Progress and Clinical Application Value of Radiomics in Intratumoral and Peritumoral Regions of Breast Cancer

Authors

  • Sisi Liang
  • Huilan Tan
  • Peng Zhang

DOI:

https://doi.org/10.54097/k58t5518

Keywords:

Breast Cancer, Radiomics, Peritumoral Region, Clinical Value

Abstract

Breast cancer is one of the most common malignant tumors in women. By extracting high-throughput features from medical images, radiomics non-invasively reveals tumor heterogeneity and provides new strategies for the precise diagnosis and treatment of breast cancer. Previous studies mainly focused on intratumoral features, while rarely explored the crucial role of peritumoral features in tumorigenesis, progression and metastasis. In recent years, intratumoral and peritumoral radiomics has gradually become a research hotspot. This paper systematically reviews the research progress of intratumoral and peritumoral radiomics based on ultrasound, MRI, PET and mammography in benign and malignant differentiation, molecular subtyping, lymph node metastasis prediction and neoadjuvant chemotherapy response assessment of breast cancer. It aims to provide comprehensive imaging evidence for clinical practice and promote the improvement and optimization of precise medical models for breast cancer.

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References

[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] Wang Zhaohua, Ma Yanyun, Cui Caozhe, et al. The value of radiomics based on full-field digital mammography in different breast parenchymal regions in differentiating BI-RADS category 4 lesions[J]. Journal of Clinical Radiology, 2024, 43(03): 346-351. DOI: 10.13437/j.cnki.jcr.2024.03.007.

[3] Lin X, Zhuang S, Yang S, et al. Development and internal validation of a conventional ultrasound-based nomogram for predicting malignant nonmasslike breast lesions[J]. Quant Imaging Med Surg, 2022,12(12):5452-5461.DOI:10. 21037/ qims-22-378.

[4] 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.

[5] Lambin P, Leijenaar R T H, Deist T M, et al. Radiomics: the bridge between medical imaging and personalized medicine[J]. Nature Reviews Clinical Oncology, 2017,14(12):749-762.DOI: 10. 1038/nrclinonc.2017.141.

[6] Han Jianjian, Ma Peiqi, Wang Xiaolei, et al. A multicenter study on predicting histological grade of invasive breast cancer based on digital mammographic radiomics nomogram[J]. Radiologic Practice, 2024, 39(05): 634-640. DOI:10.13609/ j.cnki. 1000-0313.2024.05.012.

[7] Wang Hui, Chen Fei, Li Jinqiao, et al. Prediction of HER2 status by combining ABVS ultrasound radiomics features, ABVS ultrasound and serological features in breast cancer[J]. Chinese Journal of Ultrasound in Medicine, 2024, 40(05): 516-520.

[8] Xie Zongyu, Yao Wenyu, Yang Jingru, et al. Study on predicting vasculogenic mimicry expression status of triple-negative breast cancer based on DCE-MRI radiomics nomogram[J]. Journal of Bengbu Medical College, 2024, 49(04): 425-430. DOI: 10.13898/j.cnki.issn.1000-2200.2024. 04.002.

[9] Zhao Z, Xiong S, Wang R, et al. Peri-tumor fibroblasts promote tumorigenesis and metastasis of hepatocellular carcinoma via Interleukin6/STAT3 signaling pathway[J]. Cancer Manag Res, 2019,11:2889-2901.DOI:10.2147/CMAR.S192263.

[10] Hwang H W, Jung H, Hyeon J, et al. A nomogram to predict pathologic complete response (pCR) and the value of tumor-infiltrating lymphocytes (TILs) for prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer patients[J]. Breast Cancer Res Treat, 2019,173(2):255-266.DOI: 10. 1007/ s10549-018-4981-x.

[11] Zhang Yuan, Yu Zhuoyue, Sun Lixin, et al. Effects of MEOX1 expression on cell proliferation, invasion and migration in ovarian cancer tissues[J]. Chinese Journal of Cancer Prevention and Treatment, 2020, 27(04): 262-268. DOI:10. 16073/ j.cnki.cjcpt.2020.04.03.

[12] Wu Qian, Lin Mianlei, Zhao Caitao, et al. Research progress of MRI radiomics in diagnosis and treatment of breast cancer[J]. Journal of Imaging Research and Medical Applications, 2025,9(3):1-4.DOI:10.20267/j.issn.2096-3807.2025.03.001.

[13] Zhou J, Zhang Y, Chang K, et al. Diagnosis of Benign and Malignant Breast Lesions on DCE-MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue[J]. J Magn Reson Imaging, 2020,51(3):798-809.DOI:10. 1002/ jmri.26981.

[14] Hu Y, Cai Z, Aierken N, et al. Intra- and peri-tumoral radiomics based on dynamic contrast-enhanced MRI for prediction of benign disease in BI-RADS 4 breast lesions: a multicentre study[J]. Radiat Oncol, 2025,20(1):27.DOI:10.1186/s13014-025-02605-y.

[15] Han Y, Huang M, Xie L, et al. The value of intratumoral and peritumoral radiomics features based on multiparametric MRI for predicting molecular staging of breast cancer[J]. Front Oncol, 2025,15:1379048.DOI:10.3389/fonc.2025.1379048.

[16] Cao M, Liu X, Yang A, et al. Prediction of HER-2 expression status in breast cancer based on multi-parameter MRI intratumoral and peritumoral radiomics[J]. Magn Reson Imaging, 2025, 122:110434.DOI:10.1016/j.mri.2025.110434.

[17] Liu Y, Li X, Zhu L, et al. Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Based on Intratumoral and Peritumoral DCE-MRI Radiomics Nomogram[J]. Contrast Media Mol Imaging, 2022,2022: 6729473. DOI:10.1155/2022/6729473.

[18] Zhu Y, Zhang S, Wei W, et al. Intra- and peritumoral radiomics nomogram based on DCE-MRI for the early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer[J]. Front Oncol, 2025,15: 1561599. DOI:10. 3389/fonc.2025.1561599.

[19] Chen X, Luo Y, Xie Z, et al. Prediction of neoadjuvant chemotherapy efficacy in breast cancer: integrating multimodal imaging and clinical features[J]. BMC Med Imaging, 2025, 25 (1):118.DOI:10.1186/s12880-025-01631-2.

[20] Zheng G, Peng J, Shu Z, et al. Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer patients: use of MRI radiomics data from three regions with multiple machine learning algorithms[J]. J Cancer Res Clin Oncol, 2024, 150(3): 147.DOI:10.1007/s00432-024-05680-y.

[21] Hong D, Peng J, Xu P, et al. A Multimodal Fusion Model of Radiomics and Deep Learning Integrating the Tumor Microenvironment Accurately Predicts Pathological Complete Response in Breast Cancer[J]. Acad Radiol, 2026.DOI:10. 1016/j. acra.2026.01.016.

[22] Guo S, Huang X, Xu C, et al. Multiregional radiomic model for breast cancer diagnosis: value of ultrasound-based peritumoral and parenchymal radiomics[J]. Quantitative Imaging in Medicine and Surgery, 2023,13(5):3127-3139.DOI:10. 21037/ qims-22-939.

[23] Zou Qiao, Liu Jinhui, Leng Xiaoling, et al. Predictive value of ultrasound radiomics model for benign and malignant BI-RADS category 4 breast lesions[J]. Chinese Journal of Radiological Health, 2025, 34(2): 179-185. DOI: 10.13491/j. issn. 1004-714X.2025.02.006.

[24] Du Yao, Wu Meng, Wang Yuhua, et al. Prediction of axillary lymph node metastasis based on intratumoral and peritumoral ultrasound radiomics features of primary breast cancer lesions[J]. Chinese Journal of Medical Imaging, 2025, 33(10): 1056-1062. DOI: 10.3969/j.issn.1005-5185.2025.10.006.

[25] Wang Xiaoyan. The value of a model constructed based on intratumoral and peritumoral ultrasound radiomic features in predicting Ki-67 expression level of breast cancer[J]. Chinese Community Doctors, 2025, 41(5):78-80. DOI:10.3969/j.issn. 1007-614x.2025.05.026.

[26] Wei C, Jia Y, Gu Y, et al. Predictive Analysis of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Using Multi-Region Ultrasound Imaging Features Combined With Pathological Parameters[J].Ultrasound Med Biol, 2025,51(12):2205-2216. DOI:10.1016/j.ultrasmedbio.2025.08.027.

[27] Yao J, Zhou W, Jia X, et al. Machine learning prediction of pathological complete response to neoadjuvant chemotherapy with peritumoral breast tumor ultrasound radiomics: compare with intratumoral radiomics and clinicopathologic predictors [J]. Breast Cancer Res Treat, 2025,212(2):325-336.DOI: 10. 1007/s10549-025-07727-1.

[28] Wang S, YuqiSun, RuiminLi, et al. Diagnostic performance of perilesional radiomics analysis of contrast-enhanced mammography for the differentiation of benign and malignant breast lesions[J]. European radiology, 2022,32(1):639-649.

[29] Zhang S, Shao H, Li W, et al. Intra- and peritumoral radiomics for predicting malignant BiRADS category 4 breast lesions on contrast-enhanced spectral mammography: a multicenter study[J]. Eur Radiol, 2023,33(8):5411-5422.DOI:10. 1007/ s00 330-023-09513-3.

[30] Chen Xiuting, Li Xinxin, Zhou Dawei, et al. Nomogram based on intratumoral and peritumoral breast tomosynthesis radiomics for effective prediction of axillary lymph node metastasis in breast cancer[J]. Journal of Molecular Imaging, 2024, 47(5): 465-473. DOI: 10.12122/j.issn.1674-4500. 2024. 05.03.

[31] Niu S, Jiang W, Zhao N, et al. Intra- and peritumoral radiomics on assessment of breast cancer molecular subtypes based on mammography and MRI[J]. J Cancer Res Clin Oncol, 2022, 148 (1):97-106.DOI:10.1007/s00432-021-03822-0.

[32] Guo Yunchan, Yang Caixian, Shi Jinwei. The value of DBT-based radiomics in predicting molecular subtypes of breast cancer[J]. Journal of Medical Imaging, 2023, 33(9): 1598-1602.

[33] Mao N, Shi Y, Lian C, et al. Intratumoral and peritumoral radiomics for preoperative prediction of neoadjuvant chemotherapy effect in breast cancer based on contrast-enhanced spectral mammography[J]. Eur Radiol, 2022,32(5): 3207- 3219.DOI:10.1007/s00330-021-08414-7.

[34] Hu Congying, Hu Wei, Zhao Shuang, et al. Progress in imaging evaluation of neoadjuvant chemotherapy efficacy for breast cancer[J]. Radiologic Practice, 2024, 39(11):1537-1544. DOI:10.13609/j.cnki.1000-0313.2024.11.018.

[35] Vogl W, Pinker K, Helbich T H, et al. Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features[J]. Eur Radiol Exp, 2019,3(1):18.DOI:10.1186/s41747-019-0096-3.

[36] Chen Qiaoliang, Qin Xinyan, Lai Ruihe, et al. Diagnostic value of intratumoral and peritumoral 18F-FDG PET metabolic parameters in triple-negative breast cancer[J]. Chinese Journal of NuclearMedicine and Molecular Imaging, 2026, 46(1):13-18. DOI:10.3760/cma.j.cn321828-20241014-00348.

[37] Hou X, Chen K, Wan X, et al. Intratumoral and peritumoral radiomics for preoperative prediction of neoadjuvant chemotherapy effect in breast cancer based on 18F-FDG PET/CT[J]. Journal of Cancer Research and Clinical Oncology, 2024,150(11).DOI:10.1007/s00432-024-05987-w.

[38] Liu Jin, Zhang Weiyuan, Zhang Zhiyi, et al. Research progress of PET radiomics in breast cancer[J]. Journal of Oncologic Imaging, 2022, 31(04): 449-454. DOI: 10.19732/j.cnki.2096-6210.2022.04.017.

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Published

29-04-2026

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How to Cite

Liang, S., Tan, H., & Zhang, P. (2026). Progress and Clinical Application Value of Radiomics in Intratumoral and Peritumoral Regions of Breast Cancer. International Journal of Biology and Life Sciences, 14(1), 95-99. https://doi.org/10.54097/k58t5518