CASIA OpenIR  > 中国科学院分子影像重点实验室
Developing a Radiomics Framework for Classifying Non-Small Cell Lung Carcinoma Subtypes
Yu, Dongdong1; Zang, Yali1; Dong, Di1; Zhou, Mu2; Gevaert, Olivier2; Shi, Jingyun3; Tian, Jie1
2017
Conference NameSPIE Medical Imaging
Source PublicationSPIE Medical Imaging 2017
Conference Date2017-02
Conference PlaceOrlando, Florida USA
AbstractPatient-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).
KeywordRadiomics
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12493
Collection中国科学院分子影像重点实验室
Corresponding AuthorDong, Di; Shi, Jingyun; Tian, Jie
Affiliation1.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
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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.
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