Developing a Radiomics Framework for Classifying Non-Small Cell Lung Carcinoma Subtypes
文献类型：会议论文
| 作者 | Yu, Dongdong1 ; Zang, Yali1; Dong, Di1; Zhou, Mu2; Gevaert, Olivier2; Shi, Jingyun3; Tian, Jie1
|
| 出版日期 | 2017 |
| 会议日期 | 2017-02 |
| 会议地点 | Orlando, Florida USA |
| 关键词 | Radiomics |
| 英文摘要 | 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 di erent machine-learning classi ers 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). |
| 会议录 | SPIE Medical Imaging 2017
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12493]  |
| 专题 | 自动化研究所_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 |
| 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]. 见:. Orlando, Florida USA. 2017-02. |
入库方式： OAI收割
来源：自动化研究所
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