中国癌症杂志 ›› 2024, Vol. 34 ›› Issue (3): 306-315.doi: 10.19401/j.cnki.1007-3639.2024.03.009

• 综述 • 上一篇    下一篇

基于人工智能的H-E染色全切片病理学图像分析在肺癌研究中的进展

姜梦琦 综述1(), 韩昱晨 审校2, 傅小龙 审校1()   

  1. 1.上海市胸科医院/上海交通大学医学院附属胸科医院放疗科,上海 200030
    2.上海市胸科医院/上海交通大学医学院附属胸科医院病理科,上海 200030
  • 收稿日期:2024-01-05 修回日期:2024-02-05 出版日期:2024-03-30 发布日期:2024-04-08
  • 通信作者: 傅小龙(ORCID: 0000-0001-8127-3884),博士,主任医师、教授,上海市胸科医院/上海交通大学医学院附属胸科医院放疗科主任。
  • 作者简介:姜梦琦(ORCID: 0009-0000-3691-5251),博士在读。
  • 基金资助:
    国家自然科学基金重大研究计划(92059206)

Research progress on H-E stained whole slide image analysis by artificial intelligence in lung cancer

JIANG Mengqi1(), HAN Yuchen2, FU Xiaolong1()   

  1. 1. Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
    2. Department of Pathology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
  • Received:2024-01-05 Revised:2024-02-05 Published:2024-03-30 Online:2024-04-08
  • Contact: FU Xiaolong

摘要:

病理学是疾病诊断的金标准。利用全切片扫描技术将病理切片转化为数字图像后,人工智能特别是深度学习模型在病理学图像分析领域展现出了巨大潜力。人工智能在肺癌全切片扫描中的应用涉及组织病理学分型、肿瘤微环境分析、疗效及生存预测等多个方面,有望辅助临床进行精准治疗决策。然而标注数据不足、切片质量差异等因素也限制了病理学图像分析的发展。本文总结了肺癌领域利用人工智能手段进行病理学图像分析的应用进展,并对未来发展方向进行展望。

关键词: 全切片扫描, 肺癌, 人工智能, 卷积神经网络

Abstract:

Pathology is the gold standard for diagnosis of neoplastic diseases. Whole slide imaging turns traditional slides into digital images, and artificial intelligence has shown great potential in pathological image analysis, especially deep learning models. The application of artificial intelligence in whole slide imaging of lung cancer involves many aspects such as histopathological classification, tumor microenvironment analysis, efficacy and survival prediction, etc., which is expected to assist clinical decision-making of accurate treatment. Limitations in this field include the lack of precisely annotated data and slide quality varying among institutions. Here we summarized recent research in lung cancer pathology image analysis leveraging artificial intelligence and proposed several future directions.

Key words: Whole slide imaging, Lung cancer, Artificial intelligence, Convolutional neural network

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