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 advancement of artificial intelligence concurrent with the development of medical
imaging techniques provided a unique opportunity to turn medical imaging from mostly …

Intelligent agents are showing increasing promise for clinical decision-making in a variety of
healthcare settings. While a substantial body of work has contributed to the best strategies to …

Purpose Since the 1990s, PET has been successfully combined with MR or CT systems. In
the past years, especially PET systems have seen a trend towards an enlarged axial field of …

Background Solid components of part-solid nodules (PSNs) at CT are reflective of invasive
adenocarcinoma, but studies describing radiomic features of PSNs and the perinodular …

Detection and segmentation of abnormalities on medical images is highly important for
patient management including diagnosis, radiotherapy, response evaluation, as well as for …

With the continuous development of medical image informatics technology, more and more
high-throughput quantitative data could be extracted from digital medical images, which has …

Radiomics is an emerging field using the extraction of quantitative features from medical
images for tissue characterization. While MRI-based radiomics is still at an early stage, it …

Purpose MRI-based tumor response prediction to neoadjuvant systemic therapy (NST) in
breast cancer patients is increasingly being studied using radiomics with outcomes that …

Simple Summary Handcrafted radiomic features (HRFs) are quantitative features extracted
from medical images, and they are mined for associations with different clinical endpoints …