A logistic regression model for prediction of glioma grading based on radiomics.
10.11817/j.issn.1672-7347.2021.200074
- Author:
Xianting SUN
1
,
2
,
3
;
Weihua LIAO
1
;
Dong CAO
1
;
Yuelong ZHAO
4
;
Gaofeng ZHOU
1
;
Dongcui WANG
1
;
Yitao MAO
1
,
5
Author Information
1. Department of Radiology, Xiangya Hospital, Central South University, Changsha
2. sunxianting2009@
3. com.
4. School of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, China.
5. 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.
