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

P.P.J.H. Langenhuizen (Corresponding author), Svitlana Zinger, Sieger Leenstra, H.P.M. Kunst, Jef J. S. Mulder, Patrick E.J. Hanssens, Peter H.N. de With, Jeroen B. Verheul

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

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 cm3.

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
Volume41
Issue number10
DOIs
Publication statusPublished - 1 Dec 2020

Keywords

  • vestibular schwannoma
  • stereotactic radiosurgery
  • long-term tumor control
  • machine learning
  • support vector machines
  • gray-level co-occurrence matrices

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