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

Removing the bias and variance of multicentre data has always been a challenge in large
scale digital healthcare studies, which requires the ability to integrate clinical features …

The recent advancements in artificial intelligence (AI) combined with the extensive amount
of data generated by today's clinical systems, has led to the development of imaging AI …

Simple Summary Radiomics could increase the value of medical images for oncologic
patients, allowing for the identification of novel imaging biomarkers and building prediction …

Radiomics is a technique that extracts quantitative features from medical images using data‐
characterization algorithms. Radiomic features can be used to identify tissue characteristics …

Artificial intelligence (AI) and machine learning (ML) are increasingly used in radiology
research to deal with large and complex imaging data sets. Nowadays, ML tools have …

Low-income and middle-income countries (LMICs) have a disproportionately high burden of
cancer and cancer mortality. The unique barriers to optimum cancer care in these regions …

Radiomics features extracted from medical images have been widely reported to be useful
in the patient specific outcome modeling for variety of assessment and prediction purposes …

Background and purpose To investigate the radiomic feature (RF) repeatability via
perturbation and its impact on cross-institutional prognostic model generalizability in …

Objective In the present study, we aimed to evaluate the prognostic value of PET/CT-derived
radiomic features for patients with B-cell lymphoma (BCL), who were treated with …