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

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 ana...

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Veröffentlicht in:	Medical physics (Lancaster) 2019-01, Vol.46 (1), p.250-261
Hauptverfasser:	Wu, Guoqing, Shi, Zhifeng, Chen, Yinsheng, Wang, Yuanyuan, Yu, Jinhua, Lv, Xiaofei, Chen, Liang, Ju, Xue, Chen, Zhongping
Format:	Artikel
Sprache:	eng
Schlagworte:	Adolescent Adult Algorithms Child Child, Preschool Female Glioma - diagnosis Glioma - diagnostic imaging Glioma - pathology higher grade gliomas Humans Image Processing, Computer-Assisted Infant Infant, Newborn Male Middle Aged Neoplasm Grading outcome prediction Prognosis SIFT feature sparse representation Survival Analysis Young Adult
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