Optimizing meningioma grading with radiomics and deep features integration, attention mechanisms, and reproducibility analysis.
Authors
Affiliations (15)
Affiliations (15)
- Ahl Al Bayt University, Kerbala, Iraq.
- Department of Chemical Engineering, Faculty of Engineering & Technology, Marwadi University Research Center, Marwadi University, Rajkot, Gujarat, 360003, India.
- Department of Computer Engineering and Application, GLA University Mathura, Mathura, 281406, India.
- Department of Chemistry and Biochemistry, School of Sciences, JAIN (Deemed to Be University), Bangalore, Karnataka, India.
- Chitkara Centre for Research and Development, Chitkara University, Baddi, Himachal Pradesh, 174103, India.
- Department of Chemistry, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India.
- Department of Maxillofacial Surgery, Samarkand State Medical University, 18 Amir Temur Street, 140100, Samarkand, Uzbekistan.
- College of Nursing, National University of Science and Technology, Dhi Qar, Iraq.
- Pharmacy College, Al-Farahidi University, Baghdad, Iraq.
- Department of Pharmacy, Al-Zahrawi University College, Karbala, Iraq.
- Gilgamesh Ahliya University, Baghdad, Iraq.
- Department of Radiology, Imam Reza Hospital, Birjand University of Medical Sciences, Birjand, Iran. [email protected].
- Department of Medical Physics and Radiology, Faculty of Paramedical Sciences, Kashan University of Medical Sciences, Kashan, Iran. [email protected].
- Dr. Schneiderhan GmbH and ISAR Klinikum Munich, Munich, Germany.
- Department of Health Care Management and Clinical Research, Collegium Humanum Warsaw Management University Warsaw, Warsaw, Poland.
Abstract
This study aims to develop a robust and clinically applicable framework for preoperative grading of meningiomas using T1-contrast-enhanced and T2-weighted MRI images. The approach integrates radiomic feature extraction, attention-guided deep learning models, and reproducibility assessment to achieve high diagnostic accuracy, model interpretability, and clinical reliability. We analyzed MRI scans from 2546 patients with histopathologically confirmed meningiomas (1560 low-grade, 986 high-grade). High-quality T1-contrast and T2-weighted images were preprocessed through harmonization, normalization, resizing, and augmentation. Tumor segmentation was performed using ITK-SNAP, and inter-rater reliability of radiomic features was evaluated using the intraclass correlation coefficient (ICC). Radiomic features were extracted via the SERA software, while deep features were derived from pre-trained models (ResNet50 and EfficientNet-B0), with attention mechanisms enhancing focus on tumor-relevant regions. Feature fusion and dimensionality reduction were conducted using PCA and LASSO. Ensemble models employing Random Forest, XGBoost, and LightGBM were implemented to optimize classification performance using both radiomic and deep features. Reproducibility analysis showed that 52% of radiomic features demonstrated excellent reliability (ICC > 0.90). Deep features from EfficientNet-B0 outperformed ResNet50, achieving AUCs of 94.12% (T1) and 93.17% (T2). Hybrid models combining radiomic and deep features further improved performance, with XGBoost reaching AUCs of 95.19% (T2) and 96.87% (T1). Ensemble models incorporating both deep architectures achieved the highest classification performance, with AUCs of 96.12% (T2) and 96.80% (T1), demonstrating superior robustness and accuracy. This work introduces a comprehensive and clinically meaningful AI framework that significantly enhances the preoperative grading of meningiomas. The model's high accuracy, interpretability, and reproducibility support its potential to inform surgical planning, reduce reliance on invasive diagnostics, and facilitate more personalized therapeutic decision-making in routine neuro-oncology practice. Not applicable.