Predicting surgical decision-making in vestibular schwannoma using tree-based machine learning.

Objective: Vestibular schwannomas (VSs) are the most common neoplasm of the cerebellopontine angle in adults. Though these lesions are generally slow growing, their growth patterns and associated symptoms can be unpredictable, which may complicate the decision to pursue conservative management versus active intervention. Additionally, surgical decision-making can be controversial because of limited high-quality evidence and multiple quality-of-life considerations. Machine learning (ML) is a powerful tool that utilizes data sets to essentialize multidimensional clinical processes. In this study, the authors trained multiple tree-based ML algorithms to predict the decision for active treatment versus MRI surveillance of VS in a single institutional cohort. In doing so, they sought to assess which preoperative variables carried the most weight in driving the decision for intervention and could be used to guide future surgical decision-making through an evidence-based approach.

Methods: The authors reviewed the records of patients who had undergone evaluation by neurosurgery and otolaryngology with subsequent active treatment (resection or radiation) for unilateral VS in the period from 2009 to 2021, as well as those of patients who had been evaluated for VS and were managed conservatively throughout 2021. Clinical presentation, radiographic data, and management plans were abstracted from each patient record from the time of first evaluation until the last follow-up or surgery. Each encounter with the patient was treated as an instance involving a management decision that depended on demographics, symptoms, and tumor profile. Decision tree and random forest classifiers were trained and tested to predict the decision for treatment versus imaging surveillance on the basis of unseen data using an 80/20 pseudorandom split. Predictor variables were tuned to maximize performance based on lowest Gini impurity indices. Model performance was optimized using fivefold cross-validation.

Results: One hundred twenty-four patients with 198 rendered decisions concerning management were included in the study. In the decision tree analysis, only a maximum tumor dimension threshold of 1.6 cm and progressive symptoms were required to predict the decision for treatment with 85% accuracy. Optimizing maximum dimension thresholds and including age at presentation boosted accuracy to 88%. Random forest analysis (n = 500 trees) predicted the decision for treatment with 80% accuracy. Factors with the highest variable importance based on multiple measures of importance, including mean minimal conditional depth and largest Gini impurity reduction, were maximum tumor dimension, age at presentation, Koos grade, and progressive symptoms at presentation.

Conclusions: Tree-based ML was used to predict which factors drive the decision for active treatment of VS with 80%-88% accuracy. The most important factors were maximum tumor dimension, age at presentation, Koos grade, and progressive symptoms. These results can assist in surgical decision-making and patient counseling. They also demonstrate the power of ML algorithms in extracting useful insights from limited data sets.