MRI- and DWI-Based Radiomics Features for Preoperatively Predicting Meningioma Sinus Invasion.
J Imaging Inform Med
; 37(3): 1054-1066, 2024 Jun.
Article
en En
| MEDLINE
| ID: mdl-38351221
ABSTRACT
The aim of this study was to use multimodal imaging (contrast-enhanced T1-weighted (T1C), T2-weighted (T2), and diffusion-weighted imaging (DWI)) to develop a radiomics model for preoperatively predicting venous sinus invasion in meningiomas. This prediction would assist in selecting the appropriate surgical approach and forecasting the prognosis of meningiomas. A retrospective analysis was conducted on 331 participants who had been pathologically diagnosed with meningiomas. For each participant, 3948 radiomics features were acquired from the T1C, T2, and DWI images. Minimum redundancy maximum correlation, rank sum test, and multi-factor recursive elimination were used to extract the most significant features of different models. Then, multivariate logistic regression was used to build classification models to predict meningioma venous sinus invasion. The diagnostic capabilities were assessed using receiver operating characteristic (ROC) analysis. In addition, a nomogram was constructed by incorporating clinical and radiological characteristics and a radiomics signature. To assess the clinical usefulness of the nomogram, a decision curve analysis (DCA) was performed. Tumor shape, boundary, and enhancement features were independent predictors of meningioma venous sinus invasion (p = 0.013, p = 0.013, p = 0.005, respectively). Eleven (T21, T1C4, DWI6) of the 3948 radiomics features were screened for strong association with meningioma sinus invasion. The areas under the ROC curves for the training and external test sets were 0.946 and 0.874, respectively. The clinicoradiomic model showed excellent predictive performance for invasive meningioma, which may help to guide surgical approaches and predict prognosis.
Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Imagen de Difusión por Resonancia Magnética
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Neoplasias Meníngeas
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Meningioma
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Invasividad Neoplásica
Tipo de estudio:
Prognostic_studies
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Risk_factors_studies
Idioma:
En
Revista:
J Imaging Inform Med
Año:
2024
Tipo del documento:
Article