A logistic regression model for prediction of glioma grading based on radiomics / 中南大学学报(医学版)
Journal of Central South University(Medical Sciences)
;
(12): 385-392, 2021.
Artigo
em Inglês
| WPRIM
| ID: wpr-880671
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.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Neoplasias Encefálicas
/
Imageamento por Ressonância Magnética
/
Modelos Logísticos
/
Estudos Retrospectivos
/
Curva ROC
/
Glioma
Tipo de estudo:
Estudo observacional
/
Estudo prognóstico
/
Fatores de risco
Limite:
Humanos
Idioma:
Inglês
Revista:
Journal of Central South University(Medical Sciences)
Ano de publicação:
2021
Tipo de documento:
Artigo
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