A sparse representation‐based radiomics for outcome prediction of higher grade gliomas

• Published in: Medical physics (Lancaster) Vol. 46; no. 1; pp. 250 - 261
• Main Authors: Wu, Guoqing, Shi, Zhifeng, Chen, Yinsheng, Wang, Yuanyuan, Yu, Jinhua, Lv, Xiaofei, Chen, Liang, Ju, Xue, Chen, Zhongping
• Format: Journal Article
• Language: English
• Published: United States 01.01.2019
• Subjects: higher grade gliomas; outcome prediction; SIFT feature; sparse representation; outcome prediction; higher grade gliomas; SIFT feature; sparse representation
• ISSN: 0094-2405; 2473-4209; 2473-4209
• DOI: 10.1002/mp.13288

Purpose Accurately predicting outcome (i.e., overall survival (OS) time) for higher grade glioma (HGG) has great clinical value and would provide optimized guidelines for treatment planning. Radiomics focuses on revealing underlying pathophysiological information in biomedical images for disease analysis and demonstrates promising prognostic clinical performance. In this paper, we propose a novel sparse representation‐based radiomics framework to predict if HGG patients would have long or short OS time. Methods First, taking advantages of the scale invariant feature transform (SIFT) feature in image characterizing, we developed a sparse representation‐based method to convert a local SIFT descriptor into a global tumor feature. Next, because preserving sample structure is beneficial for feature selection, we proposed a locality preserving projection and sparse representation‐combined feature selection method to select more discriminative features for tumor classification. Finally, we employed a multifeature collaborative sparse representation classification to combine the information of multimodal images to classify OS time. Results Three experiments were performed on the two datasets provided by different institutions. Specifically, the proposed model was trained and independently tested on dataset 1 (135 subjects), on dataset 2 (86 subjects), and on the combination of dataset 1 and dataset 2, respectively. Experimental results demonstrated that the proposed method achieved encouraging prediction performance, exhibiting a testing accuracy of 93.33% on dataset 1 (one modality), 92.31% on dataset 2 (two modalities), and 87.93% on the combined dataset (one modality). Conclusions The sparse representation theory provides reasonable solutions to feature extraction, feature selection, and classification for radiomics. This study provides a promising tool to enhance the prediction performance of HGG patient's outcome.