A logistic regression model for prediction of glioma grading based on radiomics.
10.11817/j.issn.1672-7347.2021.200074
- Author:
Xianting SUN
1
;
Weihua LIAO
2
;
Dong CAO
2
;
Yuelong ZHAO
3
;
Gaofeng ZHOU
2
;
Dongcui WANG
2
;
Yitao MAO
4
Author Information
1. Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008. sunxianting2009@163.com.
2. Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008.
3. School of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, China.
4. Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008. maoyt@csu.edu.cn.
- Publication Type:Journal Article
- Keywords:
glioma;
grading;
least absolute shrinkage and selection operator;
logistic regression;
radiomics
- MeSH:
Brain Neoplasms/diagnostic imaging*;
Glioma/diagnostic imaging*;
Humans;
Logistic Models;
Magnetic Resonance Imaging;
ROC Curve;
Retrospective Studies
- From:
Journal of Central South University(Medical Sciences)
2021;46(4):385-392
- CountryChina
- Language:English
-
Abstract:
OBJECTIVES:Glioma is the most common intracranial primary tumor in central nervous system. Glioma grading possesses important guiding significance for the selection of clinical treatment and follow-up plan, and the assessment of prognosis. This study aims to explore the feasibility of logistic regression model based on radiomics to predict glioma grading.
METHODS:Retrospective analysis was performed on 146 glioma patients with confirmed pathological diagnosis from January, 2012 to December, 2018. A total of 41 radiomics features were extracted from contrast-enhanced T
RESULTS:A total of 5 imaging features selected by LASSO were used to establish a logistic regression model for predicting glioma grading. The model showed good discrimination with AUC value of 0.919. Hosmer-Lemeshow test showed no significant difference between the calibration curve and the ideal curve (
CONCLUSIONS:The logistic regression model using radiomics exhibits a relatively high accuracy for predicting glioma grading, which may serve as a complementary tool for preoperative prediction of giloma grading.
