Prediction of Vestibular Schwannoma Enlargement After Radiosurgery Using Tumor Shape and MRI Texture Features

OBJECTIVE: Determine if vestibular schwannoma (VS) shape and MRI texture features predict significant enlargement after stereotactic radiosurgery (SRS). STUDY DESIGN: Retrospective case review. SETTING: Tertiary referral center. PATIENTS: Fifty-three patients were selected who underwent SRS and had a contrast-enhanced T1 sequence planning MRI scan and a follow-up contrast enhanced T1 MRI available for review. Median follow-up of 6.5 months (interquartile range/IQR, 5.9-7.4). Median pretreatment tumor volume was 1,006 mm3 (IQR, 465-1,794). INTERVENTIONS: Stereotactic radiosurgery. MAIN OUTCOME MEASURES: Texture and shape features from the SRS planning scans were extracted and used to train a linear support vector machine binary classifier to predict post-SRS enlargement >20% of the pretreatment volume. Sensitivity, specificity, area under the receiver-operating-characteristic curve (AUC), and positive likelihood ratio were computed. A stratified analysis based on pretreatment tumor volume greater or less than the median volume was also performed. RESULTS: The model had a sensitivity of 92%, specificity of 65%, AUC of 0.75, and a positive likelihood ratio of 2.6 (95% CI 1.4-5.0) for predicting post-SRS enlargement of >20%. In the larger tumor subgroup, the model had a sensitivity of 87%, specificity of 73%, AUC of 0.76, and a positive likelihood ratio of 3.2 (95% CI 1.2-8.5). In the smaller tumor subgroup, the model had a sensitivity of 95%, specificity of 50%, AUC of 0.65, and a positive likelihood ratio of 1.9 (95% CI 0.8-4.3). CONCLUSIONS: VS shape and texture features may be useful inputs for machine learning models that predict VS enlargement after SRS.