The Research of Reproducibility and Non-redundancy Feature Selection Methods in Radiomics

Abstract

“Radiomics” is a process of extracting a great quantity of descriptive features from biomedical images that can be used for prognosis. One of the major challenges of radiomics is to acquire the features that reproducibility and non-redundancy. The reproducibility of features dependent on the segmentation algorithm, and it should provide an accurate and reproducible result. In this study, a semi-automated segmentation method based on, which has an good effect for gray scale inhomogeneous of tumors was used to get tumor region for computed tomographic (CT) images of 35 non–small cell lung cancer (NSCLC) patients. A set of features (125 3D and 92 2D) was computed for each tumor region in the test/retest data set. In terms of comparing the intra-class correlation coefficient (ICC) with manual segmentation method in feature extracting, it is indicated that the features obtained by the method of on CV model has a better representative. A series quantitative feature of better reproducibility could be obtained. However, these features were redundant. A feature selection method based on sparse representation coefficient (SRC) was used to filter these redundant features. It is indicated that the features obtained by SRC have better non-redundancy through comparison the selection methods based on Pearson correlation coefficient (PCC) and symmetrical uncertainty (SU). Thus quantitative image features that reproducibility and non-redundancy provide informative and prognostic biomarkers for NSCLC.