Machine learning prediction of meningioma grade using preoperative MRI: a large multicenter study.
Authors
Affiliations (8)
Affiliations (8)
- 1Department of Neurosurgery, Duke University, Durham.
- 2Department of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina.
- 3Department of Neurosurgery, Johns Hopkins University, Baltimore, Maryland.
- 4Department of Computer Science, Duke University, Durham, North Carolina.
- 5Department of Mathematics, Computer Science, and Statistics, Ursinus College, Collegeville, Pennsylvania.
- 6Department of Radiology, University of California, San Francisco, California.
- 7Department of Biomedical Engineering, Duke University, Durham; and.
- 8Department of Radiology, Duke University, Durham, North Carolina.
Abstract
Preoperative knowledge of intracranial meningioma grade could significantly enhance surgical planning, but reliable noninvasive prediction remains an unmet clinical need. The aim of this study was to develop and externally validate a machine learning (ML) model incorporating traditional radiomic features, along with novel fractal and topological features, to predict WHO meningioma grade from preoperative MRI using a large multicenter cohort. This retrospective diagnostic study analyzed a cohort of patients in the BraTS Meningioma Challenge. Patient data (n = 909) from 6 academic medical centers were used for model training and internal validation (n = 699) and one-time final external validation (n = 210). Tumors were segmented into three regions and radiomic features extracted across four MRI sequences. Eight different ML algorithms (e.g., based on neural networks, decision trees, clustering, and linear relationships) were tested across a total of 2889 candidate features. The primary outcome was the area under the receiver operating characteristic curve (AUROC) for prediction of binarized WHO grade (1 vs 2 or 3). The final model used a support vector machine algorithm comprising 20 features and achieved an AUROC of 0.824 (95% CI 0.755-0.883) on unseen test data, with favorable calibration. Feature importance analysis revealed that topological complexity added complementary predictive value to conventional radiomics features. The final model is publicly available for anyone to use. This externally validated ML model accurately predicted meningioma grade using routine preoperative MRI, with the potential to guide surgical decision-making and serve as a basis for future imaging-based brain tumor classifications.