Reproducible meningioma grading across multi-center MRI protocols via hybrid radiomic and deep learning features.
Objective: This study aimed to create a reliable method for preoperative grading of meningiomas by combining radiomic features and deep learning-based features extracted using a 3D autoencoder. The goal was to utilize the strengths of both handcrafted radiomic features and deep learning features to...
| Publicado en: | Neuroradiology Vol. 67; no. 10; pp. 2741 - 2762 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189357971&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189357971 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Oct2025 vid: 67 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 189357971 187378567 189357971 189357971 10.1007/s00234-025-03725-8 189357971 ppf: 2741 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Reproducible meningioma grading across multi-center MRI protocols via hybrid radiomic and deep learning features. aug: au: Saadh, Mohamed J. Albadr, Rafid Jihad Sur, Dharmesh Yadav, Anupam Roopashree, R. Sangwan, Gargi Krithiga, T. Aminov, Zafar Taher, Waam Mohammed Alwan, Mariem Jawad, Mahmood Jasem Al-Nuaimi, Ali M. Ali Farhood, Bagher affil: https://ror.org/059bgad73 Faculty of Pharmacy, Middle East University, 11831, Amman, Jordan sug: subj: Preoperative Period Meningioma Surgery Neoplasm Grading Methods Magnetic Resonance Imaging Image Processing, Computer Assisted Protocols Radiomics Deep Learning Imaging, Three-Dimensional Autoencoder Sensitivity and Specificity Evaluation Human Adult Middle Age Aged Reproducibility of Results Cancer Patients Surgical Patients Factor Analysis Analysis of Variance Boosting Machine Learning Algorithms Intraclass Correlation Coefficient ROC Curve Descriptive Statistics Retrospective Design Multicenter Studies Record Review Male Female Data Analysis Software Image Interpretation, Computer Assisted Meningioma Pathology Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Objective: This study aimed to create a reliable method for preoperative grading of meningiomas by combining radiomic features and deep learning-based features extracted using a 3D autoencoder. The goal was to utilize the strengths of both handcrafted radiomic features and deep learning features to improve accuracy and reproducibility across different MRI protocols. Materials and methods: The study included 3,523 patients with histologically confirmed meningiomas, consisting of 1,900 low-grade (Grade I) and 1,623 high-grade (Grades II and III) cases. Radiomic features were extracted from T1-contrast-enhanced and T2-weighted MRI scans using the Standardized Environment for Radiomics Analysis (SERA). Deep learning features were obtained from the bottleneck layer of a 3D autoencoder integrated with attention mechanisms. Feature selection was performed using Principal Component Analysis (PCA) and Analysis of Variance (ANOVA). Classification was done using machine learning models like XGBoost, CatBoost, and stacking ensembles. Reproducibility was evaluated using the Intraclass Correlation Coefficient (ICC), and batch effects were harmonized with the ComBat method. Performance was assessed based on accuracy, sensitivity, and the area under the receiver operating characteristic curve (AUC). Results: For T1-contrast-enhanced images, combining radiomic and deep learning features provided the highest AUC of 95.85% and accuracy of 95.18%, outperforming models using either feature type alone. T2-weighted images showed slightly lower performance, with the best AUC of 94.12% and accuracy of 93.14%. Deep learning features performed better than radiomic features alone, demonstrating their strength in capturing complex spatial patterns. The end-to-end 3D autoencoder with T1-contrast images achieved an AUC of 92.15%, accuracy of 91.14%, and sensitivity of 92.48%, surpassing T2-weighted imaging models. Reproducibility analysis showed high reliability (ICC > 0.75) for 127 out of 215 features, ensuring consistent performance across multi-center datasets. Conclusions: The proposed framework effectively integrates radiomic and deep learning features to provide a robust, non-invasive, and reproducible approach for meningioma grading. Future research should validate this framework in real-world clinical settings and explore adding clinical parameters to enhance its prognostic value. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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