Predicting neurosurgical referral outcomes in patients with chronic subdural hematomas using machine learning algorithms - A multi-center feasibility study.

Background: Chronic subdural hematoma (CSDH) incidence and referral rates to neurosurgery are increasing. Accurate and automated evidence-based referral decision-support tools that can triage referrals are required. Our objective was to explore the feasibility of machine learning (ML) algorithms in predicting the outcome of a CSDH referral made to neurosurgery and to examine their reliability on external validation.

A case of a giant thoracic schwannoma.

Background: Giant solitary schwannomas are rare, benign, and typically slow-growing tumors reaching up to 20 cm in size.

Case Description: A 43-year-old male presented with shortness of breath and chest pain. The thoracic MRI showed a giant mass 15 cm in diameter filling the left chest cavity.