Background Application of radiomics proceeds by extracting and analysing imaging features
based on generic morphological, textural, and statistical features defined by formulas …

Recent advances in computer-aided diagnosis, treatment response and prognosis in
radiomics and deep learning challenge radiology with requirements for world-wide …

Radiomics is a promising tool for the development of quantitative biomarkers to support
clinical decision-making. It has been shown to improve the prediction of response to …

The aim of this study was to investigate the usefulness of radiomics in the absence of well-
defined standard guidelines. Specifically, we extracted radiomics features from multicenter …

Tract-specific microstructural analysis of the brain's white matter (WM) using diffusion MRI
has been a driver for neuroscientific discovery with a wide range of applications. Tractometry …

Background Placenta accreta spectrum (PAS) is a rare, life-threatening complication of
pregnancy. Predicting PAS severity is critical to individualise care planning for the birth. We …

Purpose To identify precise three-dimensional radiomics features in CT images that enable
computation of stable and biologically meaningful habitats with machine learning for cancer …

Objectives: Distinguishing between radiation necrosis (RN) and metastatic progression is
extremely challenging due to their similarity in conventional imaging. This is crucial from a …

Simple Summary Gastric cancer remains the world's fifth most lethal malignancy. Perineural
invasion (PNI) is a common growth pattern of gastric cancer. Currently, the diagnosis of PNI …

Simple Summary Adrenal masses represent a common incidental finding at imaging exams
such as computed tomography (CT) and magnetic resonance imaging performed for …