A New Methodology for Evaluation of Large Vestibular Aqueduct in CT and MRI Images.

HYPOTHESIS: Development of a new method for large vestibular aqueduct (LVA)/large endolymphatic sac anomaly (LESA) assessment using magnetic resonance imaging (MRI) and computed tomography (CT)/cone beam CT (CBCT) images. The secondary objective was to compare both modalities. BACKGROUND: The gold standard for LVA diagnosis is the analysis of CT images using Valvassori and Clemis or Cincinnati criteria. The previous studies showed inconclusive results regarding the correlation between audiological and radiological data. METHODS: Retrospective analysis of radiological images from 173 patients (315 ears), who were diagnosed with LVA/LESA based on CT/CBCT and/or MRI images of the temporal bone. The images obtained using both techniques were used to measure the following dimensions of vestibular aqueduct (VA)/endolymphatic duct (ED)/intraosseous endolymphatic sac (ES): width of the opening, length, and width at external aperture. In MRI images, the maximal contact diameters of the extraosseous or intraosseous ES and dura mater were measured as well. RESULTS: LVA has been reported to be bilateral in 82% (142 patients) and unilateral in 18% (31 patients) of cases. Comparison of MRI and CT/CBCT measurements showed a moderate correlation (0.64) in external aperture, a moderate correlation (0.57) in the width of the VA opening, and a weak correlation (0.34) in length measurements (p < 0.05). CONCLUSION: We developed a new method to identify the heterogeneous pathology of LVA/LESA using reconstruction along the VA/ED/intraosseous ES axis, three measurements on two planes, and focus on the maximal contact diameter between the extraosseous or intraosseous ES and dura mater.

A method to estimate probability of disease and vaccine efficacy from clinical trial immunogenicity data.

Vaccine efficacy is often assessed by counting disease cases in a clinical trial. A new quantitative framework proposed here ("PoDBAY," Probability of Disease Bayesian Analysis), estimates vaccine efficacy (and confidence interval) using immune response biomarker data collected shortly after vaccination. Given a biomarker associated with protection, PoDBAY describes the relationship between biomarker and probability of disease as a sigmoid probability of disease ("PoD") curve. The PoDBAY framework is illustrated using clinical trial simulations and with data for influenza, zoster, and dengue virus vaccines. The simulations demonstrate that PoDBAY efficacy estimation (which integrates the PoD and biomarker data), can be accurate and more precise than the standard (case-count) estimation, contributing to more sensitive and specific decisions than threshold-based correlate of protection or case-count-based methods. For all three vaccine examples, the PoD fit indicates a substantial association between the biomarkers and protection, and efficacy estimated by PoDBAY from relatively little immunogenicity data is predictive of the standard estimate of efficacy, demonstrating how PoDBAY can provide early assessments of vaccine efficacy. Methods like PoDBAY can help accelerate and economize vaccine development using an immunological predictor of protection. For example, in the current effort against the COVID-19 pandemic it might provide information to help prioritize (rank) candidates both earlier in a trial and earlier in development.