摘要
影像组学和深度学习是如今热门的研究方法,在肿瘤的诊断、鉴别诊断、分型、治疗反应及预后等方面已初步显示出重要作用。本文旨在介绍影像组学的工作流程及深度学习的基本概念,并综述其在胃癌预后和疗效评价中的应用进展,对现有研究的成果以及未来的研究挑战和发展机遇进行总结,以期为临床对胃癌的精准诊疗提供价值。
Radiomics and deep learning are popular research methods,which have initially shown important roles in the diagnosis,differential diagnosis,classification,treatment response and prognosis of tumors.The purpose of this paper is to introduce the workflow of radiomics and the basic concepts of deep learning,and review the application progress of radiomics in the prognosis and efficacy evaluation of gastric cancer,and summarize the existing research results,research challenges and development opportunities in the future,in order to provide value for the precise diagnosis and treatment of gastric cancer in clinical practice.
作者
陈星池
王冬青
庄子健
彭琳
薛靖
张礼荣
CHEN Xing-chi;WANG Dong-qing;ZHUANG Zi-jian;PENG Lin;XUE Jing;ZHANG Li-rong(Department of Medical Imaging,Affiliated Hospital of Jiangsu University,Zhenjiang 212000,Jiangsu Province,China)
出处
《中国CT和MRI杂志》
2024年第6期179-182,共4页
Chinese Journal of CT and MRI
基金
镇江“金山英才”高层次领军人才培养计划(第六期“169工程”)培养对象科研项目(YLJ202111)。
关键词
胃癌
影像组学
深度学习
预后
Gastric Cancer
Radiomics
Deep Learning
Prognosis