Department of Ultrasound in Medicine, Ningbo Medical Center Lihuili Hospital, No.1111, Jiangnan Road, Yinzhou District, Ningbo 315048, Zhejiang Province, China , yanyun_1975@163.com
Abstract: (553 Views)
Background:To evaluate the diagnostic efficacy of Magnetic resonance imaging (MRI) radiomics in predicting the overexpression of Human Epidermal Growth Factor Receptor 2(HER2) in breast cancer via meta-analysis, so as to investigate the correlation between MRI radiomics and HER2 overexpression in breast cancer. Materials and Methods: PubMed, Embase, and Web of Science were searched for studies on radiomics and HER2 overexpression in breast cancer (inception–September 2025). Two reviewers independently screened literature, extracted data, and assessed quality (RevMan 5.3). Pooled sensitivity (SEN), specificity (SPE), and AUC were calculated (Stata 17.0). Heterogeneity and bias were evaluated via subgroup/sensitivity analyses and Deeks’ test. Results: 13 studies (4,756 patients) were included. The training set showed pooled SEN 0.90(95%CI:0.81-0.95), SPE 0.82(95%CI:0.72-0.89), and AUC 0.92(95%CI:0.90-0.94). The validation set had SEN 0.74(95%CI:0.62-0.83), SPE 0.80(95%CI:0.70-0.87), and AUC 0.84(95%CI:0.80-0.87). Heterogeneity (I²> 75%) stemmed from sample size and country differences (both P<0.05). Fagan’s plot indicated radiomics increased HER2 overexpression post-test probability to 48%-56% (from 20% pre-test) and reduced negative results to 3%-8%. Sensitivity analysis confirmed robustness; no publication bias was detected (P>0.05). Conclusion: MRI Radiomics correlates with HER2 overexpression in breast cancer and demonstrates high diagnostic accuracy, offering a novel approach for HER2 evaluation.
1. Arnold M, Morgan E, Rumgay H, et al. (2022) Current and future burden of breast cancer: Global statistics for 2020 and 2040. Breast, 66 :15-23. [DOI:10.1016/j.breast.2022.08.010]
2. Yoon J, Oh DY (2024) HER2-targeted therapies beyond breast cancer - an update. Nat Rev Clin Oncol, 21(9):675-700. [DOI:10.1038/s41571-024-00924-9]
3. Kunte S, Abraham J, Montero AJ (2020) Novel HER2-targeted therapies for HER2-positive metastatic breast cancer. Cancer,126(19): 4278-4288. [DOI:10.1002/cncr.33102]
4. Spaziani S, Esposito A, Barisciano G, et al. (2024) Combined SERS-Raman screening of HER2-overexpressing or silenced breast cancer cell lines. J Nanobiotechnology, 22(1): 350. [DOI:10.1186/s12951-024-02600-7]
5. Nguyen HT, Migliozzi D, Bisig B, et al. (2019) High-content, cell-by-cell assessment of HER2 overexpression and amplification: a tool for intratumoral heterogeneity detection in breast cancer. Lab Invest, 99(5): 722-732. [DOI:10.1038/s41374-018-0172-y]
6. Conti A, Duggento A, Indovina I, et al. (2021) Radiomics in breast cancer classification and prediction. Semin Cancer Biol, 72: 238-250. [DOI:10.1016/j.semcancer.2020.04.002]
7. Wang H, Sang L, Xu J, et al. (2024) Multiparametric MRI-based radiomic nomogram for predicting HER-2 2+ status of breast cancer. Heliyon, 10(9): e29875. [DOI:10.1016/j.heliyon.2024.e29875]
8. Shen L, Li Y, Huang H, et al. (2024) HER2 in Gastric Cancer: A Comprehensive Analysis Combining Meta-Analysis and DCE-MRI Radiomics. Cancer Control, 31: 10732748241293699. [DOI:10.1177/10732748241293699]
9. Wolff AC, Hammond MEH, Allison KH, et al. (2018) 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 Clin Oncol, 36(20): 2105-2122. [DOI:10.1200/JCO.2018.77.8738]
10. Whiting PF, Rutjes AW, Westwood ME, et al. (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med, 155(8): 529-36. [DOI:10.7326/0003-4819-155-8-201110180-00009]
11. Zheng S, Yang Z, Du G, et al. (2024) Discrimination between HER2-overexpressing, -low-expressing, and -zero-expressing statuses in breast cancer using multiparametric MRI-based radiomics. Eur Radiol, 34(9): 6132-6144. [DOI:10.1007/s00330-024-10641-7]
12. Bitencourt AGV, Gibbs P, Rossi Saccarelli C, et al. (2020) MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer. EBioMedicine, 61: 103042. [DOI:10.1016/j.ebiom.2020.103042]
13. Guo Y, Xie X, Tang W, et al. (2024) Noninvasive identification of HER2-low-positive status by MRI-based deep learning radiomics predicts the disease-free survival of patients with breast cancer. Eur Radiol, 34(2): 899-913. [DOI:10.1007/s00330-023-09990-6]
14. Song X, Xu H, Wang X, et al. (2024) Use of ultrasound imaging Omics in predicting molecular typing and assessing the risk of postoperative recurrence in breast cancer. BMC Womens Health, 24(1): 380. [DOI:10.1186/s12905-024-03288-5]
15. Sheng W, Xia S, Wang Y, et al. (2022) Invasive ductal breast cancer molecular subtype prediction by MRI radiomic and clinical features based on machine learning. Front Oncol, 12: 964605. [DOI:10.3389/fonc.2022.964605]
16. Jeong J, Ham S, Seo BK, et al. (2025) Superior performance in classification of breast cancer molecular subtype and histological factors by radiomics based on ultrafast MRI over standard MRI: evidence from a prospective study. Radiol Med, 130(3): 368-380. [DOI:10.1007/s11547-025-01956-6]
17. Xu A, Chu X, Zhang S, et al. (2022) Development and validation of a clinicoradiomic nomogram to assess the HER2 status of patients with invasive ductal carcinoma. BMC Cancer, 22(1):872. [DOI:10.1186/s12885-022-09967-6]
18. Luo HJ, Ren JL, Mei Guo L, et al. (2024) MRI-based machine learning radiomics for prediction of HER2 expression status in breast invasive ductal carcinoma. Eur J Radiol Open, 13:100592. [DOI:10.1016/j.ejro.2024.100592]
19. Huang Y, Zhu T, Zhang X, et al. (2023) Longitudinal MRI-based fusion novel model predicts pathological complete response in breast cancer treated with neoadjuvant chemotherapy: a multicenter, retrospective study. EClinicalMedicine, 58: 101899. [DOI:10.1016/j.eclinm.2023.101899]
20. Yue WY, Zhang HT, Gao S, et al. (2023) Predicting breast cancer subtypes using magnetic resonance imaging based radiomics with automatic segmentation. J Comput Assist Tomogr, 47(5): 729-737. [DOI:10.1097/RCT.0000000000001474]
21. Shang Y, Wang Y, Guo Y, et al. (2024) The Clinical Study of Intratumoral and Peritumoral Radiomics Based on DCE-MRI for HER-2 Positive and Low Expression Prediction in Breast Cancer. Breast Cancer (Dove Med Press), 16: 957-972. [DOI:10.2147/BCTT.S497770]
22. Chen X, Li M, Liang X, et al. (2025) Machine learning model based on the radiomics features of CE-CBBCT shows promising predictive ability for HER2-positive BC. Medicine (Baltimore), 104(37): e44300. [DOI:10.1097/MD.0000000000044300]
23. Huang Y, Wei L, Hu Y, et al. (2021) Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer. Front Oncol, 11: 706733. [DOI:10.3389/fonc.2021.706733]
24. Gillies RJ, Anderson AR, Gatenby RA, et al. (2010) The biology underlying molecular imaging in oncology: from genome to anatome and back again. Clin Radiol, 65(7): 517-21. [DOI:10.1016/j.crad.2010.04.005]
25. Lambin P, Leijenaar RTH, Deist TM, et al. (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol, 4(12): 749-762. [DOI:10.1038/nrclinonc.2017.141]
26. Guiot J, Vaidyanathan A, Deprez L, et al. (2022) A review in radiomics: Making personalized medicine a reality via routine imaging. Med Res Rev, 42(1): 426-440. [DOI:10.1002/med.21846]
27. Rogers W, Thulasi Seetha S, Refaee TAG, et al. (2020) Radiomics: from qualitative to quantitative imaging. Br J Radiol, 93(1108): 20190948. [DOI:10.1259/bjr.20190948]
28. Zhou J, Yu X, Wu Q, et al. (2024) Radiomics analysis of intratumoral and different peritumoral regions from multiparametric MRI for evaluating HER2 status of breast cancer: A comparative study. Heliyon, 10(7): e28722. [DOI:10.1016/j.heliyon.2024.e28722]
29. Ramtohul T, Djerroudi L, Lissavalid E, et al. (2023) Multiparametric MRI and Radiomics for the Prediction of HER2-Zero, -Low, and -Positive Breast Cancers. Radiology, 308(2): e222646. [DOI:10.1148/radiol.222646]
30. Ma T, Cui J, Wang L, et al. (2022) A CT-based radiomics signature for prediction of HER2 overexpression and treatment efficacy of trastuzumab in advanced gastric cancer. Transl Cancer Res, 11(12):4326-4337. [DOI:10.21037/tcr-22-1690]
31. Fu Y, Zhou J, Li J (2024) Diagnostic performance of ultrasound-based artificial intelligence for predicting key molecular markers in breast cancer: A systematic review and meta-analysis. PLoS One, 19(5): e0303669. [DOI:10.1371/journal.pone.0303669]
32. Zhang L, Cui QX, Zhou LQ, et al. (2024) MRI-based vector radiomics for predicting breast cancer HER2 status and its changes after neoadjuvant therapy. Comput Med Imaging Graph, 118: 102443. [DOI:10.1016/j.compmedimag.2024.102443]
33. Zhang Y, Huang H, Yin L, et al. (2024) Preoperative prediction of HER-2 expression status in breast cancer based on MRI radiomics model. Zhonghua Zhong Liu Za Zhi, 46(5): 428-437.
34. Deng K, Chen T, Leng Z, et al. (2024) Radiomics as a tool for prognostic prediction in transarterial chemoembolization for hepatocellular carcinoma: a systematic review and meta-analysis. Radiol Med, 129(8): 1099-1117. [DOI:10.1007/s11547-024-01840-9]
35. Abbaspour E, Karimzadhagh S, Monsef A, et al. (2024) Application of radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis. Int J Surg, 110(6): 3795-3813. [DOI:10.1097/JS9.0000000000001239]
36. Dong F, Li J, Wang J, et al. (2024) Diagnostic performance of DCE-MRI radiomics in predicting axillary lymph node metastasis in breast cancer patients: A meta-analysis. PLoS One, 19(12): e0314653. [DOI:10.1371/journal.pone.0314653]
37. Xiao Q, Zhu W, Tang H, et al. (2023) Ultrasound radiomics in the prediction of microvascular invasion in hepatocellular carcinoma: A systematic review and meta-analysis. Heliyon, 9(6): e16997. [DOI:10.1016/j.heliyon.2023.e16997]
38. Ma D, Zhou T, Chen J, et al. (2024) Radiomics diagnostic performance for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis. BMC Med Imaging, 24(1): 144. [DOI:10.1186/s12880-024-01278-5]
39. Liang Y, Wei Y, Xu F, et al. (2024) MRI-based radiomic models for the preoperative prediction of extramural venous invasion in rectal cancer: A systematic review and meta-analysis. Clin Imaging, 110: 110146. [DOI:10.1016/j.clinimag.2024.110146]
40. Cai L, Sidey-Gibbons C, Nees J, et al. (2024) Can multi-modal radiomics using pretreatment ultrasound and tomosynthesis predict response to neoadjuvant systemic treatment in breast cancer? Eur Radiol, 34(4): 2560-2573. [DOI:10.1007/s00330-023-10238-6]
41. Park VY (2020) Expanding applications of MRI-based radiomics in HER2-positive breast cancer. EBioMedicine, 61:103085. [DOI:10.1016/j.ebiom.2020.103085]
Lin Q, Kong X, Yan Y. Meta-analysis of the correlation between MRI radiomics and HER2 overexpression in breast cancer. Int J Radiat Res 2026; 24 (3) :609-617 URL: http://ijrr.com/article-1-7173-en.html