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Proceedings Paper

Developing a radiomics framework for classifying non-small cell lung carcinoma subtypes
Author(s): Dongdong Yu; Yali Zang; Di Dong; Mu Zhou; Olivier Gevaert; Mengjie Fang; Jingyun Shi; Jie Tian
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Paper Abstract

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 different machine-learning classifiers 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).

Paper Details

Date Published: 3 March 2017
PDF: 7 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013426 (3 March 2017); doi: 10.1117/12.2253923
Show Author Affiliations
Dongdong Yu, Institute of Automation (China)
Yali Zang, Institute of Automation (China)
Di Dong, Institute of Automation (China)
Mu Zhou, Stanford Univ. (United States)
Olivier Gevaert, Stanford Univ. (United States)
Mengjie Fang, Institute of Automation (China)
Jingyun Shi, Tongji Univ. School of Medicine (China)
Jie Tian, Institute of Automation (China)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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