Deep learning and radiomics-based approach to meningioma grading: exploring the potential value of peritumoral edema regions

Abstract

Background:
Radiomics and deep learning techniques have become integral in meningioma grading. The combination of these approaches holds the potential to enhance classification accuracy. Given the frequent occurrence of peritumoral edema (PTE) in meningiomas, investigating the potential value of PTE requires further research.
Objectives:
To address the challenge of meningioma grading, this study introduces a unique approach that integrates radiomics and deep learning techniques. The primary focus is on the development of a Transfer Learning-based Meningioma Feature Extraction Model (MFEM), leveraging both Vision Transformer (ViT) and Convolutional Neural Network (CNN) architectures. Furthermore, the study explores the potential significance of the peritumoral edema (PTE) region in enhancing the grading process.
Materials and Methods:
A retrospective study was conducted involving 98 meningioma patients, with 60 classified as low-grade meningiomas and 38 as high-grade meningiomas. PTE was observed in 51.02% of low-grade meningiomas patients and 89.47% of high-grade meningiomas patients. Magnetic resonance images were acquired using a GE Signa HDxt 1.5T MRI scanner, incorporating T2-weighted Fluid-Attenuated Inversion Recovery (T2 Flair) sequences. A Transfer Learning-based Meningioma Feature Extraction Model (MFEM) was constructed by combining ViT and CNN models to extract deep features from the Transformer layer, alongside radiomics features. These were then utilized as input for a machine learning classifier to accurately grade meningiomas.
Results:
The proposed method demonstrated excellent grading accuracy and robustness on the meningioma dataset, offering valuable guidance for treatment decisions. The approach achieved 92.86% accuracy, 93.44% precision, 95% sensitivity, and 89.47% specificity.
Conclusion:
The radiomics and deep learning-based approach presented in this study offers a reliable method for preoperative meningioma grading. This innovative method not only enhances accuracy but also reduces observer subjectivity, thereby contributing to improved clinical decision-making.