Prediction of progesterone receptor expression in high-grade meningioma by using radiomics based on enhanced T1WI.
Clin Radiol
; 78(10): e752-e757, 2023 10.
Article
en En
| MEDLINE
| ID: mdl-37487839
ABSTRACT
AIM:
To predict progesterone receptor (PR) expression of high-grade meningioma using radiomics based on enhanced T1-weighted imaging (WI). MATERIALS ANDMETHODS:
There were 157 cases of high-grade meningioma in the study. Seventy-eight cases had negative expression and 79 cases had positive expression. Spearman's rank correlation coefficient and least absolute shrinkage and selection operator (LASSO) regression were used to select the valuable features. The models were developed by naive Bayes (NB), random forest (RF), and support vector machine (SVM). Receiver operating characteristic (ROC) and decision curve analysis (DCA) analysis were used to assess the models.RESULTS:
Nine features were selected as the valuable features using Spearman's analysis and LASSO regression. The RF and NB models achieved the same area under the ROC curve (AUC) of 0.75, which was higher than that of SVM (0.74). There was no significant difference among the AUCs of the three models (p>0.05). There was a larger net benefit in the RF model than the SVM and NB models across all threshold probabilities in the DCA analysis.CONCLUSION:
The RF model had good performance in predicting PR expression of high-grade meningioma. PR expression evaluation for high-grade meningioma would be helpful in clinical practice.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Neoplasias Meníngeas
/
Meningioma
Tipo de estudio:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Clin Radiol
Año:
2023
Tipo del documento:
Article
País de afiliación:
China