A logistic regression model for prediction of glioma grading based on radiomics. / åºäºå½±åç»å¦çlogisticåå½æ¨¡åé¢æµè¶è´¨ç¤å级.
Zhong Nan Da Xue Xue Bao Yi Xue Ban
; 46(4): 385-392, 2021 Apr 28.
Article
em En, Zh
| MEDLINE
| ID: mdl-33967085
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 T1-weighted imaging (T1WI+C) lesion by manual segmentation. Least absolute shrinkage and selection operator (LASSO) was used to select the most-predictive radiomics features for pathological grading and to calculate radiomics score (Rad-score) of each patient. A logistic regression model was built to explore the correlation between giloma grading and Rad-score. Receiver operating characteristic (ROC) curve was performed to evaluate the model's predictive ability with area under the curve (AUC) for the evaluation index. Hosmer-Lemeshow test was used to measure the model's predictive accuracy.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 (P=0.808), indicating high predictive accuracy of the model.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.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias Encefálicas
/
Glioma
Tipo de estudo:
Guideline
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
/
Zh
Revista:
Zhong Nan Da Xue Xue Bao Yi Xue Ban
Assunto da revista:
MEDICINA
Ano de publicação:
2021
Tipo de documento:
Article