The application of machine learning for treatment selection of unruptured brain arteriovenous malformations: A secondary analysis of the ARUBA trial data.

Objective: To build a supervised machine learning (ML) model that selects the best first-line treatment strategy for unruptured bAVMs.

Methods: A Randomized Trial of Unruptured Brain Arteriovenous Malformations (ARUBA) trial data was obtained from the National Institute of Neurological Disorders and Stroke (NINDS). A team of five clinicians examined the demographic, clinical, and radiological details of each patient at baseline and reached a consensus on the best first-line treatment for bAVMs. Their treatment choice was used to train an automated supervised ML (autoML) model to select treatments for bAVMs for the training dataset. The accuracy and AUC of the algorithm in selecting the treatment strategy were measured for the test dataset, and feature importance scores of the included variables were calculated.

Results: Among the 100,000 combinations of supervised ML algorithms and their hyperparameters attempted by autoML, gradient boosting classifier had the best predictive performance with an overall accuracy of 0.74 and an area under the curve (AUC) of 0.88. The treatment-specific accuracies were 0.96, 0.85, 0.84, and 0.82; and AUCs were 0.75, 0.95, 0.80, and 0.88 for medical management, surgery, endovascular embolization, and gamma-knife radiosurgery, respectively. Spetzler-Martin score, followed by eloquent AVM location and AVM size, were the three most important features in determining treatments.

Conclusion: ML could reliably select the best first-line treatment strategy for bAVMs as per multidisciplinary expert consensus. This study can be replicated for larger population-based AVM registries, with the inclusion of outcome data, thus helping address the bias involved in the management of unruptured bAVMs.