A Deep Neural Network-Based Model Predicting Peritumoral Edema After Radiosurgery for Meningioma

Abstract

Background: Gamma Knife radiosurgery (GKS) is a promising treatment option for meningioma. However, the incidence of peritumoral edema (PTE) following GKS has been reported to be 7%-38%. This study aimed to develop a predictive model for post-GKS PTE using a deep neural network (DNN) algorithm.

Methods: Patients treated with GKS for meningioma between November 2012 and February 2020 at a single tertiary center were reviewed. The primary outcome was newly developed or aggravated PTE after GKS. Clinical data, including radiosurgical parameters, were collected, and imaging data obtained at the time of GKS were incorporated into the model using a 50-layered residual neural network, ResNet50. Consequently, the model efficiency was evaluated considering the accuracy and area under the receiver operating curve (AUC) values.

Results: A total of 202 patients were included in this study. The median tumor volume was 2.3 mL, and the median prescription dose was 13 Gy. PTE was observed before GKS in 22 patients. Post-GKS PTE was evident in 28 patients (13.9%), which further evolved to radiation necrosis in 5 patients. The accuracy and AUC values of the hybrid data model based on both clinical and imaging data were 0.725 and 0.701, respectively. The performance of the hybrid data model was superior to that of the other models based on clinical or image data only.

Conclusions: The DNN-based model using both clinical and imaging data exhibited fair results in predicting post-GKS PTE in meningioma treatment. Predictive models using imaging data may be helpful in prognostic research.

Keywords: Deep neural network; Gamma Knife; Meningioma; Peritumoral edema; Predictive model; Radiosurgery.