[1]李双双,侯震,刘娟,等.影像组学分析与建模工具综述[J].中国医学物理学杂志,2018,35(9):1043-1049.[doi:10.3969/j.issn.1005-202X.2018.09.010]
　LI Shuangshuang,HOU Zhen,LIU Juan,et al.Review of radiomic analysis and modeling tools[J].Chinese Journal of Medical Physics,2018,35(9):1043-1049.[doi:10.3969/j.issn.1005-202X.2018.09.010]
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影像组学分析与建模工具综述()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
35卷
期数:
2018年第9期
页码:
1043-1049
栏目:
医学影像物理
出版日期:
2018-09-27

文章信息/Info

Title:
Review of radiomic analysis and modeling tools
文章编号:
1005-202X（2018）09-1043-07
作者:
李双双1; 侯震2; 刘娟1; 任伟1; 万遂人2; 闫婧1
1. 南京大学医学院附属鼓楼医院肿瘤中心，江苏南京210000；2. 东南大学生物科学与医学工程学院/生物电子学国家重点实验 室/医学电子学实验室，江苏南京210096
Author(s):
LI Shuangshuang1;  HOU Zhen2;  LIU Juan1;  RENWei1; WAN Suiren2;  YAN Jing1
1. Comprehensive Cancer Centre, Drum Tower Hospital, Medical School of Nanjing University & Clinical Cancer Institute of Nanjing University, Nanjing 210000, China; 2. Laboratory for Medical Electronics, State Key Laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China
关键词:
影像组学; 肿瘤; 医学影像; 建模; 综述
Keywords:
Keywords: radiomics;  tumor;  medical imaging;  modeling;  review
分类号:
R318
DOI:
10.3969/j.issn.1005-202X.2018.09.010
文献标志码:
A
摘要:
影像组学作为一种非侵入性的图像分析方法，大量研究证实其在肿瘤诊断、分期、治疗反应预测和预后中的研究 价值。本文从影像组学的概念、分析流程、国内外研究现状，以及最常用图像分割、特征提取、分析和建模预测软件方面进 行综述。通过本文可快速熟悉和了解不同软件的特点，为影像组学在临床上的进一步应用提供良好的契机和实践基础。 随着医学影像数据的进一步积累和标准化，以及人工智能和深度学习的发展，将为影像组学指引新的方向。
Abstract:
Abstract: Radiomics is a noninvasive method of image analysis, and numerous studies have confirmed the research value of radiomics in the tumor diagnosis, stage, treatment response prediction and prognosis. Herein the concept, analysis process and current research status of radiomics are introduced, and the commonly used software for image segmentation and feature extraction, and analysis and modeling tools are summarized. The characteristics of various software programs available for radiomics research are described, offering a good opportunity and foundation for further application of radiomics in clinical practice. With the further accumulation and standardization of medical image data, as well as the development of artificial intelligence and deep learning, it will provide us with the new directions of radiomics.

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备注/Memo

备注/Memo:
【收稿日期】2018-05-25 【作者简介】李双双，研究方向：肿瘤放射物理学，E-mail: liss1014@126. com；侯震，研究方向：肿瘤放射物理学，E-mail: houzhencn@ 163.com（李双双与侯震为共同第一作者） 【通信作者】闫婧，研究员，研究方向：肿瘤放射治疗学，E-mail: yj20030610 @126.com
更新日期/Last Update: 2018-09-29