Novel Use and Value of Contrast-Enhanced Susceptibility-Weighted Imaging Morphologic and Radiomic Features in Predicting Extremity Soft Tissue Undifferentiated Pleomorphic Sarcoma Treatment Response.

Purpose: Undifferentiated pleomorphic sarcomas (UPSs) demonstrate therapy-induced hemosiderin deposition, granulation tissue formation, fibrosis, and calcification. We aimed to determine the treatment-assessment value of morphologic tumoral hemorrhage patterns and first- and high-order radiomic features extracted from contrast-enhanced susceptibility-weighted imaging (CE-SWI).

Materials And Methods: This retrospective institutional review board-authorized study included 33 patients with extremity UPS with magnetic resonance imaging and resection performed from February 2021 to May 2023.

Quantitative Pupillometry Predicts Neurologic Deterioration in Patients with Large Middle Cerebral Artery Stroke.

Objective: This study assesses whether longitudinal quantitative pupillometry predicts neurological deterioration after large middle cerebral artery (MCA) stroke and determines how early changes are detectable.

Methods: This prospective, single-center observational cohort study included patients with large MCA stroke admitted to Boston Medical Center's intensive care unit (2019-2024). Associations between time-to-neurologic deterioration and quantitative pupillometry, including Neurological Pupil Index (NPi), were assessed using Cox proportional hazards models with time-dependent covariates adjusted for age, sex, and Alberta Stroke Program Early CT Score.

Pregnancy-Related Aortic Dissection-A Rare Complication of Pregnancy.

Patients with pregnancy-related aortic dissections should be transferred to high-volume aortic centers for management. Malperfusion syndromes from acute type A aortic dissections appear to have better outcomes with thoracic endovascular repair before definitive surgical repair.

Creating simple predictive models in ecology, conservation and environmental policy based on Bayesian belief networks.

Predictive models are often complex to produce and interpret, yet can offer valuable insights for management, conservation and policy-making. Here we introduce a new modelling tool (the R package 'BBNet'), which is simple to use, and requires little mathematical or computer programming background. By using straightforward concepts to describe interactions between model components, predictive models can be effectively constructed using basic spreadsheet tools and loaded into the R package.