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中华乳腺病杂志(电子版) ›› 2023, Vol. 17 ›› Issue (06) : 323 -328. doi: 10.3877/cma.j.issn.1674-0807.2023.06.001

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深度学习在乳腺癌影像诊疗和预后预测中的应用
唐玮, 何融泉, 黄素宁()   
  1. 530021 南宁,广西医科大学附属肿瘤医院乳腺外科
    530021 南宁,广西医科大学第一附属医院肿瘤内科
    530021 南宁,广西医科大学附属肿瘤医院放疗科
  • 收稿日期:2022-10-18 出版日期:2023-12-01
  • 通信作者: 黄素宁
  • 基金资助:
    国家自然科学基金地区基金项目(82060309); 广西壮族自治区自然科学基金面上项目(2021JJA140129); 南宁市青秀区科技计划项目重点研发计划(2020020)

Application of deep learning in imaging for diagnosis, treatment and prognosis prediction of breast cancer

Wei Tang, Rongquan He, Suning Huang()   

  1. Department of Breast Surgery, Cancer Hospital of Guangxi Medical University, Nanning 530021, China
    Department of Medical Oncology, First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China
    Department of Radiotherapy, Cancer Hospital of Guangxi Medical University, Nanning 530021, China
  • Received:2022-10-18 Published:2023-12-01
  • Corresponding author: Suning Huang
引用本文:

唐玮, 何融泉, 黄素宁. 深度学习在乳腺癌影像诊疗和预后预测中的应用[J/OL]. 中华乳腺病杂志(电子版), 2023, 17(06): 323-328.

Wei Tang, Rongquan He, Suning Huang. Application of deep learning in imaging for diagnosis, treatment and prognosis prediction of breast cancer[J/OL]. Chinese Journal of Breast Disease(Electronic Edition), 2023, 17(06): 323-328.

近年来,人工智能技术特别是深度学习技术的计算能力显著提升,发展迅猛,已然成为从X射线、CT、MRI、正电子发射断层扫描和超声等多模态图像中捕获感兴趣组织的形状和纹理的有效方法。本文回顾了上述医学成像方法的深度学习在乳腺癌的早期发现、精确诊断、个性化治疗和预后预测中的应用,旨在提高乳腺癌患者的临床管理,改善患者预后。最后,本文总结了这一具有挑战性的研究领域现阶段的困境,并讨论了其未来的发展前景。

In recent years, artificial intelligence, especially deep learning, has developed rapidly. It has become an effective method to capture the shape and texture of interested tissues from multi-modal images including X-ray, CT, MRI, positron emission tomography and ultrasound. This article comprehensively reviewed the application of deep learning in the early detection, accurate diagnosis, individualized treatment and prognosis prediction of breast cancer, based on the above-mentioned medical imaging methods, aiming to improve the clinical management of breast cancer patients and their prognosis. Finally, this paper summarized the current dilemma of this challenging research field and discussed its future prospect.

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