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.
Article
in En
| WPRIM
| ID: wpr-880671
Responsible library:
WPRO
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.
Key words
Full text:
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Index:
WPRIM
Main subject:
Brain Neoplasms
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Magnetic Resonance Imaging
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Logistic Models
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Retrospective Studies
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ROC Curve
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Glioma
Type of study:
Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Journal of Central South University(Medical Sciences)
Year:
2021
Type:
Article