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February 2, 2026

The Diagnostic Value of MRI-based-Radiomics before Surgery of Patients with Meningioma

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Authors

SNSaeed NasiriATAva TeymouriHSHussein Soleimantabar

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Overview

Cross-sectional study investigates MRI radiomics to improve meningioma diagnosis, suggesting enhanced accuracy for radiologists.

Key Points

  • This study aims to determine the diagnostic value of MRI-based radiomics in patients with meningioma before surgery.
  • Conducted a cross-sectional analysis of operated meningioma patients between 2018 and 2024.
  • Extracted radiomic features from MRI images with the help of an expert radiologist.
  • Analyzed images using various radiomic algorithms including SVM, Logistic Regression, and Random Forest.
  • LASSO test identified 10 non-zero correlated features from 851 total features.
  • Support Vector Machine (SVM) achieved an accuracy of 81.25% and precision of 0.85.
  • Logistic Regression showed an accuracy of 87.50% with precision of 0.89.
  • Random Forest also had an accuracy of 87.5% and precision of 0.89.
  • The AUC was 0.62 for SVM and 0.75 for both Logistic Regression and Random Forest.

Cite This Study

Nasiri et al. (2024) studied this question.

synapsesocial.com/papers/6980feeac1c9540dea811653https://doi.org/10.22037/orlfps.v10i1.47659
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