A predictive model for survival outcomes of glioma patients based on multi-parametric,multi-regional MRI radiomics features and clinical features
10.12122/j.issn.1673-4254.2024.10.19
- VernacularTitle:多参数多区域MRI影像组学特征与临床信息联合模型可有效预测脑胶质瘤患者生存期
- Author:
Xiaoyin HUANG
1
;
Fenglian CHEN
;
Yu ZHANG
;
Shujun LIANG
Author Information
1. 南方医科大学生物医学工程学院,广东 广州 510515
- Keywords:
glioma;
radiomics;
magnetic resonance imaging;
machine learning;
prognostic analysis
- From:
Journal of Southern Medical University
2024;44(10):2004-2014
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish a predictive model for survival outcomes of glioma patients based on both brain radiomics features from preoperative MRI multi-sequence images and clinical features.Methods We retrospectively analyzed the MRI images and clinical data of 388 glioma patients and extracted the radiomics features from the peritumoral edema zone,tumor core,and whole tumor on T1,T2,and T1-weighted contrast-enhanced(T1CE)and fluid attenuated inversion recovery(FLAIR)sequences.The cases were divided into a training set(271 cases)and a test set(117 cases).Random survival forest algorithms were used to select the radiomics features associated with overall survival(OS)in the training set to construct a radiomic score(Rad-score),based on which the patients were classified into high-and low-risk groups for Kaplan-Meier survival analysis.Cox proportional hazard regression models for the 3 different tumor zones were constructed,and their performance for predicting 1-and 3-year survival rates was evaluated using 5-fold cross-validation and AUC analysis followed by external validation using data from another 10 glioma patients.The best-performing model was used for constructing a nomogram for survival predictions.Results Five radiomics features from the tumor core,7 from the peritumoral edema zone,and 5 from the whole tumor were selected.In both the training and test sets,the high-and low-risk groups had significantly different OS(P<0.05),and age,IDH status and Rad-score were independent factors affecting OS.The combined model showed better performance than the Rad-score model with AUCs for 1-year and 3-year survival prediction of 0.750 and 0.778 in the training set,0.764 and 0.800 in the test set,and 0.938 and 0.917 in external validation,respectively.Conclusion The predictive model combining preoperative multi-modal MRI radiomics features and clinical features can effectively predict survival outcomes of glioma patients.
