Author/Authors :
Ye,Jianming First Affiliated Hospital - Gannan Medical University, China , Huang, He School of Medical Technology and Information Engineering - Zhejiang Chinese Medical University, China , Jiang, Weiwei School of Medical Technology and Information Engineering - Zhejiang Chinese Medical University, China , Xu,Xiaomei School of Medical Technology and Information Engineering - Zhejiang Chinese Medical University, China , Xie,Chun First Affiliated Hospital - Gannan Medical University, China , Lu, Bo Faculty of Engineering - Shanghai Normal University Tianhua College, China , Wang,Xiangcai First Affiliated Hospital - Gannan Medical University, China , Lai , Xiaobo School of Medical Technology and Information Engineering - Zhejiang Chinese Medical University, China
Abstract :
Glioma is one of the most common and deadly malignant brain tumors originating from glial cells. For personalized treatment, an accurate preoperative prognosis for glioma patients is highly desired. Recently, various machine learning-based approaches have been developed to predict the prognosis based on preoperative magnetic resonance imaging (MRI) radiomics, which extract quantitative features from radiographic images. However, major challenges remain for methodologic developments to optimize feature extraction and provide rapid information flow in clinical settings. This study investigates two machine learning-based prognosis prediction tasks using radiomic features extracted from preoperative multimodal MRI brain data: (i) prediction of tumor grade (higher-grade vs. lower-grade gliomas) from preoperative MRI scans and (ii) prediction of patient overall survival (OS) in higher-grade gliomas (<12 months vs. > 12 months) from preoperative MRI scans. Specifically, these two tasks utilize the conventional machine learning-based models built with various classifiers. Moreover, feature selection methods are applied to increase model performance and decrease computational costs. In the experiments, models are evaluated in terms of their predictive performance and stability using a bootstrap approach. Experimental results show that classifier choice and feature selection technique plays a significant role in model performance and stability for both tasks; a variability analysis indicates that classification method choice is the most dominant source of performance variation for both tasks.
