Assessment of gamma knife radiosurgery for unruptured cerebral arterioveneus malformations based on multi-parameter radiomics of MRI

Abstract

Objective: The treatment of Gamma knife radiosurgery (GKS) for unruptured Arteriovenous Malformations (AVM) remains controversial. A safe, effective and non-invasive method to predict outcome seems attractive for GKS. The purpose of this study was to develop and validate a MRI based multi-parameter radiomics model predicting the outcome of GKS for unruptured AVM.

Methods: Eighty-eight unruptured AVM patients who initial underwent GKS between January 2011 and December 2016 in our hospital were included in this retrospective study. Patients were divided into two groups named as favourable and unfavourable outcome, according to the clinical outcome. Favourable outcome was defined as obliteration without post-SRS hemorrhage or permanent radiation-induced changes (RIC). Multivariate logistic regression analysis was used to select appropriate clinical features and construct a clinical predicting model. In terms of radiomic model, manually segmentation and radiomics extracted were performed on each AVM lesions. Finally, 1684 radiomics features were extracted and Recursive Feature Elimination (RFE) method combined with Random forest classifier were used for feature selection and model construction. The performance of the radiomics model was evaluated by the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In addition, the favourable group was further divided into early and late respond subgroup according to the time of obliteration evaluated by 2 years. The selected features were further compared according the respond time.

Results: The median duration of neuroimaging follow-up was 65 months, 56 patients showed favourable outcome and 17 patients were observed obliteration within 2 years. The radiomics model constructed by 12 selected features achieved significant higher AUC of 0.88 (95% confidence interval 0.87-0.90) than traditional scoring system for predicting AVM outcome. Two selected radiomics features named "Dependence Variance" and "firstorder-Skewness" were found significant difference between the patients with early or late-respond.

Conclusions: The results suggest that the radiomics features could be successfully used for the pretreatment prediction of outcome for GKS in unruptured AVMs, which is helpful for decision-making process on unruptured AVM patients.

Keywords: Arteriovenous malformation; Gamma knife radiosurgery; Machine learning; Radiomic; Unruptured.