The high-throughput extraction of quantitative imaging features from medical images for the
purpose of radiomic analysis, ie, radiomics in a broad sense, is a rapidly developing and …

Radiogenomics, focusing on the relationship between genomics and imaging phenotypes,
has been widely applied to address tumour heterogeneity and predict immune …

There is a need for in vivo diagnostic imaging probes that can noninvasively measure tumor-
infiltrating CD8+ leukocytes. Such imaging probes could be used to predict early response …

Abstract Purpose of Review This review will explore the latest in advanced imaging
techniques, with a focus on the complementary nature of multiparametric, multimodality …

Simple Summary Positron emission tomography (PET) is a functional imaging technique
which plays an increasingly important role in the management of brain tumors. Owing …

Artificial intelligence and machine learning based approaches are increasingly finding their
way into various areas of nuclear medicine imaging. With the technical development of new …

With the recent advancement of novel biomedical technologies such as high-throughput
sequencing and wearable devices, multi-modal biomedical data ranging from multi-omics …

BACKGROUND Molecular characterization of glioma has implications for prognosis,
treatment planning, and prediction of treatment response. Current histopathology is limited …

Purpose To evaluate radiomic features extracted from standard static images (20–40 min pi),
early summation images (5–15 min pi), and dynamic [18 F] FET PET images for the …

There have been many recently published studies exploring machine learning (ML) and
deep learning applications within neuroradiology. The improvement in performance of these …