Intelligent meningioma grading based on medical features.

Background: Meningiomas are the most common primary intracranial tumors in adults. Low-grade meningiomas have a low recurrence rate, whereas high-grade meningiomas are highly aggressive and recurrent. Therefore, the pathological grading information is crucial for treatment, as well as follow-up and prognostic guidance. Most previous studies have used radiomics or deep learning methods to extract feature information for grading meningiomas. However, some radiomics features are pixel-level features that can be influenced by factors such as image resolution and sharpness. Additionally, deep learning models that perform grading directly from MRI images often rely on image features that are ambiguous and uncontrollable, which reduces the reliability of the results to a certain extent.

Purpose: We aim to validate that combining medical features with deep neural networks can effectively improve the accuracy and reliability of meningioma grading.

Methods: We construct a SNN-Tran model for grading meningiomas by analyzing medical features including tumor volume, peritumoral edema volume, dural tail sign, tumor location, the ratio of peritumoral edema volume to tumor volume, age and gender. This method is able to better capture the complex relationships and interactions in the medical features and enhance the reliability of the prediction results.

Results: Our model achieve an accuracy of 0.875, sensitivity of 0.886, specificity of 0.847, and AUC of 0.872. And the method is superior to the deep learning, radiomics and SOTA methods.

Conclusion: We demonstrate that combining medical features with SNN-Tran can effectively improve the accuracy and reliability of meningioma grading. The SNN-Tran model excel in capturing long-range dependencies in the medical feature sequence.