Radiomics Analysis Based on Magnetic Resonance Imaging for Preoperative Overall Survival Prediction in Isocitrate Dehydrogenase Wild-Type Glioblastoma

Frontiers in Neuroscience

Neuroscience

Chao Yang¹  Chao Ma¹  Yong Huang²  Wenbo Sun³  Haibo Xu³  Huan Li³  Dan Xu³  Feng Xiao³  Lanqing Li³  Shouchao Wang³  Jun Chen⁴ 

[1] Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China;Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China;Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China;Precision Health Institute, GE Healthcare, Shanghai, China;

关键词: radiomics;    isocitrate dehydrogenase wildtype;    glioblastoma;    MRI;    nomogram;   

DOI  :  10.3389/fnins.2021.791776

 received in 2021-10-09, accepted in 2021-12-15,  发布年份 2022

来源: Frontiers

【 摘 要 】

PurposeThis study aimed to develop a radiomics signature for the preoperative prognosis prediction of isocitrate dehydrogenase (IDH)-wild-type glioblastoma (GBM) patients and to provide personalized assistance in the clinical decision-making for different patients.Materials and MethodsA total of 142 IDH-wild-type GBM patients classified using the new classification criteria of WHO 2021 from two centers were included in the study and randomly divided into a training set and a test set. Firstly, their clinical characteristics were screened using univariate Cox regression. Then, the radiomics features were extracted from the tumor and peritumoral edema areas on their contrast-enhanced T1-weighted image (CE-T1WI), T2-weighted image (T2WI), and T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) magnetic resonance imaging (MRI) images. Subsequently, inter- and intra-class correlation coefficient (ICC) analysis, Spearman’s correlation analysis, univariate Cox, and the least absolute shrinkage and selection operator (LASSO) Cox regression were used step by step for feature selection and the construction of a radiomics signature. The combined model was established by integrating the selected clinical factors. Kaplan–Meier analysis was performed for the validation of the discrimination ability of the model, and the C-index was used to evaluate consistency in the prediction. Finally, a Radiomics + Clinical nomogram was generated for personalized prognosis analysis and then validated using the calibration curve.ResultsAnalysis of the clinical characteristics resulted in the screening of four risk factors. The combination of ICC, Spearman’s correlation, and univariate and LASSO Cox resulted in the selection of eight radiomics features, which made up the radiomics signature. Both the radiomics and combined models can significantly stratify high- and low-risk patients (p < 0.001 and p < 0.05 for the training and test sets, respectively) and obtained good prediction consistency (C-index = 0.74–0.86). The calibration plots exhibited good agreement in both 1- and 2-year survival between the prediction of the model and the actual observation.ConclusionRadiomics is an independent preoperative non-invasive prognostic tool for patients who were newly classified as having IDH-wild-type GBM. The constructed nomogram, which combined radiomics features with clinical factors, can predict the overall survival (OS) of IDH-wild-type GBM patients and could be a new supplement to treatment guidelines.

【 授权许可】

Unknown   
Copyright © 2022 Wang, Xiao, Sun, Yang, Ma, Huang, Xu, Li, Chen, Li and Xu.