A deep learning radiomics analysis for identifying sinus invasion in patients with meningioma before operation using tumor and peritumoral regions.

Background: For patients with meningioma, surgical procedures are different because of the status of sinus invasion. However, there is still no suitable technique to identify the status of sinus invasion in patients with meningiomas. We aimed to build a deep learning radiomics model to identify sinus invasion before surgery.

Methods: A total of 1048 patients with meningiomas were retrospectively enrolled from two hospitals. T1 enhanced-weighted (T1c) and T2-weighted MRI data for each patient were collected. Tumors and their corresponding peritumors were analyzed. Four ResNet50 models were built with different types of regions of interest (ROIs) (tumor and peritumor) and different modal images (T1c and T2) to predict the status of sinus invasion. Several data enhancement methods were applied before ResNet50 model building. The final model was generated by combining four ResNet50 models.

Results: The models with a combination of tumors and peritumors using multimodal images achieved the highest predictive performance (AUC = 0.884, ACC = 78.1%) in the independent test cohort. The Delong test proved that the model built with combination ROIs achieved significantly higher performance than the model built only with tumors. The net reclassification improvement and integrated discrimination improvement tests both proved that including peritumor ROIs in the tumor ROIs could significantly improve the prediction ability.

Conclusion: In the current study, the deep learning model showed potential for identifying sinus invasion before surgery in patients with meningioma. Including peritumors could significantly improve predictive performance.