Machine learning-based multiparametric magnetic resonance imaging radiomics model for distinguishing central neurocytoma from glioma of lateral ventricle.

Objectives: To develop a machine learning-based radiomics model based on multiparametric magnetic resonance imaging (MRI) for preoperative discrimination between central neurocytomas (CNs) and gliomas of lateral ventricles.

Methods: A total of 132 patients from two medical centers were enrolled in this retrospective study. Patients from the first medical center were divided into a training cohort (n = 74) and an internal validation cohort (n = 30).

Clinical, radiological, pathological and prognostic aspects of intraventricular oligodendroglioma: comparison with central neurocytoma.

Studies comparing intraventricular oligodendroglioma (IVO) and central neurocytoma (CN) in terms of their clinical, radiological and pathological features are scarce. We, therefore, investigated the similarities and differences between these types of tumors to get a better understanding of how they may be more properly diagnosed and treated. The clinical manifestations, CT/MRI findings, pathological characteristics and clinical outcomes of 8 cases of IVOs and 12 cases of CNs were analyzed retrospectively.

Role of intratumoral flow void signs in the differential diagnosis of intracranial solitary fibrous tumors and meningiomas.

Background And Objective: An absence of signal on magnetic resonance (MR) images caused by blood or cerebral spinal fluid flow is known as a flow void, and may be related to intracranial tumors such as intracranial solitary fibrous tumor (SFT) or meningioma. However, the differential diagnosis of these neoplasms based on flow void configuration is controversial. This study investigated common intratumoral flow void patterns for differentiating intracranial SFT from meningioma.