乳腺癌早期筛查专家共识：现状、争议与未来方向
Expert Consensus Regarding Early Screening for Breast Cancer: Current Landscape, Controversies, and Future Perspectives
DOI: 10.12677/acm.2026.1672606, PDF,    科研立项经费支持
作者: 张志颖, 张 瑞：内蒙古医科大学附属医院甲乳外科，内蒙古 呼和浩特
关键词: 乳腺癌；早期筛查；专家共识；乳腺X线摄影；高风险人群；过度诊断；人工智能；Breast Cancer； Early Screening； Expert Consensus； Mammography； High-Risk Population； Overdiagnosis； Artificial Intelligence
摘要: 乳腺癌依然是威胁全球女性健康的重大公共卫生问题，早期筛查在降低其死亡率方面具有核心战略意义。本综述系统综述了当前国内外关于乳腺癌早期筛查的最新专家共识，旨在系统阐述该领域的研究现状、主要争议焦点及未来发展方向。文章首先概述乳腺癌的流行病学负担及其筛查的重要性；随后详细评述乳腺X线摄影、超声及磁共振成像等主要筛查方法在适宜人群、诊断效能及循证依据方面的表现，并深入探讨有关筛查起始年龄、筛查间隔周期及高风险人群界定及管理策略方面的共识与分歧。此外，本文进一步剖析了筛查实践中面临的过度诊断、假阳性率及卫生经济学评价等关键挑战。最后，综述展望了整合人工智能影像分析及液体活检等新兴技术的个体化精准筛查模式的发展潜力。通过整合多学科专家的观点与证据，本文旨在为优化临床筛查实践及制定更为有效的公共卫生政策提供参考依据。
Abstract: Breast cancer remains a major public health issue threatening women’s health globally, and early detection plays a critical role in reducing its mortality. This review systematically summarizes the latest expert consensus on early detection of breast cancer both domestically and internationally, aiming to provide a comprehensive overview of the current research status, major points of controversy, and future directions in this field. This review first outlines the epidemiological burden of breast cancer and the importance of screening; it then provides a detailed assessment of the performance of major screening methods, including mammography, ultrasound, and MRI, in terms of target populations, diagnostic accuracy, and evidence-based foundations, while examining consensus and divergences regarding screening initiation age, screening intervals, and the definition and management strategies for high-risk populations. Furthermore, this review analyzes key challenges encountered in screening practice, such as overdiagnosis, high false-positive rates, and complexities in health economic evaluations. Finally, the review explores the potential of individualized precision screening models integrating emerging technologies such as artificial intelligence-based image analysis and liquid biopsy. By synthesizing perspectives and evidence from multidisciplinary experts, this article aims to provide a reference for optimizing clinical screening practices and formulating more effective public health policies.
文章引用：张志颖, 张瑞. 乳腺癌早期筛查专家共识：现状、争议与未来方向[J]. 临床医学进展, 2026, 16(7): 961-974. https://doi.org/10.12677/acm.2026.1672606

参考文献

[1] 陈娟, 李敏, 陈振东. 乳腺癌筛查现状[J]. 中华健康管理学杂志, 2009, 3(2): 107-109.
[2] 中国抗癌协会, 国家肿瘤临床医学研究中心(天津医科大学肿瘤医院). 中国女性乳腺癌筛查指南[J]. 中国肿瘤临床, 2019, 46(9): 430-432.
[3] 张林. 乳腺癌筛查和预防[J]. 中国医师进修杂志, 2016, 39(6): 484-486.
[4] 本刊编辑部. IARC乳腺癌筛查指南更新[J]. 中国肿瘤临床, 2015(14): 723.
[5] Al-Balas, M., Al-Balas, H., AlAmer, Z., Al-Taweel, G., Ghabboun, A., Al Bzoor, F., et al. (2024) Clinical Outcomes of Screening and Diagnostic Mammography in a Limited Resource Healthcare System. BMC Women’s Health, 24, Article No. 191. [Google Scholar] [CrossRef] [PubMed]
[6] Daniaux, M., Gruber, L., Santner, W., Czech, T. and Knapp, R. (2021) Interval Breast Cancer: Analysis of Occurrence, Subtypes and Implications for Breast Cancer Screening in a Model Region. European Journal of Radiology, 143, Article ID: 109905. [Google Scholar] [CrossRef] [PubMed]
[7] Heggland, T., Vatten, L.J., Opdahl, S. and Weedon-Fekjær, H. (2023) Non-Progressive Breast Carcinomas Detected at Mammography Screening: A Population Study. Breast Cancer Research, 25, Article No. 80. [Google Scholar] [CrossRef] [PubMed]
[8] 孙魁. 探讨乳腺癌筛查研究[J]. 滨州医学院学报, 2013(6): 466-468.
[9] 莫淼, 柳光宇, 吕力琅, 等. 乳腺癌筛查研究进展[J]. 肿瘤, 2012, 32(9): 748-754.
[10] 李卫芹, 李蓉, 刘佩芳, 等. 中国乳腺癌筛查模式探讨[J]. 中华流行病学杂志, 2016, 37(7): 1039-1043.
[11] 张立宁. 乳腺癌筛查现状浅述[J]. 求医问药(学术版), 2011, 9(12): 260-261.
[12] 商木岩, 郭帅. 中国乳腺癌筛查现状[J]. 实用癌症杂志, 2020, 35(11): 1911-1914.
[13] 封任冬, 汪华, 丁莹莹, 等. 乳腺癌筛查模式的探讨[J]. 临床放射学杂志, 2016, 35(1): 36-40.
[14] 张雅聪, 吕章艳, 宋方方, 等. 全球及我国乳腺癌发病和死亡变化趋势[J]. 肿瘤综合治疗电子杂志, 2021, 7(2): 14-20.
[15] 梁锌, 杨剑, 高婷, 等. 全球女性乳腺癌发病趋势及年龄变化情况分析[J]. 中华肿瘤杂志, 2023, 45(4): 313-321.
[16] Sun, K., Zhang, B., Lei, S., Zheng, R., Liang, X., Li, L., et al. (2024) Incidence, Mortality, and Disability-Adjusted Life Years of Female Breast Cancer in China, 2022. Chinese Medical Journal, 137, 2429-2436. [Google Scholar] [CrossRef] [PubMed]
[17] Hong, A., Chua, J., Cheng, M. and Law, M. (2020) Breast Cancer Subtypes in Australian Chinese Women. ANZ Journal of Surgery, 90, 2516-2520. [Google Scholar] [CrossRef] [PubMed]
[18] 李霓, 郑荣寿, 张思维, 等. 中国城乡女性乳腺癌发病趋势分析和预测[J]. 中华预防医学杂志, 2012, 46(8): 703-707.
[19] Xu, B., Hu, X., Feng, J., Geng, C., Jin, F., Li, H., et al. (2020) Chinese Expert Consensus on the Clinical Diagnosis and Treatment of Advanced Breast Cancer (2018). Cancer, 126, 3867-3882. [Google Scholar] [CrossRef] [PubMed]
[20] Nanayakkara, D., Batuwitage, C., Chulasiri, P., Abeywardhana, D. and Samaraweera, S. (2022) Association between Mammographic Breast Density and Breast Carcinoma—A Unit Based Experience. Ceylon Medical Journal, 67, 108-112. [Google Scholar] [CrossRef] [PubMed]
[21] Ji, G., Bao, L., Yao, Q., Zhang, J., Zhu, X., Bai, Q., et al. (2021) Germline and Tumor BRCA1/2 Pathogenic Variants in Chinese Triple-Negative Breast Carcinomas. Journal of Cancer Research and Clinical Oncology, 147, 2935-2944. [Google Scholar] [CrossRef] [PubMed]
[22] 李洁. 乳腺癌早期诊断——乳腺X线摄影[J]. 癌症康复, 2008(3): 16-20.
[23] 张保宁. 乳腺癌的普查与乳腺X线摄影[J]. 中国中西医结合杂志, 2005, 25(10): 941-942.
[24] 赵丽娟, 徐卫云, 张晓红, 等. 乳腺密度对乳腺X线摄影诊断乳腺癌的影响[J]. 四川医学, 2013, 34(11): 1670-1672.
[25] Alves, M.S., Ferro, A.G.L., Moreira, M.C.L., Santos, W.S., Neves, L.P., Perini, A.P., et al. (2021) Estimated Risk of Radiation-Induced Cancer Following Breast Screening Employing Tomosynthesis and Digital Mammography. Journal of Radiological Protection, 41, 254-265. [Google Scholar] [CrossRef] [PubMed]
[26] Yuan, Y., Vu, K., Shen, Y., Dickinson, J. and Winget, M. (2020) Importance of Quality in Breast Cancer Screening Practice—A Natural Experiment in Alberta, Canada. BMJ Open, 10, e028766. [Google Scholar] [CrossRef] [PubMed]
[27] Alabousi, M., Zha, N., Salameh, J., Samoilov, L., Sharifabadi, A.D., Pozdnyakov, A., et al. (2020) Digital Breast Tomosynthesis for Breast Cancer Detection: A Diagnostic Test Accuracy Systematic Review and Meta-Analysis. European Radiology, 30, 2058-2071. [Google Scholar] [CrossRef] [PubMed]
[28] Ekici, S. and Jawzal, H. (2020) Breast Cancer Diagnosis Using Thermography and Convolutional Neural Networks. Medical Hypotheses, 137, Article ID: 109542. [Google Scholar] [CrossRef] [PubMed]
[29] Bicchierai, G., Amato, F., Vanzi, B., De Benedetto, D., Boeri, C., Vanzi, E., et al. (2020) Which Clinical, Radiological, Histological, and Molecular Parameters Are Associated with the Absence of Enhancement of Known Breast Cancers with Contrast Enhanced Digital Mammography (CEDM)? The Breast, 54, 15-24. [Google Scholar] [CrossRef] [PubMed]
[30] Laws, A. and Mulvey, T.M. (2021) Implementation of a High-Risk Breast Clinic for Comprehensive Care of Women with Elevated Breast Cancer Risk Identified by Risk Assessment Models in the Community. JCO Oncology Practice, 17, e217-e225. [Google Scholar] [CrossRef] [PubMed]
[31] Cohen, E.O., Lesslie, M., Weaver, O., Phalak, K., Tso, H., Perry, R., et al. (2021) Batch Reading and Interrupted Interpretation of Digital Screening Mammograms without and with Tomosynthesis. Journal of the American College of Radiology, 18, 280-293. [Google Scholar] [CrossRef] [PubMed]
[32] Sechopoulos, I. and Mann, R.M. (2020) Stand-alone Artificial Intelligence—The Future of Breast Cancer Screening? The Breast, 49, 254-260. [Google Scholar] [CrossRef] [PubMed]
[33] Graewingholt, A. and Duffy, S. (2021) Retrospective Comparison between Single Reading Plus an Artificial Intelligence Algorithm and Two-View Digital Tomosynthesis with Double Reading in Breast Screening. Journal of Medical Screening, 28, 365-368. [Google Scholar] [CrossRef] [PubMed]
[34] Lång, K., Hofvind, S., Rodríguez-Ruiz, A. and Andersson, I. (2021) Can Artificial Intelligence Reduce the Interval Cancer Rate in Mammography Screening? European Radiology, 31, 5940-5947. [Google Scholar] [CrossRef] [PubMed]
[35] Díaz, O., Rodríguez-Ruíz, A. and Sechopoulos, I. (2024) Artificial Intelligence for Breast Cancer Detection: Technology, Challenges, and Prospects. European Journal of Radiology, 175, 111457. [Google Scholar] [CrossRef] [PubMed]
[36] Miles, R., Nicolae, A. and Ravi, A. (2021) Routine Breast Cancer Screening in Average-Risk Women Younger than 50 Years: Current Paradigms Based on National Guidelines. Oncology, 35, 320-323. [Google Scholar] [CrossRef] [PubMed]
[37] Barsouk, A., Saginala, K., Aluru, J.S., Rawla, P. and Barsouk, A. (2022) US Cancer Screening Recommendations: Developments and the Impact of Covid-19. Medical Sciences, 10, Article 16. [Google Scholar] [CrossRef] [PubMed]
[38] Shi, W., Rothman, A.J., Yzer, M.C. and Nagler, R.H. (2023) Effects of Exposure to Conflicting Information about Mammography on Cancer Information Overload, Perceived Scientists’ Credibility, and Perceived Journalists’ Credibility. Health Communication, 38, 2481-2490. [Google Scholar] [CrossRef] [PubMed]
[39] Chen, X., Hoffmeister, M. and Brenner, H. (2025) Deriving Risk-Adapted Starting Ages of Breast Cancer Screening According to Polygenic Risk Score. JNCI Cancer Spectrum, 9, pkaf056. [Google Scholar] [CrossRef] [PubMed]
[40] Park, H.L., Chang, J., Haridass, V., Wang, S.S., Ziogas, A. and Anton-Culver, H. (2021) Mammography Screening and Mortality by Risk Status in the California Teachers Study. BMC Cancer, 21, Article No. 1341. [Google Scholar] [CrossRef] [PubMed]
[41] 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. [Google Scholar] [CrossRef] [PubMed]
[42] Clift, A.K., Dodwell, D., Lord, S., Petrou, S., Brady, S.M., Collins, G.S., et al. (2022) The Current Status of Risk-Stratified Breast Screening. British Journal of Cancer, 126, 533-550. [Google Scholar] [CrossRef] [PubMed]
[43] Chavarri-Guerra, Y., Slavin, T.P., Longoria-Lozano, O. and Weitzel, J.N. (2020) Genetic Cancer Predisposition Syndromes among Older Adults. Journal of Geriatric Oncology, 11, 1054-1060. [Google Scholar] [CrossRef] [PubMed]
[44] Durham, D.D., Roberts, M.C., Khan, C.P., Abraham, L.A., Smith, R.A., Kerlikowske, K., et al. (2021) Age at Initiation of Screening Mammography by Family History of Breast Cancer in the Breast Cancer Surveillance Consortium. Cancer Causes & Control, 32, 103-107. [Google Scholar] [CrossRef] [PubMed]
[45] Mukama, T., Kharazmi, E., Sundquist, K., Sundquist, J. and Fallah, M. (2021) Risk‐adapted Starting Age of Breast Cancer Screening in Women with a Family History of Ovarian or Other Cancers: A Nationwide Cohort Study. Cancer, 127, 2091-2098. [Google Scholar] [CrossRef] [PubMed]
[46] Cătană, A., Trifa, A.P., Achimas-Cadariu, P.A., Bolba-Morar, G., Lisencu, C., Kutasi, E., et al. (2023) Hereditary Breast Cancer in Romania—Molecular Particularities and Genetic Counseling Challenges in an Eastern European Country. Biomedicines, 11, Article 1386. [Google Scholar] [CrossRef] [PubMed]
[47] Boddicker, N.J., Hu, C., Weitzel, J.N., Kraft, P., Nathanson, K.L., Goldgar, D.E., et al. (2021) Risk of Late-Onset Breast Cancer in Genetically Predisposed Women. Journal of Clinical Oncology, 39, 3430-3440. [Google Scholar] [CrossRef] [PubMed]
[48] van den Broek, J.J., Schechter, C.B., van Ravesteyn, N.T., Janssens, A.C.J.W., Wolfson, M.C., Trentham-Dietz, A., et al. (2021) Personalizing Breast Cancer Screening Based on Polygenic Risk and Family History. JNCI: Journal of the National Cancer Institute, 113, 434-442. [Google Scholar] [CrossRef] [PubMed]
[49] Yeh, J.M., Lowry, K.P., Schechter, C.B., Diller, L.R., Alagoz, O., Armstrong, G.T., et al. (2020) Clinical Benefits, Harms, and Cost-Effectiveness of Breast Cancer Screening for Survivors of Childhood Cancer Treated with Chest Radiation: A Comparative Modeling Study. Annals of Internal Medicine, 173, 331-341. [Google Scholar] [CrossRef] [PubMed]
[50] Senda, N., Kawaguchi‐Sakita, N., Kawashima, M., Inagaki‐Kawata, Y., Yoshida, K., Takada, M., et al. (2021) Optimization of Prediction Methods for Risk Assessment of Pathogenic Germline Variants in the Japanese Population. Cancer Science, 112, 3338-3348. [Google Scholar] [CrossRef] [PubMed]
[51] Bolze, A., Cirulli, E.T., Hajek, C., Schnell Blitstein, J.M. and Grzymski, J.J. (2024) The Potential of Genetics in Identifying Women at Lower Risk of Breast Cancer. JAMA Oncology, 10, 236-239. [Google Scholar] [CrossRef] [PubMed]
[52] 国家统计局. 2024年《中国妇女发展纲要(2021-2030年)》统计监测报告: 2025年12月[N]. 中国信息报, 2025-12-31(002).
[53] Stephano, E.E., Majengo, V.G., John, T.W. and Mtoro, M.J. (2026) Individual-Level and Community-Level Factors Associated with Breast Cancer Screening among Women of Reproductive Age in Tanzania: A Multilevel Analysis of the 2022 Tanzania Demographic and Health Survey. BMJ Public Health, 4, e002802. [Google Scholar] [CrossRef] 
[54] Sun, L., Sadique, Z., dos-Santos-Silva, I., Yang, L. and Legood, R. (2018) Cost-Effectiveness of Breast Cancer Screening Programme for Women in Rural China. International Journal of Cancer, 144, 2596-2604. [Google Scholar] [CrossRef] [PubMed]
[55] Zhou, D., Zhou, K., Wang, W., Shao, H., Zhang, H. and Tang, W. (2025) Comparing the Cost-Benefit of Breast Cancer Screening Programs in Rural and Urban Areas across Four Economic Zones in China: A Markov Modeling Analysis. BMC Public Health, 25, Article No. 256. [Google Scholar] [CrossRef] [PubMed]
[56] Peng, S., Liu, Y., Lv, W., Liu, L., Zhou, Q., Yang, H., et al. (2021) Deep Learning-Based Artificial Intelligence Model to Assist Thyroid Nodule Diagnosis and Management: A Multicentre Diagnostic Study. The Lancet Digital Health, 3, e250-e259. [Google Scholar] [CrossRef] [PubMed]
[57] 郭嫦媛, 薛学敏, 郭蕾, 等. 国家癌症中心病理科乳腺癌规范化质量控制工作经验总结[J]. 肿瘤综合治疗电子杂志, 2023, 9(3): 30-35.
[58] Zhang, S., Zhou, L., Yi, L., Chen, X., Zhang, Y., Li, J., et al. (2024) Comparative Efficacy of Telehealth Interventions on Promoting Cancer Screening: A Network Meta-Analysis of Randomized Controlled Trials. Journal of Nursing Scholarship, 56, 585-598. [Google Scholar] [CrossRef] [PubMed]
[59] 陈晓霞, 陈伟, 林灿祥, 等. 液体活检技术及其在肿瘤早筛中的应用进展[J]. 消化肿瘤杂志(电子版), 2019, 11(1): 1-6.
[60] 李洪祯, 沈珊珊. 液体活检在胰腺癌诊疗中的应用[J]. 中华胰腺病杂志, 2023, 22(6): 420-425.
[61] Cullinane, C., Fleming, C., O’Leary, D.P., Hassan, F., Kelly, L., O’Sullivan, M.J., et al. (2020) Association of Circulating Tumor DNA with Disease-Free Survival in Breast Cancer: A Systematic Review and Meta-Analysis. JAMA Network Open, 3, e2026921. [Google Scholar] [CrossRef] [PubMed]
[62] Guo, N., Zhou, Q., Chen, X., Zeng, B., Wu, S., Zeng, H., et al. (2024) Circulating Tumor DNA as Prognostic Markers of Relapsed Breast Cancer: A Systematic Review and Meta-analysis. Journal of the National Cancer Center, 4, 63-73. [Google Scholar] [CrossRef] [PubMed]
[63] Guo, N., Zhou, Q., Zhang, M., Chen, X., Zeng, B., Wu, S., et al. (2024) The Prognostic Role of Circulating Tumor DNA across Breast Cancer Molecular Subtypes: A Systematic Review and Meta-analysis. Journal of the National Cancer Center, 4, 153-161. [Google Scholar] [CrossRef] [PubMed]
[64] 赵宇婷, 杨宇晨, 蔡智慧. 液体活检技术在胰腺癌早期筛查和诊断中的应用[J]. 国际检验医学杂志, 2023, 44(24): 3054-3058.
[65] Wijesinghe, H.D. and Lokuhetty, M.D.S. (2023) Liquid Biopsy in Breast Carcinoma: Are We There Yet. The Malaysian Journal of Pathology, 45, 175-186.
[66] Fiste, O., Liontos, M., Koutsoukos, K., Terpos, E., Dimopoulos, M.A. and Zagouri, F. (2020) Circulating Tumor DNA-Based Predictive Biomarkers in Breast Cancer Clinical Trials: A Narrative Review. Annals of Translational Medicine, 8, 1603-1603. [Google Scholar] [CrossRef] [PubMed]
[67] 陈万青, 陈可欣, 贺宇彤, 等. 基于液体活检技术的多癌种联合筛查专家共识(2025版) [J]. 中国肿瘤临床, 2025, 52(14): 727-742.
[68] Khan, F., Agarwal, P., Gupta, S., Maurya, M.K., Singh, P., Agarwal, A., et al. (2023) BRCA1 Promoter Methylation & Its Immunohistochemical Correlation in Sporadic Breast Cancer. Indian Journal of Medical Research, 158, 47-54. [Google Scholar] [CrossRef] [PubMed]
[69] Li, S., He, Y., Li, C., Liu, X., Shen, Y., Wu, Y., et al. (2020) The Association between the Methylation Frequency of BRCA1/2 Gene Promoter and Occurrence and Prognosis of Breast Carcinoma: A Meta-Analysis. Medicine, 99, e19345. [Google Scholar] [CrossRef] [PubMed]
[70] Wang, K., Li, L., Franch‐Expósito, S., Le, X., Tang, J., Li, Q., et al. (2022) Integrated Multi‐Omics Profiling of High‐grade Estrogen Receptor‐Positive, HER2‐Negative Breast Cancer. Molecular Oncology, 16, 2413-2431. [Google Scholar] [CrossRef] [PubMed]
[71] Pouryahya, M., Oh, J.H., Javanmard, P., Mathews, J.C., Belkhatir, Z., Deasy, J.O., et al. (2022) Awcluster: A Novel Integrative Network-Based Clustering of Multiomics for Subtype Analysis of Cancer Data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19, 1472-1483. [Google Scholar] [CrossRef] [PubMed]

为你推荐