This review discusses potential oncologic and nononcologic applications of CT texture
analysis (CTTA CT texture analysis), an emerging area of “radiomics” that extracts, analyzes …

Radiomics is a rapidly develo** field of research focused on the extraction of quantitative
features from medical images, thus converting these digital images into minable, high …

Radiomics refers to the extraction of mineable data from medical imaging and has been
applied within oncology to improve diagnosis, prognostication, and clinical decision support …

Deep learning has been recognized as a paradigm-shifting tool in radiology. Deep learning
reconstruction (DLR) has recently emerged as a technology used in the image …

Purpose Many radiomics features were originally developed for non‐medical imaging
applications and therefore original assumptions may need to be reexamined. In this study …

White blood cells activated by either a pathogen or as part of a systemic inflammatory
disease are characterized by high energy consumption and are therefore taking up the …

After seminal papers over the period 2009–2011, the use of texture analysis of PET/CT
images for quantification of intratumour uptake heterogeneity has received increasing …

Quantitative analysis of tumour characteristics based on medical imaging is an emerging
field of research. In recent years, quantitative imaging features derived from CT, positron …

The Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel
on the diagnostic performance of radiomics signature in solitary pulmonary nodule (SPN) …

Objectives To develop and validate a pre-transcatheter arterial chemoembolization (TACE)
MRI-based radiomics model for predicting tumor response in intermediate-advanced …