Radiomics-Based Prediction of Long-Term Treatment Response of Vestibular Schwannomas Following Stereotactic Radiosurgery.
2020
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.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
41
References
1
Citations
NaN
KQI