Extramedullary hematopoiesis (EMH) usually involves reticuloendothelial system. However, it rarely may be present in the serous body effusions. In our case, the fluid cytology of both peritoneal and pleural fluid was diagnostic of the EMH in a patient with an undiagnosed underlying etiology.

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Background: Pediatric posterior fossa ependymoma contributes to morbidity and mortality in children. Following gross total resection and adjuvant radiotherapy, there is a known risk of local recurrence that portends a dismal prognosis. We sought to characterize survival in a molecularly defined cohort with an emphasis on recurrence patterns that influence outcome. Methods: This study was approved by the Ethics Board of the Hospital for Sick Children. We performed a twenty-year single-center retrospective study to identify clinical, demographic and treatment characteristics of patients with pathologically diagnosed posterior fossa ependymoma. Results: There were 60 patients identified that underwent primary resection. Recurrence rate in the cohort was 48% with 29 cases of recurrent ependymoma occurring at a mean time of 24 months after index surgery. No mortalities were observed among patients undergoing primary resection without recurrent disease. Median cohort survival was 12.3 years in the primary cohort and and 6.32 years among patients recurrent ependymoma. Recurrent disease was significantly associated with worse overall survival after multivariate analysis (HR = 0.024). Conclusions: We highlight overall survival and factors influencing mortality in pediatric posterior fossa ependymoma. Recurrent disease confers a worse prognosis. We describe for the first time survival trends following local and distant recurrences managed through multiple resections.

Background: We aimed to develop an efficient and reliable artificial intelligence solution to automate prediction of neurosurgical intervention using acute traumatic brain injury computed tomography (CT) scans. Methods: TBI patients were identified from 2005 - 2022 at a Level 1 Canadian trauma center. Model training, validation, and testing was performed using head CT scans with patient-level labels corresponding to whether the patient received neurosurgical intervention. The finalized model was then deployed in a simulated prospective fashion on all TBI patients presenting to our center over an 18-month epoch. Results: 2,806 TBI scans were utilized for development of the Automated Surgical Intervention Support Tool (ASIST-TBI). 612 additional consecutive scans were used for simulated prospective model deployment. Prediction of neurosurgical intervention exhibited an area under receiver operating curve (AUC) of 0.92, accuracy of 0.87, sensitivity of 0.87, and specificity of 0.88 on the test dataset. On simulated prospective data, the results were: AUC 0.89, sensitivity 0.85, specificity 0.84 and accuracy of 0.84. Conclusions: We demonstrate the development and validation of ASIST-TBI, a machine learning model that accurately predicts whether TBI patients will need neurosurgical intervention. This model has potential application to optimize decision support and province-wide efficiency of inter-facility TBI triage to tertiary care centers.