Radiomics is an emerging area in quantitative image analysis that aims to relate large‐scale
extracted imaging information to clinical and biological endpoints. The development of …

The domain of investigation of radiomics consists of large-scale radiological image analysis
and association with biological or clinical endpoints. The purpose of the present study is to …

Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male
population. The diagnosis, the identification of aggressive disease, and the post-treatment …

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 …

Background The large volume and suboptimal image quality of portable chest X-rays
(CXRs) as a result of the COVID-19 pandemic could post significant challenges for …

Background The purpose of this study is to investigate the use of radiomics and deep
features obtained from multiparametric magnetic resonance imaging (mpMRI) for grading …

Radiomics characterizes tumor phenotypes by extracting large numbers of quantitative
features from radiological images. Radiomic features have been shown to provide …

Prostate cancer is one of the most common forms of cancer and the third leading cause of
cancer death in North America. As an integrated part of computer-aided detection (CAD) …

Purpose To compare biparametric contrast-free radiomic machine learning (RML), mean
apparent diffusion coefficient (ADC), and radiologist assessment for characterization of …

Oral cancer (OC) is the sixth most commonly reported malignant disease globally, with high
rates of disease-related morbidity and mortality due to advanced loco-regional stage at …