Neuroendoscopic lavage for posthemorrhagic hydrocephalus of prematurity: preliminary results at a single institution in the United States.

Objective: The current neurosurgical treatment for intraventricular hemorrhage (IVH) of prematurity resulting in posthemorrhagic hydrocephalus (PHH) seeks to reduce intracranial pressure with temporary and then permanent CSF diversion. In contrast, neuroendoscopic lavage (NEL) directly addresses the intraventricular blood that is hypothesized to damage the ependyma and parenchyma, leading to ventricular dilation and hydrocephalus. The authors sought to determine the feasibility of NEL in PHH.

Genetic ancestry superpopulations show distinct prevalence and outcomes across pediatric central nervous system tumors from the PBTA and PNOC.

Background: Central nervous system (CNS) tumors lead to cancer-related mortality in children. Genetic ancestry-associated cancer prevalence and outcomes have been studied, but is limited.

Methods: We performed genetic ancestry prediction in 1,452 pediatric patients with paired normal and tumor whole genome sequencing from the Open Pediatric Cancer (OpenPedCan) project to evaluate the influence of reported race and ethnicity and ancestry-based genetic superpopulations on tumor histology, molecular subtype, survival, and treatment.

Unilateral Versus Bilateral Endoscopic Resection of Olfactory Neuroblastoma: Pooled Analysis From Prospective and Retrospective Multicenter Data.

Background: Olfactory neuroblastoma (ONB) is a rare sinonasal malignancy primarily treated with surgery. For tumors arising from the olfactory area, traditional treatment involves transcribriform resection of the anterior cranial fossa. Surgery can be performed with unilateral or bilateral resection depending on extent of involvement; however, there are currently no studies comparing outcomes between the two.

Automated pediatric brain tumor imaging assessment tool from CBTN: Enhancing suprasellar region inclusion and managing limited data with deep learning.

Background: Fully automatic skull-stripping and tumor segmentation are crucial for monitoring pediatric brain tumors (PBT). Current methods, however, often lack generalizability, particularly for rare tumors in the sellar/suprasellar regions and when applied to real-world clinical data in limited data scenarios. To address these challenges, we propose AI-driven techniques for skull-stripping and tumor segmentation.