Regression-Based Analysis of Vestibular Laboratory Tests for the Prediction of Unilateral Vestibular Schwannoma

The diagnostic work-up for vestibular pathologies involves a battery of tests designed to quantify the functioning of the otolith organs and semicircular canals. Clinical data from video head-impulse tests, vestibular-evoked myogenic potentials, subjective visual verticality, and caloric tests are usually collected. Our study applied regression analyses to predict the affected side of a patient group with vestibular schwannoma, learning from laboratory vestibular tests, to assess their relative predictive capacity in predicting the tumor side. Technology or Method: The dataset was pre-processed to handle missing values, outliers, and differences in the measurement scales. The mean asymmetry values and their direction (either negative = left-side asymmetry or positive = right-side asymmetry) were calculated. The classifiers' ability to accurately predict the tumor side was evaluated. Finally, both logistic and multiple regression analyses were conducted.

The regression models' binary output (i.e., right or left side affected) was compared to the true labels of the affected side given by magnetic resonance imaging to estimate the model's accuracy. Linear regression analysis showed that caloric, cVEMP and RLLL reached AUCs >0.9; multiple regression revealed an AUC of 0.96 for caloric and cVEMP combined.

Our study demonstrated that combining caloric and vestibular-evoked myogenic potential tests provides the most accurate identification of the vestibular schwannoma-affected side, achieving the highest predictive capacity. Furthermore, our findings align with previous studies revealing that the monocular video head-impulse test introduces a gain bias for all three semicircular canals that must be adjusted to correctly estimate semicircular canal function. Clinical and Impact-This study addresses the clinical challenge of finding the affected side in unilateral vestibular schwannoma patients by using machine learning to vestibular tests linking computational methods with clinical practice.