摘要
肿瘤的精确诊断对患者的治疗方案选择和预后预测都非常重要。病理学诊断被认为是肿瘤诊断的"金标准",但是,病理学发展目前仍然面临着巨大的挑战,如病理医生的缺乏,特别是在欠发达地区和小医院,这将导致病理医生长期超负荷工作,同时,病理诊断严重依赖于病理医生的专业知识和诊断经验,病理医生的主观性导致了诊断不一致性的激增。全切片扫描图像(Whole Slide Images,WSI)技术和深度学习方法的突破性进展为计算机辅助诊断和预后预测提供了新的发展机遇。苏木精-伊红(Hematoxylin-Eosin,H&E)染色的组织病理切片可以很好地显示细胞形态和组织结构,而且制作简单、成本便宜、使用广泛。仅仅从H&E染色的病理图像可以预测什么?在将深度学习方法应用到病理图像领域之后,这个问题得到了新的答案。文中首先总结了基于深度学习和病理图像的肿瘤相关指标预测的整体研究框架,按照整体研究框架发展的顺序将其总结为3个逐渐推进的阶段:基于人工选取感兴趣的单张图片小块进行WSI预测研究、基于多数投票的WSI预测研究以及具有普遍适用性的WSI预测研究。其次简单介绍了4种在WSI预测中经常用到的监督学习或弱监督学习方法:卷积神经网络、循环神经网络、图神经网络和多示例学习。然后综述了可以通过病理图像预测的肿瘤相关指标以及其最新研究进展,文中主要从两个方面进行文献的综述:预测专家可以阅片识别的肿瘤相关指标(肿瘤分类、肿瘤分级、肿瘤区域识别)和预测专家无法阅片识别的肿瘤相关指标(基因变异预测、分子亚型预测、治疗效果评估、生存期预测)。最后展望了该领域面临的挑战和机遇。
Accurate diagnosis of tumor is very important for customizing treatment plans and predicting prognosis.Pathological diagnosis is considered the “gold standard” for tumor diagnosis,but the development of pathology still faces great challenges,such as the lack of pathologists,especially in underdeveloped areas and small hospitals,has led to long-term overload of pathologists.At the same time,pathological diagnosis relies heavily on the professional knowledge and diagnostic experience of pathologists,and this subjectivity of pathological diagnosis has led to a surge in diagnostic inconsistencies.The breakthrough of whole slide images(WSI) technology and deep learning methods provides new development opportunities for computer-aided diagnosis and prognosis prediction.Histopathological sections stained with hematoxylin-eosin(H&E) can show cell morphology and tissue structure very well,and are simple to make,inexpensive,and widely used.What can be predicted from pathological images alone? After the deep learning method was applied to the field of pathological images,this question got a new answer.In this paper,we first summarize the overall research framework of tumor-related indicators prediction based on deep learning and pathological images.According to the development sequence of the overall research framework,it can be summarized into three progressive stages:WSI predictions based on manually selected single patch,WSI predictions based on majority voting,and WSI predictions with general applicability;Secondly,four supervised or weakly supervised learning methods commonly used in WSI prediction are briefly introduced:convolutional neural network(CNN),recurrent neural network(RNN),graph neural network(GNN),multiple instance learning(MIL).Then,we reviewed the related deep learning methods used in this field,what are the tumor-related indicators that can be predicted through pathological images,and the latest research progress.We mainly reviewed the literature from two aspects:predicting tumor-related indicators(tumor classification,tumor grading,tumor area recognition) that pathologists can read and recognize,and predicting tumor-related indicators(genetic variation prediction,molecular subtype prediction,treatment effect evaluation,survival time prediction) that pathologists cannot read and recognize.Finally,the general problems in this field are summarized,and the possible development direction in the future is suggested.
作者
颜锐
梁智勇
李锦涛
任菲
YAN Rui;LIANG Zhi-yong;LI Jin-tao;REN Fei(Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China;Department of Pathology,Peking Union Medical College Hospital,Beijing 100730,China)
出处
《计算机科学》
CSCD
北大核心
2022年第2期69-82,共14页
Computer Science
基金
国家自然科学基金(82072939)。
关键词
病理图像
深度学习
肿瘤相关指标
计算机辅助诊断
预测模型
医学图像分析
Pathological images
Deep learning
Tumor-related indicators
Computer-aided diagnosis
Prediction model
Medical image analysis
