Related Experiment Video
Updated: Jun 19, 2026
A Unified Methodological Framework for Vestibular Schwannoma Research
Published on: June 20, 2017
Therapeutic Hybrid Intelligence with Neural and Knowledge-based Expert Reasoning for SRS (THINKERS): a
Jheremy S Reyes1,2, Constantinos G Hadjipanayis1,2, Ajay Niranjan3,4
1Center for Image-Guided Neurosurgery, Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
Background:
Outcome prediction after Gamma Knife radiosurgery (GKRS) for vestibular schwannoma remains largely guided by tumor size, Koos grade, baseline symptoms, cochlear dose constraints, and institutional experience rather than individualized estimates of tumor progression and functional outcomes. We developed THINKERS-VS, a mixture-of-experts (MoE) artificial intelligence framework for progression and symptom-transition prediction after GKRS.
Methods:
We performed a retrospective single-center study of vestibular schwannomas treated with GKRS. Variables available before the treatment were used to train a MoE neural network with discrete-time survival modeling. The model incorporated demographic, clinical, tumor, and radiosurgical variables. The primary endpoint was tumor progression across intervals of 3, 6, 12, 18, 24, 32, 48, and 60 months. Secondary endpoints included new or worsened hearing loss, imbalance, vertigo, dizziness, and tinnitus. Internal validation used grouped 5-fold cross-validation and a grouped holdout test split. Performance was assessed using AUC and Brier score.
Results:
The cohort included 686 patients. Median age was 59.5 years, median tumor volume was 0.746 cm³, median prescription dose was 12.0 Gy, and median follow-up was 52.3 months. THINKERS-VS achieved strong progression discrimination across evaluated intervals, with AUCs ranging from 0.807 to 0.859. At 60 months, AUC was 0.807 and Brier score was 0.012. For symptom-transition prediction, holdout AUCs were 0.875 for hearing loss, 0.844 for imbalance, 0.813 for vertigo, 0.884 for dizziness, and 0.839 for tinnitus.
Conclusions:
THINKERS-VS provides an internally validated framework for individualized tumor progression and symptom-transition prediction after GKRS for vestibular schwannoma and recommends tumor margin dose associated with best outcome.
Clinical Trial Number:
Not applicable.
Insights
A new AI framework, THINKERS-VS, accurately predicts tumor progression and symptom changes after Gamma Knife radiosurgery for vestibular schwannoma, enabling personalized treatment recommendations.
Area of Science:
- Neurosurgery
- Artificial Intelligence
- Oncology
Background:
- Vestibular schwannoma treatment outcomes are typically predicted by tumor size and clinical factors.
- Individualized prediction of tumor progression and functional outcomes after Gamma Knife radiosurgery (GKRS) is lacking.
- THINKERS-VS, a novel AI framework, was developed for enhanced outcome prediction.
Purpose of the Study:
- To develop and validate an AI framework for predicting tumor progression after GKRS for vestibular schwannoma.
- To predict the likelihood of new or worsened symptoms, including hearing loss, imbalance, vertigo, dizziness, and tinnitus.
- To recommend optimal tumor margin doses for improved patient outcomes.
Main Methods:
- A retrospective study of 686 vestibular schwannoma patients treated with GKRS.
- A mixture-of-experts (MoE) neural network incorporating demographic, clinical, tumor, and radiosurgical data.
- Discrete-time survival modeling was used for progression prediction, with secondary endpoints for symptom transitions. Internal validation included cross-validation and a holdout test set.
Main Results:
- THINKERS-VS demonstrated strong performance in predicting tumor progression, with Area Under the Curve (AUC) values ranging from 0.807 to 0.859.
- At 60 months, the AUC for progression was 0.807, with a Brier score of 0.012.
- High AUCs were achieved for symptom-transition prediction: 0.875 for hearing loss, 0.844 for imbalance, 0.813 for vertigo, 0.884 for dizziness, and 0.839 for tinnitus.
Conclusions:
- THINKERS-VS offers an internally validated AI framework for personalized prediction of tumor progression and symptom transitions post-GKRS.
- The AI framework can guide treatment decisions by recommending tumor margin doses associated with the best patient outcomes.
- This approach moves beyond traditional prediction methods, offering more individualized care for vestibular schwannoma patients.