影像组学的临床价值及面临的挑战Clinical Value and Challenges of Radiomics
刘再毅;
LIU Zai-yi;Department of Radiology,Guangdong General Hospital;
摘要(Abstract)：
影像组学可将医学影像资料转化为可挖掘的数据。利用计算机软件从医学影像资料中挖掘海量的深层图像信息,再基于机器学习或统计学方法,筛选出关键的影像组学特征,构建模型,用于疾病的精准诊断、疗效评估和预后预测等,在辅助临床决策方面具有重要作用。尽管目前已有多项研究证实了影像组学在临床中的重要价值,但其仍是一门新兴的学科,在将影像组学研究成果转化为临床实践前,仍面临着诸如数据标准化、模型验证等问题。本文对影像组学的临床价值和目前研究中面临的挑战进行简要阐述。
Radiomics can convert medical images into minable data,which extracts high-throughput features from the images. By machine learning or statistical methods,key radiomics features were selected and used for model development to aid in clinical decision making on precise diagnosis,treatment evaluation,outcome prediction,etc. Though a lot of studies have demonstrated the useful potential of radiomics in clinical management,radiomics is still facing a lot of challenges such as data standardization,model validation before it could be implemented in clinical practice. In this short-review,clinical values and challenges will be briefly addressed.
关键词(KeyWords)：
影像组学;临床决策;数据挖掘
radiomics;clinical decision making;data mining
基金项目(Foundation): 国家重点研发计划(2017YFC130910002);; 国家自然科学基金面上项目(81771912)
作者(Authors):
刘再毅;
LIU Zai-yi;Department of Radiology,Guangdong General Hospital;
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