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.