Developing a Radiomics Framework for Classifying Non-Small Cell Lung Carcinoma Subtypes

	Developing a Radiomics Framework for Classifying Non-Small Cell Lung Carcinoma Subtypes
	Yu, Dongdong1 ; Zang, Yali1 ; Dong, Di1 ; Zhou, Mu 2; Gevaert, Olivier 2; Shi, Jingyun 3; Tian, Jie 1
	2017
会议名称	SPIE Medical Imaging
会议录名称	SPIE Medical Imaging 2017
会议日期	2017-02
会议地点	Orlando, Florida USA
摘要	Patient-targeted treatment of non-small cell lung carcinoma (NSCLC) has been well documented according to the histologic subtypes over the past decade. In parallel, recent development of quantitative image biomarkers has recently been highlighted as important diagnostic tools to facilitate histological subtype classification. In this study, we present a radiomics analysis that classifies the adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). We extract 52-dimensional, CT-based features (7 statistical features and 45 image texture features) to represent each nodule. We evaluate our approach on a clinical dataset including 324 ADCs and 110 SqCCs patients with CT image scans. Classification of these features is performed with four dierent machine-learning classiers including Support Vector Machines with Radial Basis Function kernel (RBF-SVM), Random forest (RF), K-nearest neighbor (KNN), and RUSBoost algorithms. To improve the classifiers' performance, optimal feature subset is selected from the original feature set by using an iterative forward inclusion and backward eliminating algorithm. Extensive experimental results demonstrate that radiomics features achieve encouraging classification results on both complete feature set (AUC=0.89) and optimal feature subset (AUC=0.91).
关键词	Radiomics
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12493
专题	中国科学院分子影像重点实验室
通讯作者	Dong, Di; Shi, Jingyun; Tian, Jie
作者单位	1.The Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences 2.The Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University 3.Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Yu, Dongdong,Zang, Yali,Dong, Di,et al. Developing a Radiomics Framework for Classifying Non-Small Cell Lung Carcinoma Subtypes[C],2017.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
2017.SPIEMI.YuDongdo（243KB）	会议论文		开放获取	CC BY-NC-SA	浏览下载