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

Purpose Artificial intelligence (AI) models are playing an increasing role in biomedical
research and healthcare services. This review focuses on challenges points to be clarified …

Modern medicine is reliant on various medical imaging technologies for non-invasively
observing patients' anatomy. However, the interpretation of medical images can be highly …

Radiomics is a rapidly evolving field of research concerned with the extraction of quantitative
metrics—the so-called radiomic features—within medical images. Radiomic features capture …

Radiomics is the quantitative analysis of standard‐of‑care medical imaging; the information
obtained can be applied within clinical decision support systems to create diagnostic …

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 …

Feature selection technique is a knowledge discovery tool which provides an understanding
of the problem through the analysis of the most relevant features. Feature selection aims at …

Radiomics is an emerging translational field of research aiming to extract mineable high-
dimensional data from clinical images. The radiomic process can be divided into distinct …

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated
remarkable progress in image-recognition tasks. Methods ranging from convolutional neural …

Purpose: Tumors are continuously evolving biological systems, and medical imaging is
uniquely positioned to monitor changes throughout treatment. Although qualitatively tracking …