Radiomics is a process that allows the extraction and analysis of quantitative data from
medical images. It is an evolving field of research with many potential applications in …

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 …

Advances in both imaging and computers have synergistically led to a rapid rise in the
potential use of artificial intelligence in various radiological imaging tasks, such as risk …

Lung malignancies have been extensively characterized through radiomics and deep
learning. By providing a three-dimensional characterization of the lesion, models based on …

Recent advancements in signal processing (SP) and machine learning, coupled with
electronic medical record kee** in hospitals and the availability of extensive sets of …

Radiomics research is rapidly growing in recent years, but more concerns on radiomics
reliability are also raised. This review attempts to update and overview the current status of …

Interest in real‐world data (RWD) and real‐world evidence (RWE) to expedite and enrich the
development of new biopharmaceutical products has proliferated in recent years, spurred by …

Simple Summary Brain MRI scans often require different imaging sequences based on
tissue types, posing a common challenge. In our research, we propose a method that utilizes …

Advanced cross-sectional and functional imaging techniques enable non-invasive
visualization of tumor extent and functional metabolic activity and play a central role in the …

Recent years have witnessed tremendous growth in the application of machine learning
(ML) and deep learning (DL) techniques in medical physics. Embracing the current big data …