自动全乳腺超声在乳腺癌中的应用进展
Application Progress of Automated Breast Ultrasound in Breast Cancer
DOI: 10.12677/acm.2025.1592562, PDF,    科研立项经费支持
作者: 张靖茹, 王胜利, 贾红娥*:延安大学附属医院超声医学科,陕西 延安
关键词: 自动全乳腺超声乳腺癌Automated Breast Ultrasound Breast Cancer
摘要: 自动全乳腺超声是一种新型高分辨率乳腺三维超声成像技术,可以克服常规手持超声对操作者依赖性大、缺乏标准化操作、可重复性差等缺点,其特有的冠状面视角可以提供额外的影像资料,在鉴别诊断乳腺良恶性病变方面表现出较高的价值。本文将对自动全乳腺超声辅助乳腺癌诊断的应用现状进行综述。
Abstract: Automated breast ultrasound is a new high-resolution breast three-dimensional ultrasound imaging technology that can overcome the disadvantages of conventional hand-held ultrasound, such as high operator dependence, lack of standardized operation, and poor repeatability. Its unique coronal view angle can provide additional imaging data, showing high value in the differential diagnosis of benign and malignant breast lesions. This article will review the application status of automated breast ultrasound assisted diagnosis of breast cancer.
文章引用:张靖茹, 王胜利, 贾红娥. 自动全乳腺超声在乳腺癌中的应用进展[J]. 临床医学进展, 2025, 15(9): 825-832. https://doi.org/10.12677/acm.2025.1592562

参考文献

[1] Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A., et al. (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians, 71, 209-249. [Google Scholar] [CrossRef] [PubMed]
[2] Zeng, H., Zheng, R., Zhang, S., Zou, X. and Chen, W. (2014) Female Breast Cancer Statistics of 2010 in China: Estimates Based on Data from 145 Population-Based Cancer Registries. Journal of Thoracic Disease, 6, 466-470.
[3] Tagliafico, A.S., Calabrese, M., Mariscotti, G., et al. (2016) Giovanna, M., Manuela, D., Simona, T., et al. (2016) Adjunct Screening with Tomosynthesis or Ultrasound in Women With Mammography-Negative Dense Breasts: Interim Report of a Prospective Comparative Trial. Journal of Clinical Oncology, 34, 1882-1888.
[4] van Zelst, J.C.M. and Mann, R.M. (2018) Automated Three-Dimensional Breast US for Screening: Technique, Artifacts, and Lesion Characterization. RadioGraphics, 38, 663-683. [Google Scholar] [CrossRef] [PubMed]
[5] Jackson, V.P., Kelly-Fry, E., Rothschild, P.A., Holden, R.W. and Clark, S.A. (1986) Automated Breast Sonography Using a 7.5-Mhz PVDF Transducer: Preliminary Clinical Evaluation. Work in Progress. Radiology, 159, 679-684. [Google Scholar] [CrossRef] [PubMed]
[6] Shin, H.J., Kim, H.H. and Cha, J.H. (2015) Current Status of Automated Breast Ultrasonography. Ultrasonography, 34, 165-172. [Google Scholar] [CrossRef] [PubMed]
[7] Wang, H., Jiang, Y., Zhu, Q., Zhang, J., Dai, Q., Liu, H., et al. (2012) Differentiation of Benign and Malignant Breast Lesions: A Comparison between Automatically Generated Breast Volume Scans and Handheld Ultrasound Examinations. European Journal of Radiology, 81, 3190-3200. [Google Scholar] [CrossRef] [PubMed]
[8] Lin, X., Jia, M., Zhou, X., Bao, L., Chen, Y., Liu, P., et al. (2020) The Diagnostic Performance of Automated versus Handheld Breast Ultrasound and Mammography in Symptomatic Outpatient Women: A Multicenter, Cross-Sectional Study in China. European Radiology, 31, 947-957. [Google Scholar] [CrossRef] [PubMed]
[9] Zhang, L., Bao, L., Tan, Y., Zhu, L., Xu, X., Zhu, Q., et al. (2019) Diagnostic Performance Using Automated Breast Ultrasound System for Breast Cancer in Chinese Women Aged 40 Years or Older: A Comparative Study. Ultrasound in Medicine & Biology, 45, 3137-3144. [Google Scholar] [CrossRef] [PubMed]
[10] Zhang, X., Chen, J., Zhou, Y., Mao, F., Lin, Y., Shen, S., et al. (2019) Diagnostic Value of an Automated Breast Volume Scanner Compared with a Hand-Held Ultrasound: A Meta-Analysis. Gland Surgery, 8, 698-711. [Google Scholar] [CrossRef] [PubMed]
[11] Liu, J., Zhou, Y., Wu, J., Li, P., Liang, X., Duan, H., et al. (2021) Diagnostic Performance of Combined Use of Automated Breast Volume Scanning & Hand-Held Ultrasound for Breast Lesions. Indian Journal of Medical Research, 154, 347-354. [Google Scholar] [CrossRef] [PubMed]
[12] Choi, E.J., Choi, H., Park, E.H., Song, J.S. and Youk, J.H. (2018) Evaluation of an Automated Breast Volume Scanner According to the Fifth Edition of BI-RADS for Breast Ultrasound Compared with Hand-Held Ultrasound. European Journal of Radiology, 99, 138-145. [Google Scholar] [CrossRef] [PubMed]
[13] Schmachtenberg, C., Fischer, T., Hamm, B. and Bick, U. (2017) Diagnostic Performance of Automated Breast Volume Scanning (ABVS) Compared to Handheld Ultrasonography with Breast MRI as the Gold Standard. Academic Radiology, 24, 954-961. [Google Scholar] [CrossRef] [PubMed]
[14] Meng, Z., Chen, C., Zhu, Y., Zhang, S., Wei, C., Hu, B., et al. (2015) Diagnostic Performance of the Automated Breast Volume Scanner: A Systematic Review of Inter-Rater Reliability/Agreement and Meta-Analysis of Diagnostic Accuracy for Differentiating Benign and Malignant Breast Lesions. European Radiology, 25, 3638-3647. [Google Scholar] [CrossRef] [PubMed]
[15] Wang, L. and Qi, Z. (2019) Automatic Breast Volume Scanner versus Handheld Ultrasound in Differentiation of Benign and Malignant Breast Lesions: A Systematic Review and Meta-Analysis. Ultrasound in Medicine & Biology, 45, 1874-1881. [Google Scholar] [CrossRef] [PubMed]
[16] Zhang, J., Cai, L., Chen, L., Pang, X., Chen, M., Yan, D., et al. (2021) Re-Evaluation of High-Risk Breast Mammography Lesions by Target Ultrasound and ABUS of Breast Non-Mass-Like Lesions. BMC Medical Imaging, 21, Article No. 156. [Google Scholar] [CrossRef] [PubMed]
[17] Xiang, H., Huang, Y.S., Lee, C.H., et al. (2021) 3-D Res-CapsNet Convolutional Neural Network on Automated Breast Ultrasound Tumor Diagnosis. European Journal of Radiology, 138, Article 109608. [Google Scholar] [CrossRef] [PubMed]
[18] 宋灿许, 马菁菁, 李逢生, 袁权, 李林, 刘瑞. 自动乳腺容积成像与手持超声对触诊阴性乳腺的诊断价值比较[J]. 中国医学影像学杂志, 2021, 29(1): 39-41.
[19] Mostafa, A.A.E., Eltomey, M.A., Elaggan, A.M. and Hashish, A.A. (2019) Automated Breast Ultrasound (ABUS) as a Screening Tool: Initial Experience. Egyptian Journal of Radiology and Nuclear Medicine, 50, Article No. 37. [Google Scholar] [CrossRef
[20] Li, X., Lu, F., Zhu, A., Du, D., Zhang, Y., Guo, L., et al. (2020) Multimodal Ultrasound Imaging in Breast Imaging-Reporting and Data System 4 Breast Lesions: A Prediction Model for Malignancy. Ultrasound in Medicine & Biology, 46, 3188-3199. [Google Scholar] [CrossRef] [PubMed]
[21] Li, W., Zheng, Y., Liu, H., Tai, Z., Zhu, H., Li, Z., et al. (2024) Multimodal Ultrasound Imaging for Diagnostic Differentiation of Sclerosing Adenosis from Invasive Ductal Carcinoma. Quantitative Imaging in Medicine and Surgery, 14, 877-887. [Google Scholar] [CrossRef] [PubMed]
[22] Wang, J., Fan, H., Zhu, Y., Shen, C. and Qiang, B. (2021) The Value of Automated Breast Volume Scanner Combined with Virtual Touch Tissue Quantification in the Differential Diagnosis of Benign and Malignant Breast Lesions. Medicine, 100, e25568. [Google Scholar] [CrossRef] [PubMed]
[23] Jia, M., Lin, X., Zhou, X., Yan, H., Chen, Y., Liu, P., et al. (2020) Diagnostic Performance of Automated Breast Ultrasound and Handheld Ultrasound in Women with Dense Breasts. Breast Cancer Research and Treatment, 181, 589-597. [Google Scholar] [CrossRef] [PubMed]
[24] Ren, W., Yan, H., Zhao, X., Jia, M., Zhang, S., Zhang, J., et al. (2023) Integration of Handheld Ultrasound or Automated Breast Ultrasound among Women with Negative Mammographic Screening Findings: A Multi-Center Population-Based Study in China. Academic Radiology, 30, S114-S126. [Google Scholar] [CrossRef] [PubMed]
[25] Brem, R.F., Tabár, L., Duffy, S.W., Inciardi, M.F., Guingrich, J.A., Hashimoto, B.E., et al. (2015) Assessing Improvement in Detection of Breast Cancer with Three-Dimensional Automated Breast US in Women with Dense Breast Tissue: The Somoinsight Study. Radiology, 274, 663-673. [Google Scholar] [CrossRef] [PubMed]
[26] Gatta, G., Cappabianca, S., La Forgia, D., Massafra, R., Fanizzi, A., Cuccurullo, V., et al. (2021) Second-Generation 3D Automated Breast Ultrasonography (prone ABUS) for Dense Breast Cancer Screening Integrated to Mammography: Effectiveness, Performance and Detection Rates. Journal of Personalized Medicine, 11, Article 875. [Google Scholar] [CrossRef] [PubMed]
[27] Zhang, X., Lin, X., Tan, Y., Zhu, Y., Wang, H., Feng, R., et al. (2018) A Multicenter Hospital-Based Diagnosis Study of Automated Breast Ultrasound System in Detecting Breast Cancer among Chinese Women. Chinese Journal of Cancer Research, 30, 231-239. [Google Scholar] [CrossRef] [PubMed]
[28] Lin, X., Wang, J., Han, F., Fu, J. and Li, A. (2012) Analysis of Eighty-One Cases with Breast Lesions Using Automated Breast Volume Scanner and Comparison with Handheld Ultrasound. European Journal of Radiology, 81, 873-878. [Google Scholar] [CrossRef] [PubMed]
[29] Sherchan, A., Liang, J.T., Sherchan, B., Suwal, S. and Katwal, S. (2024) Comparative Analysis of Automated Breast Volume Scanner (ABVS) Combined with Conventional Hand-Held Ultrasound and Mammography in Female Breast Cancer Detection. Annals of Medicine & Surgery, 86, 159-165. [Google Scholar] [CrossRef] [PubMed]
[30] Zheng, F.Y., Yan, L.X., Huang, B.J., et al. (2015) Comparison of Retraction Phenomenon and BI-RADS-US Descriptors in Differentiating Benign and Malignant Breast Masses Using an Automated Breast Volume Scanner. European Journal of Radiology, 84, 2123-2129. [Google Scholar] [CrossRef] [PubMed]
[31] Füsun, T., Kutsi, K., Alparslan, U., et al. (2011) Sclerosing Adenosis of the Breast: Radiologic Appearance and Efficiency of Core Needle Biopsy. Diagnostic and Interventional Radiology (Ankara, Turkey), 17, 311-316.
[32] 自动乳腺容积超声技术专家共识(2022版) [J]. 中国超声医学杂志, 2022, 38(3): 241-247.
[33] Nakhlis, F., Lester, S., Denison, C., Wong, S.M., Mongiu, A. and Golshan, M. (2017) Complex Sclerosing Lesions and Radial Sclerosing Lesions on Core Needle Biopsy: Low Risk of Carcinoma on Excision in Cases with Clinical and Imaging Concordance. The Breast Journal, 24, 133-138. [Google Scholar] [CrossRef] [PubMed]
[34] Tang, G., An, X., Xiang, H., Liu, L., Li, A. and Lin, X. (2020) Automated Breast Ultrasound: Interobserver Agreement, Diagnostic Value, and Associated Clinical Factors of Coronal-Plane Image Features. Korean Journal of Radiology, 21, 550-560. [Google Scholar] [CrossRef] [PubMed]
[35] Pellegrino, B., Hlavata, Z., Migali, C., De Silva, P., Aiello, M., Willard-Gallo, K., et al. (2021) Luminal Breast Cancer: Risk of Recurrence and Tumor-Associated Immune Suppression. Molecular Diagnosis & Therapy, 25, 409-424. [Google Scholar] [CrossRef] [PubMed]
[36] 王海彬, 魏秋良, 崔振华. 不同分子亚型乳腺癌患者动态增强MRI的定量参数变化[J]. 临床与病理杂志, 2018, 38(11): 2453-2460.
[37] 周汇明, 肖际东, 刘梦涵, 聂淼淼, 戴美雪. 基于乳腺二维超声及自动乳腺容积扫描构建影像组学及列线图模型预测乳腺癌分子分型[J]. 中国医学影像技术, 2024, 40(1): 55-61.
[38] 黄思, 肖耀成, 李建, 左文思. 乳腺自动容积成像对乳腺癌分子分型的预判分析[J]. 医学研究杂志, 2022, 51(11): 106-109.
[39] Xu, X., Lu, L., Zhu, L., Tan, Y., Yu, L. and Bao, L. (2022) Predicting the Molecular Subtypes of Breast Cancer Using Nomograms Based on Three-Dimensional Ultrasonography Characteristics. Frontiers in Oncology, 12, Article 838787. [Google Scholar] [CrossRef] [PubMed]
[40] 范莉芳, 张超学, 黄磊, 吴艺敏, 吴树剑, 朱向明. 病理参数联合ABVS影像特征列线图预测乳腺癌Luminal分型[J]. 中国超声医学杂志, 2024, 40(2): 153-157.
[41] Chen, W., Ru, R., Wang, F. and Li, M. (2021) Automated Breast Volume Scanning Combined with Shear Wave Elastography for Diagnosis of Triple-Negative Breast Cancer and Human Epidermal Growth Factor Receptor 2-Positive Breast Cancer. Revista da Associação Médica Brasileira, 67, 1167-1171. [Google Scholar] [CrossRef] [PubMed]
[42] Giuliano, V. and Giuliano, C. (2013) Improved Breast Cancer Detection in Asymptomatic Women Using 3d-Automated Breast Ultrasound in Mammographically Dense Breasts. Clinical Imaging, 37, 480-486. [Google Scholar] [CrossRef] [PubMed]
[43] Liao, H., Zhang, W., Sun, J., Li, F., He, Z. and Wu, S. (2018) The Clinicopathological Features and Survival Outcomes of Different Histological Subtypes in Triple-Negative Breast Cancer. Journal of Cancer, 9, 296-303. [Google Scholar] [CrossRef] [PubMed]
[44] van Zelst, J.C.M., Balkenhol, M., Tan, T., Rutten, M., Imhof-Tas, M., Bult, P., et al. (2017) Sonographic Phenotypes of Molecular Subtypes of Invasive Ductal Cancer in Automated 3-D Breast Ultrasound. Ultrasound in Medicine & Biology, 43, 1820-1828. [Google Scholar] [CrossRef] [PubMed]
[45] Huang, Y., Liu, Y., Wang, Y., Zheng, X., Han, J., Li, Q., et al. (2021) Quantitative Analysis of Shear Wave Elastic Heterogeneity for Prediction of Lymphovascular Invasion in Breast Cancer. The British Journal of Radiology, 94, Article 20210682. [Google Scholar] [CrossRef] [PubMed]
[46] Zhou, P., Jin, C., Lu, J., Xu, L., Zhu, X., Lian, Q., et al. (2020) The Value of Nomograms in Pre-Operative Prediction of Lymphovascular Invasion in Primary Breast Cancer Undergoing Modified Radical Surgery: Based on Multiparametric Ultrasound and Clinicopathologic Indicators. Ultrasound in Medicine & Biology, 47, 517-526. [Google Scholar] [CrossRef] [PubMed]
[47] Li, J., Ma, W., Jiang, X., Cui, C., Wang, H., Chen, J., et al. (2019) Development and Validation of Nomograms Predictive of Axillary Nodal Status to Guide Surgical Decision-Making in Early-Stage Breast Cancer. Journal of Cancer, 10, 1263-1274. [Google Scholar] [CrossRef] [PubMed]
[48] Li, Y., Wu, X., Yan, Y. and Zhou, P. (2023) Automated Breast Volume Scanner Based Radiomics for Non-Invasively Prediction of Lymphovascular Invasion Status in Breast Cancer. BMC Cancer, 23, Article No. 813. [Google Scholar] [CrossRef] [PubMed]
[49] 范莉芳, 黄磊, 赵劲松, 吴艺敏, 徐争元, 徐晓燕, 傅雨晨. 基于ABVS影像组学联合VTQ术前预测浸润性乳腺癌淋巴血管侵犯[J]. 放射学实践, 2023, 38(3): 342-348.
[50] 王美晨, 史丽群, 李照喜. 基于ABVS联合VTIQ技术构建乳腺癌腋窝淋巴结高转移负荷预测模型的研究[J]. 中国超声医学杂志, 2022, 38(12): 1354-1357.
[51] Yang, J., Yang, Q., Mukherjee, A. and Lv, Q. (2021) Distance between the Tumour and Nipple as a Predictor of Axillary Lymph Node Involvement in Breast Cancer. Cancer Management and Research, 13, 193-199. [Google Scholar] [CrossRef] [PubMed]
[52] Li, J.M., Shao, Y.H., Sun, X.M. and Shi, J. (2024) Ultrasonic Features of Automated Breast Volume Scanner (ABVS) and Handheld Ultrasound (HHUS) Combined with Molecular Biomarkers in Predicting Axillary Lymph Node Metastasis of Clinical T1-T2 Breast Cancer. Quantitative Imaging in Medicine and Surgery, 14, 1359-1368. [Google Scholar] [CrossRef] [PubMed]
[53] Lewis, E.I., Ozonoff, A., Nguyen, C.P., Kim, M. and Slanetz, P.J. (2011) Breast Cancer Close to the Nipple: Does This Increase the Risk of Nodal Metastasis at Diagnosis? Canadian Association of Radiologists Journal, 62, 209-214. [Google Scholar] [CrossRef] [PubMed]
[54] Zhu, A., Li, X., An, L., Guo, L., Fu, H., Sun, L., et al. (2020) Predicting Axillary Lymph Node Metastasis in Patients with Breast Invasive Ductal Carcinoma with Negative Axillary Ultrasound Results Using Conventional Ultrasound and contrast-Enhanced Ultrasound. Journal of Ultrasound in Medicine, 39, 2059-2070. [Google Scholar] [CrossRef] [PubMed]
[55] Montemurro, F., Nuzzolese, I. and Ponzone, R. (2020) Neoadjuvant or Adjuvant Chemotherapy in Early Breast Cancer? Expert Opinion on Pharmacotherapy, 21, 1071-1082. [Google Scholar] [CrossRef] [PubMed]
[56] Slanetz, P.J., Moy, L., Baron, P., diFlorio, R.M., Green, E.D., Heller, S.L., et al. (2017) ACR Appropriateness Criteria Monitoring Response to Neoadjuvant Systemic Therapy for Breast Cancer. Journal of the American College of Radiology, 14, S462-S475. [Google Scholar] [CrossRef] [PubMed]
[57] D’Angelo, A., Orlandi, A., Bufi, E., Mercogliano, S., Belli, P. and Manfredi, R. (2021) Automated Breast Volume Scanner (ABVS) Compared to Handheld Ultrasound (HHUS) and Contrast-Enhanced Magnetic Resonance Imaging (CE-MRI) in the Early Assessment of Breast Cancer during Neoadjuvant Chemotherapy: An Emerging Role to Monitoring Tumor Response? La radiologia medica, 126, 517-526. [Google Scholar] [CrossRef] [PubMed]
[58] D’Angelo, A., Rinaldi, P., Belli, P., et al. (2019) Usefulness of Automated Breast Volume Scanner (ABVS) for Monitoring Tumor Response to Neoadjuvant Treatment in Breast Cancer Patients: Preliminary Results. European Review for Medical and Pharmacological Sciences, 23, 225-231.
[59] Hatzipanagiotou, M.E., Huber, D., Gerthofer, V., Hetterich, M., Ripoll, B.R., Ortmann, O., et al. (2022) Feasibility of ABUS as an Alternative to Handheld Ultrasound for Response Control in Neoadjuvant Breast Cancer Treatment. Clinical Breast Cancer, 22, e142-e146. [Google Scholar] [CrossRef] [PubMed]
[60] Jiang, W., Deng, X., Zhu, T., Fang, J. and Li, J. (2023) ABVS-Based Radiomics for Early Predicting the Efficacy of Neoadjuvant Chemotherapy in Patients with Breast Cancers. Breast Cancer: Targets and Therapy, 15, 625-636. [Google Scholar] [CrossRef] [PubMed]
[61] McAnena, P., Moloney, B.M., Browne, R., O’Halloran, N., Walsh, L., Walsh, S., et al. (2022) A Radiomic Model to Classify Response to Neoadjuvant Chemotherapy in Breast Cancer. BMC Medical Imaging, 22, Article No. 225. [Google Scholar] [CrossRef] [PubMed]
[62] Li, Y., Fan, Y., Xu, D., Li, Y., Zhong, Z., Pan, H., et al. (2023) Deep Learning Radiomic Analysis of DCE-MRI Combined with Clinical Characteristics Predicts Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer. Frontiers in Oncology, 12, Article 1041142. [Google Scholar] [CrossRef] [PubMed]
[63] Wang, X., Huo, L., He, Y., Fan, Z., Wang, T., Xie, Y., et al. (2016) Early Prediction of Pathological Outcomes to Neoadjuvant Chemotherapy in Breast Cancer Patients Using Automated Breast Ultrasound. Chinese Journal of Cancer Research, 28, 478-485. [Google Scholar] [CrossRef] [PubMed]
[64] Xie, Y., Chen, Y., Wang, Q., Li, B., Shang, H. and Jing, H. (2023) Early Prediction of Response to Neoadjuvant Chemotherapy Using Quantitative Parameters on Automated Breast Ultrasound Combined with Contrast-Enhanced Ultrasound in Breast Cancer. Ultrasound in Medicine & Biology, 49, 1638-1646. [Google Scholar] [CrossRef] [PubMed]
[65] Murakami, R., Tani, H., Kumita, S. and Uchiyama, N. (2021) Diagnostic Performance of Digital Breast Tomosynthesis for Predicting Response to Neoadjuvant Systemic Therapy in Breast Cancer Patients: A Comparison with Magnetic Resonance Imaging, Ultrasound, and Full-Field Digital Mammography. Acta Radiologica Open, 10, 1-8. [Google Scholar] [CrossRef] [PubMed]
[66] van Egdom, L.S.E., Lagendijk, M., Heijkoop, E.H.M., Koning, A.H.J., van Deurzen, C.H.M., Jager, A., et al. (2018) Three-Dimensional Ultrasonography of the Breast; an Adequate Replacement for MRI in Neoadjuvant Chemotherapy Tumour Response Evaluation—Responder Trial. European Journal of Radiology, 104, 94-100. [Google Scholar] [CrossRef] [PubMed]
[67] Lim, H.F., Sharma, A., Gallagher, C. and Hall, P. (2023) Value of Ultrasound in Assessing Response to Neoadjuvant Chemotherapy in Breast Cancer. Clinical Radiology, 78, 912-918. [Google Scholar] [CrossRef] [PubMed]
[68] Tutar, B., Esen Icten, G., Guldogan, N., Kara, H., Arıkan, A.E., Tutar, O., et al. (2020) Comparison of Automated versus Hand-Held Breast US in Supplemental Screening in Asymptomatic Women with Dense Breasts: Is There a Difference Regarding Woman Preference, Lesion Detection and Lesion Characterization? Archives of Gynecology and Obstetrics, 301, 1257-1265. [Google Scholar] [CrossRef] [PubMed]