Dual-level image and feature augmentation approach for improving radiomics performance in multisequence MRI meningioma grading

Motivation: Prediction of high-grade meningioma on preoperative MRI is essential in therapeutic planning and evaluation of prognosis. Goal(s): We seek to propose a data augmentation strategy to reduce class imbalance for model improvement. Approach: In this study, we propose a dual-level augmentation strategy incorporating image-level augmentation and feature-level augmentation to tackle class-imbalance and improve the predictive performance of radiomics for meningioma grading on multisequence MRI. Results: The radiomics models yields robust performance in 100 repetitions in 3-, 5-, and 10-fold cross-validation. In addition, our method significantly outperformed single-level augmentation (image or feature) or no augmentation in each cross-validation. Impact: As an effective and robust meningioma grading tool, our radiomics model has the potential to aid clinical decision making for a broader range of meningioma grades seen in practice, allowing for better radiomics-based pre-operative stratification and individualized patient management.