From molecular signatures to radiomics: tailoring neurooncological strategies through forecasting of glioma growth.

Objective: Contemporary oncological paradigms for adjuvant treatment of low- and intermediate-grade gliomas are often guided by a limited array of parameters, overlooking the dynamic nature of the disease. The authors' aim was to develop a comprehensive multivariate glioma growth model based on multicentric data, to facilitate more individualized therapeutic strategies.

Methods: Random slope models with subject-specific random intercepts were fitted to a retrospective cohort of grade II and III gliomas from the database at Kepler University Hospital (n = 191) to predict future mean tumor diameters.

Tailored Intraoperative MRI Strategies in High-Grade Glioma Surgery: A Machine Learning-Based Radiomics Model Highlights Selective Benefits.

Background And Objectives: In high-grade glioma (HGG) surgery, intraoperative MRI (iMRI) has traditionally been the gold standard for maximizing tumor resection and improving patient outcomes. However, recent Level 1 evidence juxtaposes the efficacy of iMRI and 5-aminolevulinic acid (5-ALA), questioning the continued justification of iMRI because of its associated costs and extended surgical duration. Nonetheless, drawing from our clinical observations, we postulated that a subset of intricate HGGs may continue to benefit from the adjunctive application of iMRI.

Predicting Visual Acuity by Using Machine Learning in Patients Treated for Neovascular Age-Related Macular Degeneration.

Purpose: To predict, by using machine learning, visual acuity (VA) at 3 and 12 months in patients with neovascular age-related macular degeneration (AMD) after initial upload of 3 anti-vascular endothelial growth factor (VEGF) injections.

Design: Database study.

Participants: For the 3-month VA forecast, 653 patients (379 female) with 738 eyes and an average age of 74.

Characterization of a sCMOS-based high-resolution imaging system.

The detection system is a key part of any imaging station. Here the performance of the novel sCMOS-based detection system installed at the ID17 biomedical beamline of the European Synchrotron Radiation Facility and dedicated to high-resolution computed-tomography imaging is analysed. The system consists of an X-ray-visible-light converter, a visible-light optics and a PCO.