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Updated: Nov 20, 2025

A Unified Methodological Framework for Vestibular Schwannoma Research
Published on: June 20, 2017
Radiomics-Based Prediction of Long-Term Treatment Response of Vestibular Schwannomas Following Stereotactic
Patrick P J H Langenhuizen1,2, Svetlana Zinger2, Sieger Leenstra3
1Gamma Knife Center, Department of Neurosurgery, ETZ Hospital, Tilburg.
Objective:
Stereotactic radiosurgery (SRS) is one of the treatment modalities for vestibular schwannomas (VSs). However, tumor progression can still occur after treatment. Currently, it remains unknown how to predict long-term SRS treatment outcome. This study investigates possible magnetic resonance imaging (MRI)-based predictors of long-term tumor control following SRS.
Study Design:
Retrospective cohort study.
Setting:
Tertiary referral center.
Patients:
Analysis was performed on a database containing 735 patients with unilateral VS, treated with SRS between June 2002 and December 2014. Using strict volumetric criteria for long-term tumor control and tumor progression, a total of 85 patients were included for tumor texture analysis.
Intervention(S):
All patients underwent SRS and had at least 2 years of follow-up.
Main Outcome Measure(S):
Quantitative tumor texture features were extracted from conventional MRI scans. These features were supplied to a machine learning stage to train prediction models. Prediction accuracy, sensitivity, specificity, and area under the receiver operating curve (AUC) are evaluated.
Results:
Gray-level co-occurrence matrices, which capture statistics from specific MRI tumor texture features, obtained the best prediction scores: 0.77 accuracy, 0.71 sensitivity, 0.83 specificity, and 0.93 AUC. These prediction scores further improved to 0.83, 0.83, 0.82, and 0.99, respectively, for tumors larger than 5 cm.
Conclusions:
Results of this study show the feasibility of predicting the long-term SRS treatment response of VS tumors on an individual basis, using MRI-based tumor texture features. These results can be exploited for further research into creating a clinical decision support system, facilitating physicians, and patients to select a personalized optimal treatment strategy.
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