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Artigo em Inglês | WPRIM (Pacífico Ocidental) | ID: wpr-880671

RESUMO

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.


Assuntos
Humanos , Neoplasias Encefálicas/diagnóstico por imagem , Glioma/diagnóstico por imagem , Modelos Logísticos , Imageamento por Ressonância Magnética , Curva ROC , Estudos Retrospectivos
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