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).
Dongdong Yu, Yali Zang, Di Dong, Mu Zhou, Olivier Gevaert, Mengjie Fang, Jingyun Shi, and Jie Tian, "Developing a radiomics framework for classifying non-small cell lung carcinoma subtypes," Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013426 (Presented at SPIE Medical Imaging: February 17, 2017; Published: 3 March 2017); https://doi.org/10.1117/12.2253923.
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