Computer-extracted tumour characteristics have been incorporated into medical imaging
computer-aided diagnosis (CAD) algorithms for decades. With the advent of radiomics, an …

Magnetic resonance imaging and computed tomography from multiple batches (eg sites,
scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to …

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

The development of clinical trials has led to substantial improvements in the prevention and
treatment of many diseases, including brain cancer. Advances in medicine, such as …

Radiomics is a high-throughput approach to image phenotyping. It uses computer
algorithms to extract and analyze a large number of quantitative features from radiological …

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

Background Many studies in radiomics are using feature selection methods to identify the
most predictive features. At the same time, they employ cross-validation to estimate the …

Purpose The aim of this study is to determine if radiomics features extracted from staging
magnetic resonance (MR) images could predict 2-year long-term clinical outcome in patients …

Simple Summary Ovarian cancer has a heterogeneous response to treatment, and relapse
may vary considerably. Different studies investigated the role of radiomics in ovarian cancer …

Objectives A critical problem in radiomic studies is the high dimensionality of the datasets,
which stems from small sample sizes and many generic features extracted from the volume …