乳腺癌多学科诊疗的现状、挑战与创新模式

doi:10.6040/j.issn.1671-7554.0.2025.0036

摘要/Abstract

摘要： 多学科诊疗(multidisciplinary treatment, MDT)模式已成为当前优化乳腺癌诊疗的重要策略。本述评从山东省乳腺癌诊疗的现状出发,全面分析优化MDT乳腺癌诊疗的实施情况及面临的主要挑战,包括亚专业不完善、数据共享能力不足、资源配置不均以及医务人员积极性和患者参与度较低的困境。通过梳理山东省乳腺癌诊疗MDT模式本地化实践中的经验,提出一系列适合国情的创新模式,包括规范化诊疗流程、构建高效的信息共享平台、强化患者随访与支持系统以及深化专科联盟和区域合作网络。并进一步结合乳腺癌分子生物学、影像组学和人工智能等领域的最新研究,探讨多学科协作与现代技术在乳腺癌诊疗中的应用前景,指出数字医疗转型和精准医学对个体化治疗的推动作用,旨在为国内的乳腺癌诊疗工作提供理论支持和实践借鉴。

关键词: 乳腺癌, 多学科诊疗, 精准医学, 信息共享平台, 数字医疗

Abstract: With the rising incidence of breast cancer, the multidisciplinary treatment(MDT)mode has become an important strategy for optimizing breast cancer diagnosis and treatment. This article analyzes the current status of breast cancer diagnosis and treatment in Shandong Province and comprehensively examines the implementation of MDT and the major challenges it faces, including the inadequacy of subspecialties, insufficient data sharing capabilities, uneven resource allocation, and the low enthusiasm of medical staff and patient participation. By summarizing Shandong Provinces experiences in the localized practice of the MDT mode, a series of innovative pathways suitable for national conditions are proposed, including standardizing treatment processes, building efficient information sharing platforms, strengthening patient follow-up and support systems, and deepening professional alliances and regional cooperation networks. Furthermore, by integrating the latest research in the fields of molecular biology, radiomics, and artificial intelligence in breast cancer, the prospects of multidisciplinary collaboration and modern technology in breast cancer diagnosis and treatment are explored, highlighting the role of digital healthcare transformation and precision medicine in promoting personalized treatment, aiming to provide theoretical support and practical references for breast cancer diagnosis and treatment in our country.

Key words: Breast cancer, Multidisciplinary treatment, Precision medicine, Information sharing platform, Digital healthcare

中图分类号:

R737.9

余之刚,郑超. 乳腺癌多学科诊疗的现状、挑战与创新模式[J]. 山东大学学报 (医学版), 2025, 63(1): 1-9.

YU Zhigang, ZHENG Chao. Current status, challenges and innovative approaches of multidisciplinary treatment for breast cancer[J]. Journal of Shandong University (Health Sciences), 2025, 63(1): 1-9.

参考文献

[1] 山东省医学会乳腺疾病多学科联合委员会. 乳腺癌多学科协作诊疗山东共识(2024年版)[J]. 山东大学学报(医学版), 2025, 63(1): 10-16. Multidisciplinary Joint Committee on Breast Disease of Shandong Provincial Medical Association. Shandong Province expert consensus on multidisciplinary treatment of breast cancer(2024 edition)[J]. Journal of Shandong University(Health Sciences), 2025, 63(1): 10-16.
[2] Emerson MA, Reeder-Hayes KE, Tipaldos HJ, et al. Integrating biology and access to care in addressing breast cancer disparities: 25 years research experience in the Carolina Breast Cancer Study[J]. Curr Breast Cancer Rep, 2020, 12(3): 149-160.
[3] Blackwood O, Deb R. Multidisciplinary team approach in breast cancer care: benefits and challenges[J]. Indian J Pathol Microbiol, 2020, 63(Supplement): S105-S112.
[4] Hadžikadic'-Guc'iš L, Ceric' T, Marijanovic' I, et al. Guidelines for breast cancer management in Bosnia and Herzegovina[J]. Biomol Biomed, 2023, 23(1): 2-14.
[5] Meduri B, De Rose F, Cabula C, et al. Hypofractionated breast irradiation: a multidisciplinary review of the Senonetwork study group[J]. Med Oncol, 2021, 38(6): 67. doi:10.1007/s12032-021-01514-w.
[6] Fabi A, Rossi A, Mocini E, et al. An integrated care approach to improve well-being in breast cancer patients[J]. Curr Oncol Rep, 2024, 26(4): 346-358.
[7] Rodríguez-Reinado C, Delgado-Parrilla A, Alguacil J. Breast cancer treatment in integrated care process in Andalusia: the challenge of multidisciplinarity[J]. Int J Environ Res Public Health, 2022, 19(19): 12728. doi:10.3390/ijerph191912728.
[8] Abdel-Razeq H, Mansour A. Challenges and opportunities in breast cancer care in low-resourced countries, Jordan as an example[J]. Cancers, 2024, 16(9): 1751. doi:10.3390/cancers16091751.
[9] 艾宪程, 王斐, 周文重, 等. 山东省38所三甲医院乳腺多学科诊疗现状[J]. 山东大学学报(医学版), 2025, 63(1): 108-114. AI Xiancheng, WANG Fei, ZHOU Wenzhong, et al. Current status of breast multidisciplinary team in 38 tertiary hospitals in Shandong Province, China[J]. Journal of Shandong University(Health Sciences), 2025, 63(1):108-114.
[10] Shinji O. Multidisciplinary care for patients with breast cancer[J]. Nihon Geka Gakkai Zasshi, 2017, 118(2): 173-178.
[11] Capobianco AL, Gallucci G, Lapadula L, et al. Impact of the multidisciplinary cancer team on the diagnostic and therapeutic care pathway of early breast cancer patients and perception of team members: the experience of a cancer centre in Italy[J]. Cancer Invest, 2024, 42(1): 12-20.
[12] Horlait M, Baes S, De Regge M, et al. Understanding the complexity, underlying processes, and influencing factors for optimal multidisciplinary teamwork in hospital-based cancer teams: a systematic integrative review[J]. Cancer Nurs, 2021, 44(6): 476-492.
[13] Puschel K, León A, Arancibia V, et al. The interdisciplinary and psychosocial gap in cancer survivorship: a longitudinal study in a Latin American Cancer Center[J]. J Surg Oncol, 2021, 124(5): 876-885.
[14] Cherny NI, Paluch-Shimon S, Berner-Wygoda Y. Palliative care: needs of advanced breast cancer patients[J]. Breast Cancer, 2018, 10: 231-243. doi:10.2147/BCTT.S160462.
[15] Aly SH, Abulsoud AI, Moustafa YM, et al. Harnessing natural compounds to modulate miRNAs in breast cancer therapy[J]. Funct Integr Genomics, 2024, 24: 211. doi: 10.1007/s10142-024-01489-7.
[16] Balcioglu O, Gates BL, Freeman DW, et al. Mcam stabilizes a luminal progenitor-like breast cancer cell state via Ck2 control and Src/Akt/Stat3 attenuation[J]. NPJ Breast Cancer, 2024, 10: 80. doi: 10.1038/s41523-024-00687-7.
[17] Sinha AP, Jurrius P, van Schelt AS, et al. Tumor biomechanics quantified using MR elastography to predict response to neoadjuvant chemotherapy in individuals with breast cancer[J]. Radiol Imaging Cancer, 2025, 7: e240138. doi: 10.1148/rycan.240138.
[18] Yu Y, Tan Y, Xie C, et al. Development and validation of a preoperative magnetic resonance imaging radiomics-based signature to predict axillary lymph node metastasis and disease-free survival in patients with early-stage breast cancer[J]. JAMA Network Open, 2020, 3: e2028086. doi: 10.1001/jamanetworkopen.2020.28086.
[19] Zhu JY, He HL, Lin ZM, et al. Ultrasound-based radiomics analysis for differentiating benign and malignant breast lesions: from static images to CEUS video analysis[J]. Front Oncol, 2022, 12: 951973. doi: 10.3389/fonc.2022.951973.
[20] Moser EC, Narayan G. Improving breast cancer care coordination and symptom management by using AI driven predictive toolkits[J]. Breast, 2020, 50: 25-29. doi:10.1016/j.breast.2019.12.006.
[21] Strach MC, Prasanna T, Kirova YM, et al. Optimise not compromise: the importance of a multidisciplinary breast cancer patient pathway in the era of oncoplastic and reconstructive surgery[J]. Crit Rev Oncol Hematol, 2019, 134: 10-21. doi:10.1016/j.critrevonc.2018.11.007.
[22] Wind A, Gonçalves FR, Marosi E, et al. Benchmarking cancer centers: from care pathways to integrated practice units[J]. J Natl Compr Canc Netw, 2018, 16(9): 1075-1083.
[23] Redjdal A, Bouaud J, Gligorov J, et al. Using machine learning and deep learning methods to predict the complexity of breast cancer cases[J]. Stud Health Technol Inform, 2022, 294: 78-82. doi:10.3233/SHTI220400.
[24] Vuksanovic D, Sanmugarajah J, Lunn D, et al. Unmet needs in breast cancer survivors are common, and multidisciplinary care is underutilised: the Survivorship Needs Assessment Project[J]. Breast Cancer, 2021, 28(2): 289-297.
[25] Monkkonen T, Traustadóttir Gá, Koledova Z. Unraveling the breast: advances in mammary biology and cancer methods[J]. J Mammary Gland Biol Neoplasia, 2020, 25(4): 233-236.
[26] Zhang X, Zhang D, Yu PD, et al. Effects of continuous care combined with evidence-based nursing on mental status and quality of life and self-care ability in patients with liver from breast cancer: a single-center randomized controlled study[J]. Comput Math Methods Med, 2022: 3637792. doi:10.1155/2022/3637792.
[27] Power K, Martone P, Khanna A, et al. The role of physiatry practice in a multidisciplinary breast tumor board: a descriptive study[J]. PM R, 2020, 12(5): 486-490.
[28] Cheng VT, Heetun A, Robinson T, et al. The breast cancer trainees research collaborative group: a new multidisciplinary network to facilitate breast cancer research[J]. Clin Oncol, 2020, 32(1): 16-18.
[29] 张舒妮,赵楠楠,李阳,等. 多模态影像组学列线图术前预测乳腺浸润性导管癌腋窝淋巴结转移的价值[J]. 磁共振成像, 2024, 15(4): 78-87. ZHANG Shuni, ZHAO Nannan, LI Yang, et al. Value of multimodal radiomics nomogram in predicting axillary lymph node metastasis in invasive ductal carcinoma of the breast before surgery[J]. Chin J Magn Reson Imaging, 2024, 15(4): 78-87.
[30] Verhoeven D, Allemani C, Kaufman C, et al. New frontiers for fairer breast cancer care in a globalized world[J]. Eur J Breast Health, 2021, 17(2): 86-94.

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