背景组织特征在乳腺超声中的研究进展
Research Progress on Background Tissue Characteristics in Breast Ultrasound
摘要: 乳腺超声背景组织特征与病灶检出、诊断及乳腺癌风险评估关系密切。本文从背景回声异质性、腺体组织成分(Glandular Tissue Component, GTC)和自动乳腺全容积超声三个方面进行综述，重点说明这些特征在病灶显示、诊断判断、组织组成评价及乳腺癌风险研究中的价值。复杂背景会降低病灶与周围组织的声学对比度，使小病灶、等回声病灶及非肿块样异常的识别更加困难；GTC则从腺体成分保留角度，为致密乳腺女性提供额外风险评估信息。未来需要建立标准化评价体系，并开展多中心前瞻性研究，以明确其临床价值，为乳腺癌个体化筛查和精准诊疗提供依据。
Abstract: Background tissue features on breast ultrasound are closely associated with lesion detection, diagnosis and breast cancer risk assessment. This paper reviews three aspects including background echogenic heterogeneity, glandular tissue component (GTC) and automated breast volume scanner ultrasound, and highlights their values in lesion visualization, diagnostic judgment, tissue composition evaluation and breast cancer risk research. Complex background reduces the acoustic contrast between lesions and surrounding tissues, bringing difficulties to the identification of small lesions, isoechoic lesions and non-mass abnormalities. From the perspective of glandular tissue retention, GTC provides additional risk assessment information for women with dense breasts. Standardized evaluation systems and multicenter prospective studies are required in the future to clarify their clinical value and lay a foundation for individualized screening and precise diagnosis and treatment of breast cancer.
文章引用：张若楠, 吴琼, 王影. 背景组织特征在乳腺超声中的研究进展[J]. 临床医学进展, 2026, 16(9): 805-812. https://doi.org/10.12677/acm.2026.1693108

参考文献

[1] Chang, J.M., Leung, J.W.T., Heacock, L., Lee, S.H., Moon, W.K. and Hooley, R.J. (2026) Breast US: State of the Art. Radiology, 318, e233101.
https://doi.org/10.1148/radiol.233101
[2] Expert Panel on Breast Imaging, Paulis, L.V., Lewin, A.A., et al. (2025) ACR Appropriateness Criteria® Supplemental Breast Cancer Screening Based on Breast Density: 2024 Update. Journal of the American College of Radiology, 22, S405-S423.
https://doi.org/10.1016/j.jacr.2025.02.023
[3] Bechyna, S. and Baltzer, P.A.T. (2025) Impact of Background Parenchymal Enhancement on Diagnostic Performance of Breast MRI: A Systematic Review and Meta-Analysis. Radiology, 315, e241919.
https://doi.org/10.1148/radiol.241919
[4] D’Orsi, C.J., Sickles, E.A., Mendelson, E.B. and Morris, E.A. (2013) ACR BI-RADS Atlas, Breast Imaging Reporting and Data System. American College of Radiology.
[5] Minichetti, P., Cereser, L., Versienti, E., Francioso, F., Sparascio, F., Girometti, R., et al. (2026) BI-RADS V2025: Key Updates and Implications for Breast Imaging Practice. American Journal of Roentgenology.
https://doi.org/10.2214/ajr.26.35051
[6] Ko, K.H., Jung, H.K. and Kim, I. (2017) Analysis of Background Parenchymal Echogenicity on Breast Ultrasound: Correlation with Mammographic Breast Density and Background Parenchymal Enhancement on Magnetic Resonance Imaging. Medicine, 96, e7850.
https://doi.org/10.1097/md.0000000000007850
[7] Kim, W.H., Lee, S.H., Chang, J.M., Cho, N. and Moon, W.K. (2017) Background Echotexture Classification in Breast Ultrasound: Inter-Observer Agreement Study. Acta Radiologica, 58, 1427-1433.
https://doi.org/10.1177/0284185117695665
[8] 赵若兰, 王影. 女性乳腺超声背景特征与乳腺癌关联性的研究进展[J]. 河北医科大学学报, 2022, 43(11): 1351-1355.
[9] 宋宏萍, 舒瑞. ACR第6版BI-RADS超声部分更新内容解读[J]. 中华超声影像学杂志, 2025, 34(5): 369-376.
[10] Lee, S.H. and Moon, W.K. (2022) Glandular Tissue Component on Breast Ultrasound in Dense Breasts: A New Imaging Biomarker for Breast Cancer Risk. Korean Journal of Radiology, 23, 574-580.
https://doi.org/10.3348/kjr.2022.0099
[11] Lee, S.H., Ryu, H.S., Jang, M., Yi, A., Ha, S.M., Kim, S., et al. (2021) Glandular Tissue Component and Breast Cancer Risk in Mammographically Dense Breasts at Screening Breast Us. Radiology, 301, 57-65.
https://doi.org/10.1148/radiol.2021210367
[12] Monticciolo, D.L., Newell, M.S., Moy, L., Lee, C.S. and Destounis, S.V. (2023) Breast Cancer Screening for Women at Higher-than-Average Risk: Updated Recommendations from the ACR. Journal of the American College of Radiology, 20, 902-914.
https://doi.org/10.1016/j.jacr.2023.04.002
[13] 侯怡如, 于韬, 叶冬熳, 等. 乳腺密度的多模态影像学评估及其在乳腺癌风险预测中的应用[J]. 中国临床医学影像杂志, 2026, 37(3): 209-213.
[14] Kim, S.J., Jung, H.K., Han, B., Lee, J.H., Oh, K.S., Lee, J.S., et al. (2020) Relationship between Breast Ultrasound Background Echotexture and Magnetic Resonance Imaging Background Parenchymal Enhancement and the Effect of Hormonal Status Thereon. Ultrasound Quarterly, 36, 179-191.
https://doi.org/10.1097/ruq.0000000000000487
[15] Brooks, J.D., Christensen, R.A.G., Sung, J.S., Pike, M.C., Orlow, I., Bernstein, J.L., et al. (2022) MRI Background Parenchymal Enhancement, Breast Density and Breast Cancer Risk Factors: A Cross-Sectional Study in Pre-and Post-Menopausal Women. NPJ Breast Cancer, 8, Article No. 97.
https://doi.org/10.1038/s41523-022-00458-2
[16] Acciavatti, R.J., Lee, S.H., Reig, B., Moy, L., Conant, E.F., Kontos, D., et al. (2023) Beyond Breast Density: Risk Measures for Breast Cancer in Multiple Imaging Modalities. Radiology, 306, e222175.
https://doi.org/10.1148/radiol.222575
[17] Zhang, Y., Ma, L., Hu, N., Chang, C., Gao, Y. and Chen, Y. (2025) Pregnancy Associated Breast Cancer: Correlation between Parity and Ultrasonic Characteristics of Tumors & Background Echotexture. Cancer Imaging, 25, Article No. 119.
https://doi.org/10.1186/s40644-025-00941-6
[18] Kim, H., Lee, H. and Choi, J.S. (2025) Nonmass Lesions on Breast Ultrasound: Radiologic-Pathologic Correlation and a Practical Guide to Diagnostic Approach. Korean Journal of Radiology, 26, 1133-1148.
https://doi.org/10.3348/kjr.2025.1125
[19] Park, A.Y., Seo, B.K., Han, H., Cho, K.R., Woo, O.H., Cha, S.H., et al. (2018) Clinical Value of Real-Time Ultrasonography-MRI Fusion Imaging for Second-Look Examination in Preoperative Breast Cancer Patients: Additional Lesion Detection and Treatment Planning. Clinical Breast Cancer, 18, 261-269.
https://doi.org/10.1016/j.clbc.2017.07.007
[20] Choi, W.J., Kim, S.H., Shin, H.J., Bang, M., Kang, B.J., Lee, S.H., et al. (2021) Automated Breast US as the Primary Screening Test for Breast Cancer among East Asian Women Aged 40-49 Years: A Multicenter Prospective Study. European Radiology, 31, 7771-7782.
https://doi.org/10.1007/s00330-021-07864-3
[21] Choi, J.S. (2023) Breast Imaging Reporting and Data System (BI-RADS): Advantages and Limitations. Journal of the Korean Society of Radiology, 84, 3-14.
https://doi.org/10.3348/jksr.2022.0142
[22] Hai, L., Feng, Y., Zhao, J., Tang, Q., Wang, X., Cao, X., et al. (2022) An Improved Nomogram to Reduce False-Positive Biopsy Rates of Breast Imaging Reporting and Data System Ultrasonography Category 4A Lesions. Cancer Control, 29.
https://doi.org/10.1177/10732748221122703
[23] Vatteroni, G., Dietzel, M. and Baltzer, P.A.T. (2025) Can Structured Integration of BI-RADS Criteria by a Clinical Decision Rule Reduce the Number of Unnecessary Biopsies in BI-RADS 4 Lesions? A Systematic Review and Meta-Analysis. European Radiology, 35, 1504-1513.
https://doi.org/10.1007/s00330-024-11274-6
[24] Zhong, Y., Chen, Y.T., Qiu, Y.D., Xiao, Y., Chen, X., Wang, L., et al. (2025) Sonographic Glandular Tissue Component: A Potential Imaging Marker for Upgrading BI-RADS 4A Breast Masses. Academic Radiology, 32, 3883-3891.
https://doi.org/10.1016/j.acra.2025.03.041
[25] Milanese, T.R., Hartmann, L.C., Sellers, T.A., Frost, M.H., Vierkant, R.A., Maloney, S.D., et al. (2006) Age-Related Lobular Involution and Risk of Breast Cancer. JNCI: Journal of the National Cancer Institute, 98, 1600-1607.
https://doi.org/10.1093/jnci/djj439
[26] Bao, Z., Zhao, Y., Chen, S., Chen, X., Xu, X., Wei, L., et al. (2021) Evidence and Assessment of Parenchymal Patterns of Ultrasonography for Breast Cancer Detection among Chinese Women: A Cross-Sectional Study. BMC Medical Imaging, 21, Article No. 152.
https://doi.org/10.1186/s12880-021-00687-0
[27] Kim, M.K., Kim, H., Baek, S., Ko, E.Y., Han, B., Ko, E.S., et al. (2026) Association of Breast Tissue Composition on Preoperative Automated Breast Ultrasound with Accuracy of Cancer Multiplicity Evaluation and Recurrence-Free Survival in Patients with Early-Stage Breast Cancer. Korean Journal of Radiology, 27, 97-110.
https://doi.org/10.3348/kjr.2025.1249
[28] Uematsu, T., Nakashima, K., Nasu, H., Igarashi, T., Okayama, Y. and Notsu, A. (2024) Preliminary Study of Standardized Semiquantitative Method for Ultrasonographic Breast Composition Assessment. Journal of Medical Ultrasonics, 51, 497-505.
https://doi.org/10.1007/s10396-024-01463-7
[29] 孙雨, 杨琛. 基于自动乳腺超声诊断系统的乳腺肿瘤人工智能诊断研究进展[J]. 中国医学影像学杂志, 2024, 32(11): 1176-1181.
[30] Choi, E.J., Choi, H., Byon, J.H. and Youk, J.H. (2023) Analysis of Background Echotexture on Automated Breast Ultrasound Using BI-RADS and Modified Classification: Association with Clinical Features and Mammographic Density. Journal of Clinical Ultrasound, 51, 687-695.
https://doi.org/10.1002/jcu.23426
[31] Huang, J.N., Yan, H.J., Qiu, Y.X., et al. (2026) ABUS-Based Glandular Tissue Component Classification for Breast Cancer Risk Prediction in Chinese Women with Dense Breasts: A Retrospective Study. BMC Medical Imaging, 26, Article No. 106.
https://doi.org/10.1186/s12880-026-02190-w
[32] Hueting, T.A., van Maaren, M.C., Hendriks, M.P., Koffijberg, H. and Siesling, S. (2023) External Validation of 87 Clinical Prediction Models Supporting Clinical Decisions for Breast Cancer Patients. The Breast, 69, 382-391.
https://doi.org/10.1016/j.breast.2023.04.003
[33] Eriksson, M., Czene, K., Vachon, C., Conant, E.F. and Hall, P. (2023) A Clinical Risk Model for Personalized Screening and Prevention of Breast Cancer. Cancers, 15, Article No. 3246.
https://doi.org/10.3390/cancers15123246

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