Radiomics is an emerging field of research based on extracting mathematical descriptive
features from medical images with the aim of improving diagnostic performance and …

Background and objectives Standardization of radiomic data acquisition protocols is still at a
very early stage, revealing a strong need to work towards the definition of uniform image …

“Just Accepted” papers have undergone full peer review and have been accepted for
publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout …

Diffusion-weighted imaging (DWI) combined with radiomics can aid in the differentiation of
breast lesions. Segmentation characteristics, however, might influence radiomic features. To …

The aim of this systematic review and meta-analysis is to evaluate the performance of
classification metrics of machine learning-driven radiomics in diagnosing hepatocellular …

Radiomics is a progressive field aiming to quantitatively assess the diversity within and
between tumors using image analysis. It holds tremendous promise for tracking tumor …

Objective. Accurate and reproducible tumor delineation on positron emission tomography
(PET) images is required to validate predictive and prognostic models based on PET …

We aimed to compare the performance and interobserver agreement of radiologists
manually segmenting images or those assisted by automatic segmentation. We further …

Radiomics is the process of extracting valuable features from medical images, which can be
further analyzed to improve diagnosis, prognosis, and clinical decision support in oncology …

The diffusion-weighted imaging (DWI) technique is known for its capability to differentiate the
diffusion of water molecules between cancerous and non-cancerous cervix tissues, which …