Meningioma is the most common intracranial non-malignant tumor but is usually closely associated with the major venous sinuses. It has been recognized by neurosurgeons that meningioma should be treated with different surgical options depending on the status of sinus invasion. Therefore, it is necessary to accurately identify the venous sinus invasion status of meningioma patients before surgery; however, appropriate techniques are still lacking. Our study aimed to construct a deep learning model for accurate determination of sinus invasion before surgery.

In this study, we collected multi-modal imaging data and clinical information for a total of 1048 meningioma patients from two hospitals. ResNet-50 with a dual attention mechanism was used on the preprocessed T1c and T2WI data respectively, and the final model was generated by combining the two unimodal models. The classification performance was evaluated by the area under receiver operating characteristic (ROC) curve (AUC).

The results implied that the multimodal fusion classification model showed good performance in predicting meningioma sinus invasion. Further analysis on the patients with different WHO gradings indicated that our model has the best classification ability under WHO grading 1 in an independent validation cohort(0.84 AUC) . This study shows that deep learning is a reliable method for predicting sinus invasion in patients with meningioma before surgery.