This article is a comprehensive review of the basic background, technique, and clinical
applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A …

In an increasingly data-driven world, artificial intelligence is expected to be a key tool for
converting big data into tangible benefits and the healthcare domain is no exception to this …

The vital physiological role of the pituitary gland, alongside its proximity to critical
neurovascular structures, means that pituitary adenomas can cause significant morbidity or …

Purpose To perform a systematic review on the research on the application of artificial
intelligence (AI) to imaging published in Italy and identify its fields of application, methods …

Rationale and Objectives To evaluate an MRI radiomics-powered machine learning (ML)
model's performance for the identification of deep myometrial invasion (DMI) in endometrial …

Purpose To evaluate the diagnostic performance of machine learning for discrimination
between low-grade and high-grade cartilaginous bone tumors based on radiomic …

Imaging diagnosis is crucial for early detection and monitoring of brain tumors. Radiomics
enable the extraction of a large mass of quantitative features from complex clinical imaging …

This study aims to investigate the influence of interobserver manual segmentation variability
on the reproducibility of 2D and 3D unenhanced computed tomography (CT)-and magnetic …

Purpose Pituitary macroadenoma consistency can influence the ease of lesion removal
during surgery, especially when using a transsphenoidal approach. Unfortunately, it is not …

Introduction Deep learning–based MRI reconstruction has recently been introduced to
improve image quality. This study aimed to evaluate the performance of deep learning …