|本期目录/Table of Contents|

[1]方胜儒,李逸凡,张宇威,等.放射组学在肺癌诊断中的应用[J].天津医科大学学报,2018,24(06):480-483.
　FANG Sheng-ru,LI Yi-fan,ZHANG Yu-wei,et al.The application of radiomics in the diagnosis of lung cancer[J].Journal of Tianjin Medical University,2018,24(06):480-483.
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《天津医科大学学报》[ISSN:1006-8147/CN:12-1259/R]

卷:
24卷
期数:
2018年06期
页码:
480-483
栏目:
基础医学
出版日期:
2018-11-20

文章信息/Info

Title:
The application of radiomics in the diagnosis of lung cancer
作者:
方胜儒; 李逸凡; 张宇威; 蔡 娜; 郭 丽
（天津医科大学医学影像学院，天津 300203）
Author(s):
FANG Sheng-ru;  LI Yi-fan; ZHANG Yu-wei; CAI Na;  GUO Li
（School of Medical Imaging, Tianjin Medical University, Tianjin 300203, China）
关键词:
计算机辅助诊断技术; 肺结节; 放射组学; 纹理特征
Keywords:
computer-aided diagnosis;  pulmonary nodules;  radiomics;  texture feature
分类号:
R816.41
DOI:
-
文献标志码:
A
摘要:
目的：通过放射组学对肺癌病例进行定量特征提取，优化选择，然后通过机器学习方法实现肺癌病例讨论和分析。方法：通过公开数据库LIDC中提取224例和医院收集250例肺结节病例，提取共841个放射组学特征；对特征进行正态分析和方差齐性分析，双独立样本t检验进行降维；其余采用秩和分析降维，之后采取Pearson相关系数降维，最后通过机器学习方法进行分类。结果：来自LIDC数据库和来自医院的数据在基于随机森林的分类器中的结果分别为AUC=0.657 1、ACC=76.26%，AUC=0.866 7、ACC=76%；在基于支持向量机的分类器中的结果分别为AUC=0.642 9，ACC=76.37%，AUC=0.773 3、ACC=72%。结论：在肺癌良恶诊断鉴别中，使用放射组学特征方法可以鉴别良恶性。基于纹理特征的计算机辅助诊断系统可以提高对此类结节的诊断效能。
Abstract:
Objective: To quantitatively extract and optimizeradiomicsfeatures for lung cancer cases and to analyze and discuss lung cancer cases by machine learning method. Methods:We obtained images of 224 patients from LIDC databaseand 250 patients from hospital, and 841 radiomicsfeatures were extracted. The features were used to perform dimensionality reduction by the double independent sample t-test, when normality of distribution and Homogeneity variance were calculated. Futhermore, the dimensionality reduction was performed by the rank sum test. And then, the Pearson correlation coefficient was used for further dimensionality reduction.Finally, machine learning method was used for classification. Results: In the classifier based on the random forest, the LIDC database showed that ACC=76.26%, AUC=0.657 1 and the data from the Hospital showed that ACC=76%, AUC=0.866 7. In the classifier based on the support vector machine, the LIDC database showed that ACC=76.37%, AUC=0.642 9, and the data from the hospital showed that ACC=72%, AUC=0.773 3. Conclusion: In pulmonary nodules, radiomics can be used to identify benign and malignant nodules. Texture-based computer-aided diagnosis systems may improve the diagnostic efficacy on pulmonary nodules.

参考文献/References:


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备注/Memo

备注/Memo:
基金项目 国家自然科学基金青年基金资助项目（81000639），天津医科大学校基金资助项目（2015KYZQ19）
作者简介 方胜儒（1987-），男，硕士在读，研究方向：生物医学工程；通信作者：郭丽，E-mail:gl6290@126.com。
更新日期/Last Update: 2018-11-30