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 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 language | English |
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Pages (from-to) | E1321-E1327 |
Number of pages | 7 |
Journal | Otology & Neurotology |
Volume | 41 |
Issue number | 10 |
DOIs | |
Publication status | Published - Dec 2020 |
Keywords
- Machine learning
- Magnetic resonance imaging
- Radiomics
- Stereotactic radiosurgery
- Treatment prediction
- Tumor texture
- Vestibular schwannoma
- GAMMA-KNIFE RADIOSURGERY
- SURGERY
- MANAGEMENT
- FEATURES
- OUTCOMES
- MR
- RADIONECROSIS
- PROGRESSION
- RESECTION
- EFFICACY