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Objective: Preoperative magnetic resonance imaging (MRI) studies are routinely ordered for trigeminal neuralgia (TN), though with contested reliability in contemporary literature. A potential reason for this disagreement is inconsistency in MRI reading methodologies. Here, we compare the rate of reported neurovascular compression on preoperative MRI by radiologists employed in community or private practice settings and academic neuroradiologists., Methods: A retrospective review was conducted on patients who underwent endoscopic microvascular decompression for TN with intraoperatively visualized neurovascular compression and primary read by a non-academic or community radiologist. Patient imaging was then re-read by a board-certified neuroradiologist practicing in an academic setting, who was blinded to the initial read and the side of TN symptoms., Results: Non-academic radiologists reported vascular compression in 26.0% (20/77) of all patients, and mention was rarely made of the non-pathological side (sensitivity = 26.0%). On academic neuroradiologist re-reads, vascular compression was noted in 87.0% (67/77) of patients on the pathological side and in 57.1% (44/77) on the non-pathological side (sensitivity = 87.0%, specificity = 42.9%). Isotropic/near isotropic 3-dimensional steady state or heavily T2-weighted sequences were read with 92.3% sensitivity and 36.9% specificity, compared to 58.3% sensitivity and 66.7% specificity using routine T2 weighted sequences., Conclusions: The frequency of vascular compression reported by non-academic radiologists is much lower than what is reported by academic neuroradiologists reading the same MRI scans. These results highlight the effect of practice setting on the predictive power of neuroimaging. Future studies are indicated to further investigate these relationships, as well as to trial newer imaging modalities., (Copyright © 2022 Elsevier Inc. All rights reserved.)

This article, published in the Journal of Neurological Surgery, explores the use of a random forest algorithm to predict functional outcomes following microvascular decompression (MVD) for trigeminal neuralgia (TN). TN is a painful disorder caused by blood vessel compression, and MVD is considered the most effective treatment. The study reviewed 225 cases of TN and trained a random forest algorithm to predict postoperative pain outcomes. The algorithm demonstrated 88% accuracy in predicting average postoperative pain, with sleep and mood found to be important predictors. The study highlights the potential of machine learning in identifying meaningful associations for rare conditions like TN. [Extracted from the article]