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
Research progress on H-E stained whole slide image analysis by artificial intelligence in lung cancer
姜梦琦 1韩昱晨 2傅小龙1
作者信息
- 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
- 折叠
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.
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.
关键词
全切片扫描/肺癌/人工智能/卷积神经网络Key words
Whole slide imaging/Lung cancer/Artificial intelligence/Convolutional neural network引用本文复制引用
基金项目
国家自然科学基金重大研究计划(92059206)
出版年
2024
国家科技期刊平台
NSTL
万方数据