Technological advances have made it possible to study a patient from multiple angles with
high-dimensional, high-throughput multiscale biomedical data. In oncology, massive …

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

This paper reviews state-of-the-art research solutions across the spectrum of medical
imaging informatics, discusses clinical translation, and provides future directions for …

The goals of this review paper on deep learning (DL) in medical imaging and radiation
therapy are to (a) summarize what has been achieved to date;(b) identify common and …

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered
data with nuanced decision making. Cancer offers a unique context for medical decisions …

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 …

Medical image processing and analysis (also known as Radiomics) is a rapidly growing
discipline that maps digital medical images into quantitative data, with the end goal of …

Historically, medical imaging has been a qualitative or semi-quantitative modality. It is
difficult to quantify what can be seen in an image, and to turn it into valuable predictive …

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 …

Background In this study, we evaluated the ability of radiomic textural analysis of
intratumoral and peritumoral regions on pretreatment breast cancer dynamic contrast …