Stalking the stalk: Isolated pituitary stalk thickening and predictive factors for proliferative disease.

Background: Few studies have evaluated predictive factors of isolated pituitary stalk thickening (iPST) in children.

Methods: In this retrospective study, radiology, endocrinology, and neuro-oncology databases were interrogated to identify patients with iPST between January 2000 and June 2019. A blinded, longitudinal assessment of MRIs was performed using quantitative, semi-quantitative, and qualitative metrics.

The landscape of primary mismatch repair deficient gliomas in children, adolescents, and young adults: a multi-cohort study.

Background: Gliomas are a major cause of cancer-related death among children, adolescents, and young adults (age 0-40 years). Primary mismatch repair deficiency (MMRD) is a pan-cancer mechanism with unique biology and therapeutic opportunities. We aimed to determine the extent and impact of primary MMRD in gliomas among children, adolescents, and young adults.

Generating 3D brain tumor regions in MRI using vector-quantization Generative Adversarial Networks.

Medical image analysis has significantly benefited from advancements in deep learning, particularly in the application of Generative Adversarial Networks (GANs) for generating realistic and diverse images that can augment training datasets. The common GAN-based approach is to generate entire image volumes, rather than the region of interest (ROI). Research on deep learning-based brain tumor classification using MRI has shown that it is easier to classify the tumor ROIs compared to the entire image volumes.

Artificial intelligence in emergency neuroradiology: Current applications and perspectives.

Emergency neuroradiology provides rapid diagnostic decision-making and guidance for management for a wide range of acute conditions involving the brain, head and neck, and spine. This narrative review aims at providing an up-to-date discussion about the state of the art of applications of artificial intelligence in emergency neuroradiology, which have substantially expanded in depth and scope in the past few years. A detailed analysis of machine learning and deep learning algorithms in several tasks related to acute ischemic stroke involving various imaging modalities, including a description of existing commercial products, is provided.