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Advances in predicting HER-2 expression status in breast cancer using magnetic resonance imaging
DU Jinxiao  ZHANG Xiaoan 

DOI:10.12015/issn.1674-8034.2025.12.031.


[Abstract] Breast cancer is the most common malignant tumor in women worldwide, and human epidermal growth factor receptor-2 (HER-2) overexpression significantly affects the occurrence, malignant transformation, clinical outcomes and metastasis of breast tumors, exhibiting high aggressiveness and poor prognosis. Recent updates in classification criteria have refined the categorization of HER-2 expression status from a traditional binary classification (positive/negative) to a tripartite classification (over-expression/low-expression/null-expression), making the precise assessment of HER-2 expression status a critical component for individualized therapeutic decision-making in breast cancer. Magnetic resonance imaging (MRI) is widely used in the evaluation of breast cancer, incorporating various sequences and techniques such as morphology, dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted image (DWI). Radiomics can convert microscopic information that is difficult to identify in traditional MRI images into quantifiable biomarkers for the evaluation of research objects. This article reviews the research progress, limitations and development directions of MRI in predicting different expression states of HER-2 in breast cancer, aiming to provide theoretical basis and practical guidance for optimizing the precise diagnosis and treatment strategies of breast cancer.
[Keywords] breast cancer；magnetic resonance imaging；radiomics；human epidermal growth factor receptor-2；expression status

DU Jinxiao1   ZHANG Xiaoan1, 2*  

1 Imaging Center, the First Affiliated Hospital College of Clinical Medicine of Henan University of Science and Technology, Luoyang 471003, China

2 Department of Medical Imaging, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China

Corresponding author: ZHANG X A, E-mail: zxa@vip.163.com

Conflicts of interest   None.

Received  2025-07-26
Accepted  2025-10-22
DOI: 10.12015/issn.1674-8034.2025.12.031
DOI:10.12015/issn.1674-8034.2025.12.031.

[1]
BRAY F, LAVERSANNE M, SUNG H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2024, 74(3): 229-263. DOI: 10.3322/caac.21834.
[2]
WU H, LÜ Q. Epidemiological trends and implications of breast cancer prevention and control in China and the world: an interpretation of the Global Cancer Statistics Report 2018—2022[J]. Chin J Bases Clin Gen Surg, 2024, 31(7): 796-802. DOI: 10.7507/1007-9424.202407031.
[3]
SITIA L, SEVIERI M, SIGNATI L, et al. HER-2-targeted nanoparticles for breast cancer diagnosis and treatment[J/OL]. Cancers (Basel), 2022, 14(10): 2424 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/35626028/. DOI: 10.3390/cancers14102424.
[4]
BANERJI U, VAN HERPEN C M L, SAURA C, et al. Trastuzumab duocarmazine in locally advanced and metastatic solid tumours and HER2-expressing breast cancer: a phase 1 dose-escalation and dose-expansion study[J]. Lancet Oncol, 2019, 20(8): 1124-1135. DOI: 10.1016/S1470-2045(19)30328-6.
[5]
SMIT E F, FELIP E, UPRETY D, et al. Trastuzumab deruxtecan in patients with metastatic non-small-cell lung cancer (DESTINY-Lung01): primary results of the HER2-overexpressing cohorts from a single-arm, phase 2 trial[J]. Lancet Oncol, 2024, 25(4): 439-454. DOI: 10.1016/S1470-2045(24)00064-0.
[6]
OUYANG Q C, WANG Y, ZHANG J, et al. HS-10352 in hormone receptor-positive, HER2-negative advanced breast cancer: a phase 1 dose-escalation trial[J]. Cancer Med, 2023, 12(24): 21849-21860. DOI: 10.1002/cam4.6755.
[7]
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.
[8]
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.
[9]
TARANTINO P, HAMILTON E, TOLANEY S M, et al. HER2-low breast cancer: pathological and clinical landscape[J]. J Clin Oncol, 2020, 38(17): 1951-1962. DOI: 10.1200/JCO.19.02488.
[10]
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 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/35484593/. DOI: 10.1186/s12916-022-02346-9.
[11]
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 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/37607916/. DOI: 10.1038/s41467-023-40715-x.
[12]
MODI S N, 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.
[13]
MIGLIETTA F, GRIGUOLO G, BOTTOSSO M, et al. HER2-low-positive breast cancer: evolution from primary tumor to residual disease after neoadjuvant treatment[J/OL]. NPJ Breast Cancer, 2022, 8(1): 66 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/35595761/. DOI: 10.1038/s41523-022-00434-w.
[14]
PRAT A, BARDIA A, CURIGLIANO G, et al. An overview of clinical development of agents for metastatic or advanced breast cancer without ERBB2 amplification (HER2-low)[J/OL]. JAMA Oncol, 2022 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/36107417/. DOI: 10.1001/jamaoncol.2022.4175.
[15]
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.
[16]
FAN P C, XU K. Antibody-drug conjugates in breast cancer: Marching from HER2-overexpression into HER2-low[J/OL]. Biochim Biophys Acta Rev Cancer, 2023, 1878(1): 188849 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/36502928/. DOI: 10.1016/j.bbcan.2022.188849.
[17]
ZHANG X D, KOU M, ZOU W Y, et al. HER-2 expression is correlated with multimodal imaging features in breast cancer: a pilot study[J/OL]. Ann Med, 2024, 56(1): 2434182 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/39618080/. DOI: 10.1080/07853890.2024.2434182.
[18]
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 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/36170446/. DOI: 10.1126/scitranslmed.abo4802.
[19]
SHETH D, GIGER M L. Artificial intelligence in the interpretation of breast cancer on MRI[J]. J Magn Reson Imaging, 2020, 51(5): 1310-1324. DOI: 10.1002/jmri.26878.
[20]
SHANG Y Y, WANG Y X, GUO Y X, et al. The clinical study of intratumoral and peritumoral radiomics based on DCE-MRI for HER-2 positive and low expression prediction in breast cancer[J/OL]. Breast Cancer (Dove Med Press), 2024, 16: 957-972 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/39703346/. DOI: 10.2147/BCTT.S497770.
[21]
WANG Q X, ZHENG N, YU Q Q, et al. Multimodal imaging and advanced quantitative techniques for HER-2 status prediction in breast cancer[J/OL]. Discov Oncol, 2025, 16(1): 1820 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/41051612/. DOI: 10.1007/s12672-025-03627-4.
[22]
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 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/40137571/. DOI: 10.3390/tomography11030031.
[23]
MUMIN N A, RAMLI HAMID M T, WONG J H D, et al. Magnetic resonance imaging phenotypes of breast cancer molecular subtypes: a systematic review[J/OL]. Acad Radiol, 2022, 29(Suppl 1): S89-S106 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/34481705/. DOI: 10.1016/j.acra.2021.07.017.
[24]
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.
[25]
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.
[26]
TEMERIK S M, ELWAHAB S M A, WAHMAN M M, et al. Relation between morphological features of initial breast MRI and breast cancer molecular subtypes[J/OL]. Egypt J Radiol Nucl Med, 2023, 54(1): 147 [2025-07-30]. https://ejrnm.springeropen.com/articles/10.1186/s43055-023-01087-w. DOI: 10.1186/s43055-023-01087-w.
[27]
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 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/35934721/. DOI: 10.1186/s41747-022-00289-7.
[28]
JING X M, YANG F M, SHAO C C, et al. Role of hypoxia in cancer therapy by regulating the tumor microenvironment[J/OL]. Mol Cancer, 2019, 18(1): 157 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/31711497/. DOI: 10.1186/s12943-019-1089-9.
[29]
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 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/37526540/. DOI: 10.1148/radiol.222646.
[30]
LAI T F, CHEN X F, YANG Z Q, 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 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/36273200/. DOI: 10.1186/s40644-022-00499-7.
[31]
LI C L, SONG L R, YIN J D. Intratumoral and peritumoral radiomics based on functional parametric maps from breast DCE-MRI for prediction of HER-2 and ki-67 status[J]. J Magn Reson Imaging, 2021, 54(3): 703-714. DOI: 10.1002/jmri.27651.
[32]
YUAN C R, JIN F, GUO X L, et al. Correlation analysis of breast cancer DWI combined with DCE-MRI imaging features with molecular subtypes and prognostic factors[J/OL]. J Med Syst, 2019, 43(4): 83 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/30810823/. DOI: 10.1007/s10916-019-1197-5.
[33]
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 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/35454981/. DOI: 10.3390/life12040490.
[34]
WANG W W, LV S Q, XUN J, et al. Comparison of diffusion kurtosis imaging and dynamic contrast enhanced MRI in prediction of prognostic factors and molecular subtypes in patients with breast cancer[J/OL]. Eur J Radiol, 2022, 154: 110392 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/35679701/. DOI: 10.1016/j.ejrad.2022.110392.
[35]
WANG Z, REN G Y, YIN Q, et al. Correlation of magnetic resonance imaging quantitative parameters and apparent diffusion coefficient value with pathological breast cancer[J]. World J Clin Cases, 2022, 10(21): 7333-7340. DOI: 10.12998/wjcc.v10.i21.7333.
[36]
LI S E, YANG Z Q, LIN Y L, et al. Value of clinical and MRI features in predicting HER-2-positive and HER-2-low expression breast cancer[J]. Int J Med Radiol, 2025, 48(3): 306-311. DOI: 10.19300/j.2025.L21656.
[37]
ZHU G M, DONG Y D, ZHU R T, et al. Dynamic contrast-enhanced magnetic resonance imaging parameters combined with diffusion-weighted imaging for discriminating malignant lesions, molecular subtypes, and pathological grades in invasive ductal carcinoma patients[J/OL]. PLoS One, 2025, 20(4): e0320240 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/40233046/. DOI: 10.1371/journal.pone.0320240.
[38]
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.
[39]
WANG Q, HOU W S, LI X H, et al. Research progress on MRI-based artificial intelligence in predicting breast cancer molecular subtypes[J]. Int J Med Radiol, 2025, 48(1): 96-101. DOI: 10.19300/j.2025.Z21524.
[40]
LE N N, LI W, ONISHI N, et al. Effect of inter-reader variability on diffusion-weighted MRI apparent diffusion coefficient measurements and prediction of pathologic complete response for breast cancer[J]. Tomography, 2022, 8(3): 1208-1220. DOI: 10.3390/tomography8030099.
[41]
NIU S X, WANG X Y, ZHAO N N, et al. Radiomic evaluations of the diagnostic performance of DM, DBT, DCE MRI, DWI, and their combination for the diagnosisof breast cancer[J/OL]. Front Oncol, 2021, 11: 725922 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/34568055/. DOI: 10.3389/fonc.2021.725922.
[42]
LIU Y P, BU H, YANG W T. Interpretation of 2019 update of Chinese breast cancer HER2 detection guidelines[J]. Chin J Pathol, 2019, 48(3): 182-185. DOI: 10.3760/cma.j.issn.0529-5807.2019.03.003.
[43]
SI L F, LIU X J, LI X Y, et al. Diffusion kurtosis imaging and intravoxel incoherent motion imaging parameters in breast lesions: Effect of radiologists' experience and region-of-interest selection[J/OL]. Eur J Radiol, 2023, 158: 110633 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/36470051/. DOI: 10.1016/j.ejrad.2022.110633.
[44]
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 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/35880168/. DOI: 10.3389/fonc.2022.844790.
[45]
USLU H, ÖNAL T, TOSUN M, et al. Intravoxel incoherent motion magnetic resonance imaging for breast cancer: a comparison with molecular subtypes and histological grades[J/OL]. Magn Reson Imaging, 2021, 78: 35-41 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/33556485/. DOI: 10.1016/j.mri.2021.02.005.
[46]
SAJUST DE BERGUES DE ESCALUP A, DURON L, KOSKAS P, et al. Synthetic MRI for detecting abnormal signals in the optic nerves: an exploratory study[J/OL]. Invest Radiol, 2025 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/40667842/. DOI: 10.1097/RLI.0000000000001226.
[47]
CUI Y D, HAN S Y, LIU M, et al. Diagnosis and grading of prostate cancer by relaxation maps from synthetic MRI[J]. J Magn Reson Imaging, 2020, 52(2): 552-564. DOI: 10.1002/jmri.27075.
[48]
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 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/33317609/. DOI: 10.1186/s40644-020-00365-4.
[49]
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 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/37864025/. DOI: 10.1038/s41598-023-45079-2.
[50]
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 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/37775610/. DOI: 10.1186/s13244-023-01492-9.
[51]
KAZAMA T, TAKAHARA T, KWEE T C, et al. Quantitative values from synthetic MRI correlate with breast cancer subtypes[J/OL]. Life (Basel), 2022, 12(9): 1307 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/36143344/. DOI: 10.3390/life12091307.
[52]
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 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/33857828/. DOI: 10.1016/j.ejrad.2021.109697.
[53]
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 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/38905803/. DOI: 10.1016/j.ejrad.2024.111573.
[54]
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 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/33309922/. DOI: 10.1016/j.mri.2020.12.001.
[55]
GOLDHIRSCH A, WOOD W C, COATES A S, et al. Strategies for subtypes: dealing with the diversity of breast cancer: highlights of the St. Gallen international expert consensus on the primary therapy of early breast cancer 2011[J]. Ann Oncol, 2011, 22(8): 1736-1747. DOI: 10.1093/annonc/mdr304.
[56]
LIANG T, TANG W W, ZHOU H M, et al. Application research on the use of synthetic MRI to identify HER-2 expression status in breast cancer[J]. Chin J CT MRI, 2025, 23(6): 91-94. DOI: 10.3969/j.issn.1672-5131.2025.06.027.
[57]
GILLIES R J, ANDERSON A R, GATENBY R A, et al. The biology underlying molecular imaging in oncology: from genome to anatome and back again[J]. Clin Radiol, 2010, 65(7): 517-521. DOI: 10.1016/j.crad.2010.04.005.
[58]
LAMBIN P, LEIJENAAR R T H, DEIST T M, et al. Radiomics: the bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol, 2017, 14(12): 749-762. DOI: 10.1038/nrclinonc.2017.141.
[59]
WANG Z P, FANG M J, ZHANG J, et al. Radiomics and deep learning in nasopharyngeal carcinoma: a review[J/OL]. IEEE Rev Biomed Eng, 2024, 17: 118-135 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/37097799/. DOI: 10.1109/RBME.2023.3269776.
[60]
CONTI A, DUGGENTO A, INDOVINA I, et al. Radiomics in breast cancer classification and prediction[J/OL]. Semin Cancer Biol, 2021, 72: 238-250 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/32371013/. DOI: 10.1016/j.semcancer.2020.04.002.
[61]
AERTS H J W L. The potential of radiomic-based phenotyping in precision medicine: a review[J]. JAMA Oncol, 2016, 2(12): 1636-1642. DOI: 10.1001/jamaoncol.2016.2631.
[62]
LI H, ZHU Y T, BURNSIDE E S, et al. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set[J/OL]. NPJ Breast Cancer, 2016, 2: 16012 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/27853751/. DOI: 10.1038/npjbcancer.2016.12.
[63]
SONG L R, LI C L, YIN J D. Texture analysis using semiquantitative kinetic parameter maps from DCE-MRI: preoperative prediction of HER2 status in breast cancer[J/OL]. Front Oncol, 2021, 11: 675160 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/34168994/. DOI: 10.3389/fonc.2021.675160.
[64]
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 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/36158700/. DOI: 10.3389/fonc.2022.922185.
[65]
XU A Q, CHU X F, ZHANG S J, et al. Development and validation of a clinicoradiomic nomogram to assess the HER2 status of patients with invasive ductal carcinoma[J/OL]. BMC Cancer, 2022, 22(1): 872 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/35945526/. DOI: 10.1186/s12885-022-09967-6.
[66]
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.
[67]
ZHANG G S, SHI Y, YIN P P, et al. A machine learning model based on ultrasound image features to assess the risk of sentinel lymph node metastasis in breast cancer patients: Applications of scikit-learn and SHAP[J/OL]. Front Oncol, 2022, 12: 944569 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/35957890/. DOI: 10.3389/fonc.2022.944569.
[68]
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.
[69]
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.
[70]
EJIYI C J, QIN Z, MONDAY H, et al. Breast cancer diagnosis and management guided by data augmentation, utilizing an integrated framework of SHAP and random augmentation[J]. Biofactors, 2024, 50(1): 114-134. DOI: 10.1002/biof.1995.
[71]
HU J L, XU J, LI M, et al. Identification and validation of an explainable prediction model of acute kidney injury with prognostic implications in critically ill children: a prospective multicenter cohort study[J/OL]. EClinicalMedicine, 2024, 68: 102409 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/38273888/. DOI: 10.1016/j.eclinm.2023.102409.
[72]
MA M W, LIU R Y, WEN C J, et al. Predicting the molecular subtype of breast cancer and identifying interpretable imaging features using machine learning algorithms[J]. Eur Radiol, 2022, 32(3): 1652-1662. DOI: 10.1007/s00330-021-08271-4.
[73]
HUANG Y, WANG X X, CAO Y, et al. Multiparametric MRI model to predict molecular subtypes of breast cancer using Shapley additive explanations interpretability analysis[J]. Diagn Interv Imag, 2024, 105(5): 191-205. DOI: 10.1016/j.diii.2024.01.004.
[74]
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 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/39413232/. DOI: 10.2214/AJR.24.31717.
[75]
GILLIES R J, KINAHAN P E, HRICAK H. Radiomics: images are more than pictures, they are data[J]. Radiology, 2016, 278(2): 563-577. DOI: 10.1148/radiol.2015151169.
[76]
SUN S M, WANG R L, BI C L, et al. Research progress on artificial intelligence in MRI for breast cancer diagnosis and treatment response prediction[J]. Chin J Magn Reson Imag, 2025, 16(2): 193-197, 228. DOI: 10.12015/issn.1674-8034.2025.02.031.
[77]
FRANCHINA M, PIZZIMENTI C, FIORENTINO V, et al. Low and ultra-low HER2 in human breast cancer: an effort to define new neoplastic subtypes[J/OL]. Int J Mol Sci, 2023, 24(16): 12795 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/37628975/. DOI: 10.3390/ijms241612795.
[78]
GILLIES R J, BALAGURUNATHAN Y. Perfusion MR imaging of breast cancer: insights using "habitat imaging"[J]. Radiology, 2018, 288(1): 36-37. DOI: 10.1148/radiol.2018180271.
[79]
LI S L, DAI Y M, CHEN J Y, et al. MRI-based habitat imaging in cancer treatment: current technology, applications, and challenges[J/OL]. Cancer Imaging, 2024, 24(1): 107 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/39148139/. DOI: 10.1186/s40644-024-00758-9.
[80]
XUE Y F, WANG W W, MA S, et al. Advances in the application of magnetic resonance habitat imaging for the diagnosis and treatment of breast cancer[J]. Chin J Magn Reson Imag, 2025, 16(7): 154-159. DOI: 10.12015/issn.1674-8034.2025.07.025.
[81]
SUJIT S J, AMINU M, KARPINETS T V, et al. Enhancing NSCLC recurrence prediction with PET/CT habitat imaging, ctDNA, and integrative radiogenomics-blood insights[J/OL]. Nat Commun, 2024, 15(1): 3152 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/38605064/. DOI: 10.1038/s41467-024-47512-0.
[82]
LI Z H, CHEN X Y, ZHANG X L, et al. Prediction of different expression status of human epidermal growth factor receptor 2 in breast cancer by multi-parameter MRI habitat images[J]. Chin J Radiol, 2024(9): 909-915. DOI: 10.3760/cma.j.cn112149-20240228-00091.
[83]
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 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/39578913/. DOI: 10.1186/s13058-024-01921-7.
[84]
LI Q Y, LIANG Y, ZHANG L, et al. MRI-based habitat analysis for Intratumoral heterogeneity quantification combined with deep learning for HER2 status prediction in breast cancer[J/OL]. Magn Reson Imaging, 2025, 122: 110429 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/40414575/. DOI: 10.1016/j.mri.2025.110429.
[85]
DUFFY S W, TABÁR L, YEN A M, et al. Mammography screening reduces rates of advanced and fatal breast cancers: Results in 549, 091 women[J]. Cancer, 2020, 126(13): 2971-2979. DOI: 10.1002/cncr.32859.
[86]
ARIBAL E, ASADOV R, RAMAZAN A, et al. Multiparametric breast MRI with 3T: effectivity of combination of contrast enhanced MRI, DWI and 1H single voxel spectroscopy in differentiation of breast tumors[J]. Eur J Radiol, 2016, 85(5): 979-986. DOI: 10.1016/j.ejrad.2016.02.022.
[87]
CLAUSER P, KRUG B, BICKEL H, et al. Diffusion-weighted imaging allows for downgrading MR BI-RADS 4 lesions in contrast-enhanced MRI of the breast to avoid unnecessary biopsy[J]. Clin Cancer Res, 2021, 27(7): 1941-1948. DOI: 10.1158/1078-0432.CCR-20-3037.
[88]
YANG J R, ZHAO N N, ZHANG S N, et al. Application of multimodal radiomics nomogram in predicting HER-2 expression in breast cancer patients[J]. J Clin Radiol, 2024, 43(9): 1471-1477. DOI: 10.13437/j.cnki.jcr.2024.09.011.
[89]
WANG Q, ZHANG Z Q, HUANG C C, et al. Dual-modality virtual biopsy system integrating MRI and MG for noninvasive predicting HER2 status in breast cancer[J]. Acad Radiol, 2025, 32(7): 3858-3869. DOI: 10.1016/j.acra.2025.02.039.
[90]
WU J, LI Y X, GONG W Q, et al. Multi-modality radiomics diagnosis of breast cancer based on MRI, ultrasound and mammography[J/OL]. BMC Med Imaging, 2025, 25(1): 265 [2025-07-30]. https://pubmed.ncbi.nlm.nih.gov/40615781/. DOI: 10.1186/s12880-025-01767-1.

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