A Comparative and Summative Study of Radiomics-based Overall Survival Prediction in Glioblastoma Patients. Issue 3 (8th May 2022)

• Record Type: Journal Article
• Authors: Ruan, Zhuoying; Mei, Nan; Lu, Yiping; Xiong, Ji; Li, Xuanxuan; Zheng, Weiwei; Liu, Li; Yin, Bo
• Is Part Of: Journal of computer assisted tomography. Volume 46:Issue 3(2022)
• Page Start: 470
• Page End: 479
• Publication Date: 2022-05-08
• Subjects: machine learning -- glioblastoma -- magnetic resonance imaging -- Cox proportional hazard regression -- random survival forest; Tomography -- Periodicals; Tomography -- Periodicals; Tomography; Periodicals; 616.0757
• DOI: 10.1097/RCT.0000000000001300
• Languages: English
• ISSNs: 0363-8715

Abstract

Abstract : Purpose: This study aimed to assess different machine learning models based on radiomic features, Visually Accessible Rembrandt Images features and clinical characteristics in overall survival prediction of glioblastoma and to identify the reproducible features. Materials and Methods: Patients with preoperative magnetic resonance scans were allocated into 3 data sets. The Least Absolute Shrinkage and Selection Operator was used for feature selection. The prediction models were built by random survival forest (RSF) and Cox regression. C-index and integrated Brier scores were calculated to compare model performances. Results: Patients with cortical involvement had shorter survival times in the training set ( P = 0.006). Random survival forest showed higher C-index than Cox, and the RSF model based on the radiomic features was the best one (testing set: C-index = 0.935 ± 0.023). Ten reproducible radiomic features were summarized. Conclusions: The RSF model based on radiomic features had promising potential in predicting overall survival of glioblastoma. Ten reproducible features were identified.