[J/OL]. Front Oncol, 2021, 11: 628824 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/34604024/. DOI: 10.3389/fonc.2021.628824.
[22]
ZHAO S Q, WANG S Y, LI Y F, et al. Quantitative parameters of intravoxel incoherent movement imaging and dynamic contrast enhancement MRI for the prediction of HER2-zero, -low, and-positive breast cancers[J]. Acad Radiol, 2025, 32(4): 1851-1860. DOI: 10.1016/j.acra.2024.11.011.
[23]
DAI L J, MA D, XU Y Z, et al. Molecular features and clinical implications of the heterogeneity in Chinese patients with HER2-low breast cancer[J/OL]. Nat Commun, 2023, 14(1): 5112 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/37607916/. DOI: 10.1038/s41467-023-40715-x.
[24]
MENDEZ A M, FANG L K, MERIWETHER C H, et al. Diffusion breast MRI: current standard and emerging techniques[J/OL]. Front Oncol, 2022, 12: 844790 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/35880168/. DOI: 10.3389/fonc.2022.844790.
[25]
IIMA M, KATAOKA M, HONDA M, et al. Diffusion-weighted MRI for the assessment of molecular prognostic biomarkers in breast cancer[J]. Korean J Radiol, 2024, 25(7): 623-633. DOI: 10.3348/kjr.2023.1188.
[26]
MAO C P, HU L X, JIANG W, et al. Discrimination between human epidermal growth factor receptor 2 (HER2)-low-expressing and HER2-overexpressing breast cancers: a comparative study of four MRI diffusion models[J]. Eur Radiol, 2024, 34(4): 2546-2559. DOI: 10.1007/s00330-023-10198-x.
[27]
WANG X X, BA R C, HUANG Y, et al. Time-dependent diffusion MRI helps predict molecular subtypes and treatment response to neoadjuvant chemotherapy in breast cancer[J/OL]. Radiology, 2024, 313(1): e240288 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/39436292/. DOI: 10.1148/radiol.240288.
[28]
SUN K, CHEN X S, CHAI W M, et al. Breast cancer: diffusion kurtosis MR imaging-diagnostic accuracy and correlation with clinical-pathologic factors[J]. Radiology, 2015, 277(1): 46-55. DOI: 10.1148/radiol.15141625.
[29]
SHEN Y Y, ZHANG X, ZHENG J L, et al. Distinguishing low expression levels of human epidermal growth factor receptor 2 in breast cancer: insights from qualitative and quantitative magnetic resonance imaging analysis[J/OL]. Tomography, 2025, 11(3): 31 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/40137571/. DOI: 10.3390/tomography11030031.
[30]
JIRARAYAPONG J, PORTNOW L H, CHIKARMANE S A, et al. High peritumoral and intratumoral T2 signal intensity in HER2-positive breast cancers on preneoadjuvant breast MRI: assessment of associations with histopathologic characteristics[J/OL]. AJR Am J Roentgenol, 2024, 222(3): e2330280 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/38117101/. DOI: 10.2214/AJR.23.30280.
[31]
ZHANG G C, REN C Y, LI C, et al. Distinct clinical and somatic mutational features of breast tumors with high-, low-, or non-expressing human epidermal growth factor receptor 2 status[J/OL]. BMC Med, 2022, 20(1): 142 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/35484593/. DOI: 10.1186/s12916-022-02346-9.
[32]
IIMA M, HONDA M, SIGMUND E E, et al. Diffusion MRI of the breast: Current status and future directions[J]. J Magn Reson Imaging, 2020, 52(1): 70-90. DOI: 10.1002/jmri.26908.
[33]
JACKSON A, O'CONNOR J P, PARKER G J, et al. Imaging tumor vascular heterogeneity and angiogenesis using dynamic contrast-enhanced magnetic resonance imaging[J]. Clin Cancer Res, 2007, 13(12): 3449-3459. DOI: 10.1158/1078-0432.CCR-07-0238.
[34]
HE L T, QIN Y J, HU Q L, et al. Quantitative characterization of breast lesions and normal fibroglandular tissue using compartmentalized diffusion-weighted model: comparison of intravoxel incoherent motion and restriction spectrum imaging[J/OL]. Breast Cancer Res, 2024, 26(1): 71 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/38658999/. DOI: 10.1186/s13058-024-01828-3.
[35]
LI Z H, LIANG H Z, ZOU Y, et al. Research progress in amide proton transfer imaging for the diagnosis and treatment of breast cancer[J]. Chin Comput Med Imaging, 2025, 31(2): 291-295. DOI: 10.3969/j.issn.1006-5741.2025.02.026.
[36]
ZHAN T. Predictive efficacy of amide proton transfer weighted imaging and diffusion weighted imaging in evaluating HER2 expression status in breast cancer[D]. Nanchang: Nanchang University, 2025. DOI: 10.27232/d.cnki.gnchu.2025.000270.
[37]
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.
[38]
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 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/38905803/. DOI: 10.1016/j.ejrad.2024.111573.
[39]
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.
[40]
AREFAN D, ZULEY M L, BERG W A, et al. Assessment of background parenchymal enhancement at dynamic contrast-enhanced MRI in predicting breast cancer recurrence risk[J/OL]. Radiology, 2024, 310(1): e230269 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/38259203/. DOI: 10.1148/radiol.230269.
[41]
YIN L, ZHANG Y, WEI X, et al. Preliminary study on DCE-MRI radiomics analysis for differentiation of HER2-low and HER2-zero breast cancer[J/OL]. Front Oncol, 2024, 14: 1385352 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/39211554/. DOI: 10.3389/fonc.2024.1385352.
[42]
JIANG Z J, SONG L R, LU H C, et al. The potential use of DCE-MRI texture analysis to predict HER2 2+ status[J/OL]. Front Oncol, 2019, 9: 242 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/31032222/. DOI: 10.3389/fonc.2019.00242.
[43]
MANN R M, CHO N, MOY L. Breast MRI: state of the art[J]. Radiology, 2019, 292(3): 520-536. DOI: 10.1148/radiol.2019182947.
[44]
ZHOU J, TAN H N, LI W, et al. Radiomics signatures based on multiparametric MRI for the preoperative prediction of the HER2 status of patients with breast cancer[J]. Acad Radiol, 2021, 28(10): 1352-1360. DOI: 10.1016/j.acra.2020.05.040.
[45]
ZHENG S Y, YANG Z H, DU G Z, et al. Discrimination between HER2-overexpressing, -low-expressing, and-zero-expressing statuses in breast cancer using multiparametric MRI-based radiomics[J]. Eur Radiol, 2024, 34(9): 6132-6144. DOI: 10.1007/s00330-024-10641-7.
[46]
ZHAO S Q, WEI F, LI Y F, et al. Multi-parametric MRI radiomics predicts different HER2 expression in breast cancer[J/OL]. Cancer Imaging, 2025, 26(1): 13 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/41444699/. DOI: 10.1186/s40644-025-00981-y.
[47]
BRAMAN N, PRASANNA P, WHITNEY J, et al. Association of peritumoral radiomics with tumor biology and pathologic response to preoperative targeted therapy for HER2 (ERBB2)-positive breast cancer[J/OL]. JAMA Netw Open, 2019, 2(4): e192561 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/31002322/. DOI: 10.1001/jamanetworkopen.2019.2561.
[48]
SHI Z W, HUANG X M, CHENG Z L, et al. MRI-based quantification of intratumoral heterogeneity for predicting treatment response to neoadjuvant chemotherapy in breast cancer[J/OL]. Radiology, 2023, 308(1): e222830 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/37432083/. DOI: 10.1148/radiol.222830.
[49]
BIAN X Q, DU S Y, YUE Z B, et al. Potential antihuman epidermal growth factor receptor 2 target therapy beneficiaries: the role of MRI-based radiomics in distinguishing human epidermal growth factor receptor 2-low status of breast cancer[J]. J Magn Reson Imaging, 2023, 58(5): 1603-1614. DOI: 10.1002/jmri.28628.
[50]
ZHANG D W, WANG P, TIAN D J, et al. Research progress of multi-parameter MRI habitat imaging in breast cancer[J]. Radiol Pract, 2025, 40(5): 677-681. DOI: 10.13609/j.cnki.1000-0313.2025.05.026.
[51]
WANG X S, ZHANG Y, ZHAO J Y, et al. Research progress of MRI-based habitat analysis in the clinical diagnosis and treatment of breast cancer[J]. Chin J Magn Reson Imaging, 2025, 16(10): 177-183. DOI: 10.12015/issn.1674-8034.2025.10.028.
[52]
TSAROUCHI M, VAMVAKAS A. Editorial for "habitat radiomics based on dynamic contrast-enhanced magnetic resonance imaging for assessing axillary lymph node burden in clinical T1-T2 stage breast cancer: a multicenter and interpretable study"[J]. J Magn Reson Imaging, 2025, 62(3): 765-766. DOI: 10.1002/jmri.29809.
[53]
CHEN H Q, LIU Y L, ZHAO J Q, et al. Quantification of intratumoral heterogeneity using habitat-based MRI radiomics to identify HER2-positive, -low and-zero breast cancers: a multicenter study[J/OL]. Breast Cancer Res, 2024, 26(1): 160 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/39578913/. DOI: 10.1186/s13058-024-01921-7.
[54]
MAYFIELD J D, ATAYA D, ABDALAH M, et al. Presurgical upgrade prediction of DCIS to invasive ductal carcinoma using time-dependent deep learning models with DCE MRI[J/OL]. Radiol Artif Intell, 2024, 6(5): e230348 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/38900042/. DOI: 10.1148/ryai.230348.
[55]
ZHANG J D, LI Y H, LI Z R, et al. Deep-learning-based HER2 status assessment from multimodal breast cancer data predicts neoadjuvant therapy response[J/OL]. Nat Biomed Eng, 2025 [2025-12-22]. https://pubmed.ncbi.nlm.nih.gov/41107520/. DOI: 10.1038/s41551-025-01495-5.
[56]
DAI Y, LIAN C, ZHANG Z, et al. Development and validation of a deep learning system to differentiate HER2-zero, HER2-low, and HER2-positive breast cancer based on dynamic contrast-enhanced MRI[J]. J Magn Reson Imaging, 2025, 61(5): 2212-2220. DOI: 10.1002/jmri.29670.

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