Radiomics-Based Prediction of Long-Term Treatment Response of Vestibular Schwannomas Following Stereotactic Radiosurgery

Patrick P. J. H. Langenhuizen*, Svetlana Zinger, Sieger Leenstra, Henricus P. M. Kunst, Jef J. S. Mulder, Patrick E. J. Hanssens, Peter H. N. de With, Jeroen B. Verheul

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


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(3). 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.

Original languageEnglish
Pages (from-to)E1321-E1327
Number of pages7
JournalOtology & Neurotology
Issue number10
Publication statusPublished - Dec 2020


  • Machine learning
  • Magnetic resonance imaging
  • Radiomics
  • Stereotactic radiosurgery
  • Treatment prediction
  • Tumor texture
  • Vestibular schwannoma
  • MR

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