A comprehensive evaluation of non-vascular prepontine cistern anatomy influencing trigeminal nerve vulnerability using machine learning-based morphometric analysis.

Background: Trigeminal neuralgia (TN) is a severe neuropathic pain disorder traditionally attributed to neurovascular compression. However, emerging evidence suggests that non-vascular anatomical variations of the prepontine cistern may significantly contribute to disease susceptibility. Objective: To quantify non-vascular morphometric features of the trigeminal nerve and adjacent cistern and evaluate their discriminative value using a leakage-free, machine-learning-based MRI pipeline. Methods: We retrospectively analyzed 131 participants (71 with idiopathic TN (iTN) and 60 controls) who were imaged with temporal MRI. Two neuroradiologists independently assessed the neurovascular conflict status, achieving inter-rater agreement of 97% ( Results: TN showed thinner nerve diameters (particularly at the porus), larger Meckel cave areas (axial and coronal) and height, smaller sagittal angles, and shorter cisternal length; several of these differences remained significant after multiple-comparison control (e.g., porus diameters and Meckel cave areas, Holm-adjusted Conclusion: Non-vascular morphometric variation in the prepontine cistern, particularly at the porus level nerve caliber, Meckel cave size, and sagittal angle, contributes to TN pathophysiology. An AI-assisted, leakage-free morphometry pipeline yields reproducible and interpretable discrimination, supporting the integration of vascular and non-vascular anatomy into diagnostic and treatment planning workflows.