Keywords
Breast cancer
Diagnosis and treatment
Research progress
Submitted : 2024-05-05
Accepted : 2024-05-20
Published : 2024-06-04
Abstract
With the advancement of medical research in recent years and the frequent occurrence of different types of cancer, breast cancer has gradually attracted the public’s attention. The incidence of breast cancer is rising, mainly affecting women with a high mortality rate. According to the clinical treatment effect, early diagnosis and early treatment can effectively control the mortality of breast cancer and improve patient’s quality of life. Ultrasound radiomics is an emerging field that can extract quantitative high-dimensional data from ultrasound images. Recently, ultrasound radiomics has been widely used in the clinical treatment of breast cancer. This paper analyzed the research progress of ultrasound radiomics in the diagnosis and treatment of breast cancer.
References
Sung H, Ferlay J, Siegel RL, et al., 2021, Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin, 71(3): 209–249. https://doi.org/10.3322/caac.21660
Lambin P, Rios-Velazquez E, Leijenaar R, et al., 2012, Radiomics: Extracting More Information from Medical Images Using Advanced Feature Analysis. Eur J Cancer, 48(4): 441–446. https://doi.org/10.1016/j.ejca.2011.11.036
Gao Y, Luo Y, Zhao C, et al., 2021, Nomogram Based on Radiomics Analysis of Primary Breast Cancer Ultrasound Images: Prediction of Axillary Lymph Node Tumor Burden in Patients. Eur Radiol, 31(2): 928–937. https://doi.org/10.1007/s00330-020-07181-1
DiCenzo D, Quiaoit K, Fatima K, et al., 2020, Quantitative Ultrasound Radiomics in Predicting Response to Neoadjuvant Chemotherapy in Patients with Locally Advanced Breast Cancer: Results from Multi-Institutional Study. Cancer Med, 9(16): 5798–5806. https://doi.org/10.1002/cam4.3255
Lowerison MR, Tse JJ, Hague MN, et al., 2017, Compound Speckle Model Detects Anti-Angiogenic Tumor Response in Preclinical Nonlinear Contrast-Enhanced Ultrasonography. Med Phys, 44(1): 99–111. https://doi.org/10.1002/mp.12030
Choi JS, Han BK, Ko EY, et al., 2016, Additional Diagnostic Value of Shear-Wave Elastography and Color Doppler US for Evaluation of Breast Non-Mass Lesions Detected at B-Mode US. Eur Radiol, 26(10): 3542–3549. https://doi.org/10.1007/s00330-015-4201-6
Suo JF, Zhang Q, Chang WY, et al., 2017, Assessment of Axillary Lymph Node Metastasis Based on Bimodal Ultrasound Images Relying on Elastography and B-Mode. Chin J Med Instrum, 41(5): 313–316 + 326.
Kapetas P, Clauser P, Woitek R, et al., 2019, Quantitative Multiparametric Breast Ultrasound: Application of Contrast-Enhanced Ultrasound and Elastography Leads to an Improved Differentiation of Benign and Malignant Lesions. Invest Radiol, 54(5): 257–264. https://doi.org/10.1097/RLI.0000000000000543
Liu BH, Jiang F, Yan N, et al., 2019, Clinical Study on the Relationship between Breast Cancer Hormone Receptor Expression and Ultrasound Imaging. J Clin Ultrasound Med, 21(11): 834–836. https://doi.org/10.3969/j.issn.1008-6978.2019.11.014
Li JW, Shi ZT, Guo Y, et al., 2017, Exploratory Study on the Predictive Value of Ultrasound Imagingomics for Hormone Receptor Expression in Invasive Breast Cancer. Oncol Imaging, 26(2): 128–135.
Zhang FQ, Yang H, Chen K, et al., 2021, Diagnostic Efficacy Analysis of SPECT/CT Imaging, Conventional Ultrasound, and Molybdenum Target Imaging for Axillary Lymph Node Metastasis in Breast Cancer. Mod Oncol Med, 29(15): 2704–2708.
Duan SY, Yang XD, Sun L, et al., 2018, MRI Features of False-Negative Breast Cancer Detected by Molybdenum Target X-Ray. Chin J Med Imaging Technol, 26(9): 654–657.
Zhou SC, Liu TT, Zhou J, et al., 2017, Preliminary Study on the Application of Imagingomics in Thyroid Cancer. Oncol Imaging, 26(2): 102–105. https://doi.org/10.3969/j.issn.1008-617X.2017.02.004
Zhang Q, Xiao Y, Suo J, et al., 2017, Sonoelastomics for Breast Tumor Classification: A Radiomics Approach with Clustering-Based Feature Selection on Sonoelastography. Ultrasound Med Biol, 43(5): 1058–1069.