Purpose Artificial intelligence (AI) is playing an ever-increasing role in Neuroradiology.
Methods When designing AI-based research in neuroradiology and appreciating the …

Simple Summary The two most common cross-sectional imaging modalities, computed
tomography (CT) and magnetic resonance imaging (MRI), have shown enormous utility in …

Simple Summary As a rapid-development research field, radiomics-based analysis has
been applied to many clinical problems. However, the reproducibility of the radiomics …

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 …

Background Radiomics is the extraction of predefined mathematic features from medical
images for the prediction of variables of clinical interest. While some studies report …

Introduction Being the most common primary brain tumor, glioblastoma presents as an
extremely challenging malignancy to treat with dismal outcomes despite treatment. Varying …

This report presents an overview of how machine learning is rapidly advancing clinical
translational imaging in ways that will aid in the early detection, prediction, and treatment of …

Background The determination of HER2 expression status contributes significantly to HER2-
targeted therapy in breast carcinoma. However, an economical, efficient, and non-invasive …

Objectives To investigate sources of variation in a multicenter rectal cancer MRI dataset
focusing on hardware and image acquisition, segmentation methodology, and radiomics …

Objectives Radiomic features have demonstrated encouraging results for non-invasive
detection of molecular biomarkers, but the lack of guidelines for pre-processing MRI-data …