Machine Learning and Artificial Intelligence based prediction of tumour dynamics in vestibular schwannoma integrating morphological, anatomical, and dosimetric insights

Purpose/Objective
Predicting tumour response following stereotactic radiosurgery (SRS) for vestibular schwannoma (VS) remains a challenge due to patient specific differences in tumour morphology and prescribed dose. Conventional statistical approaches fail to capture the complex and non-linear interactions between these features and long term volumetric outcomes. This study developed and evaluated machine learning (ML) and artificial intelligence (AI) models for predicting tumour size changes over 1-, 2-, and 3-year intervals after SRS, aiming to determine how anatomical, morphological, and dosimetric factors together influence this response.

Material/Methods
A longitudinal dataset of 148 patients with VS treated with SRS was analysed. Predictive models were trained using features such as laterality, intracanalicular involvement, NF2 status, cystic composition, mean intracanalicular tumour diameter (MITD), and marginal dose (11Gy, 12Gy, and 12.5Gy) on 70% of the cohort. Several supervised algorithms including Random Forest (RF), Gradient Boosting (GB), Support Vector Machines (SVM), and Deep Neural Networks (DNN) were compared against baseline regression models for predictive performance on the remaining 30% of the cohort. Model performance was evaluated using accuracy, precision, and area under the curve metrics. Explainable AI methods, including SHAP analysis, were used to interpret the influence of each predictor on volumetric outcomes.
Results
AI based models demonstrated superior predictive accuracy compared to traditional approaches. RF and GB emerged as the most reliable algorithms for integrating all factors. Both morphology and marginal dose were significant predictors of tumour size reduction. At 11Gy, tumours ≤1 cm³ showed an average reduction of 47%, while tumours >4cm³ exhibited minimal response. At 12Gy, tumours <4cm³ showed consistent decrease of 8-27%, while those >4 cm³ reduced by up to 77%. At 12.5Gy, shrinkage was most pronounced, ranging from 28% tumours <4 cm³ to 58% for large lesions. Cystic composition and NF2 status were dominant morphological predictors of non-linear tumour volume change, while intracanalicular involvement was associated with early stabilization. The inclusion of temporal MITD data improved the model’s ability to capture subtle early morphological changes preceding measurable regression.

Conclusion
Machine learning and artificial intelligence provide a robust and interpretable framework for suggesting tumour response after SRS. By integrating morphological, anatomical, and dosimetric data, the models offer a more specific, data driven understanding of how prescription and tumour morphology jointly affect response. This approach supports adaptive dose planning and individualized follow up strategies for patients with vestibular schwannoma.