Ocular neuromyotonia caused by a recurrent sphenoidal ridge meningioma.

BACKGROUND: Ocular neuromyotonia (ONM) is a rare ocular motility disorder characterized by involuntary paroxysmal extraocular muscle contraction and is caused by radiation therapy, vascular compression, and inflammatory disease. This study includes a rare case of ONM caused by a recurrent meningioma. CASE DESCRIPTION: A 56-year-old man presented with diplopia due to the right oculomotor nerve palsy caused by a sphenoidal atypical meningioma, with improved symptoms after initial surgery. During the next 7 years, he underwent local radiation therapy, second surgery, and Gamma Knife radiosurgery to control the tumor's repetitive recurrence around the right anterior clinoid process. After these treatments, residual tumor was controlled for the next 3 years. However, 3 months after his last visit, he started to suffer from the right ONM and visual disturbance. The magnetic resonance imaging results revealed a rapid growth of the posterior part of the residual tumor, involving the right oculomotor nerve. The third tumor resection was performed to prevent further aggravation of the symptoms. Decompression of the right oculomotor nerve was achieved, and ONM disappeared immediately after surgery. CONCLUSION: If nerve compression by the tumor is clearly indicated with the neuroradiological assessment, surgical intervention is the treatment of choice to improve ONM.

XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury.

The accurate prediction of neurological outcomes in patients with cervical spinal cord injury (SCI) is difficult because of heterogeneity in patient characteristics, treatment strategies, and radiographic findings. Although machine learning algorithms may increase the accuracy of outcome predictions in various fields, limited information is available on their efficacy in the management of SCI. We analyzed data from 165 patients with cervical SCI, and extracted important factors for predicting prognoses. Extreme gradient boosting (XGBoost) as a machine learning model was applied to assess the reliability of a machine learning algorithm to predict neurological outcomes compared with that of conventional methodology, such as a logistic regression or decision tree. We used regularly obtainable data as predictors, such as demographics, magnetic resonance variables, and treatment strategies. Predictive tools, including XGBoost, a logistic regression, and a decision tree, were applied to predict neurological improvements in the functional motor status (ASIA [American Spinal Injury Association] Impairment Scale [AIS] D and E) 6 months after injury. We evaluated predictive performance, including accuracy and the area under the receiver operating characteristic curve (AUC). Regarding predictions of neurological improvements in patients with cervical SCI, XGBoost had the highest accuracy (81.1%), followed by the logistic regression (80.6%) and the decision tree (78.8%). Regarding AUC, the logistic regression showed 0.877, followed by XGBoost (0.867) and the decision tree (0.753). XGBoost reliably predicted neurological alterations in patients with cervical SCI. The utilization of predictive machine learning algorithms may enhance personalized management choices through pre-treatment categorization of patients.