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

Modern medicine is reliant on various medical imaging technologies for non-invasively
observing patients' anatomy. However, the interpretation of medical images can be highly …

Carrying out large multicenter studies is one of the key goals to be achieved towards a faster
transfer of the radiomics approach in the clinical setting. This requires large-scale radiomics …

Objective To investigate the impact of harmonization on the performance of CT, PET, and
fused PET/CT radiomic features toward the prediction of mutations status, for epidermal …

Rapid advances in artificial intelligence (AI) and machine learning, and specifically in deep
learning (DL) techniques, have enabled broad application of these methods in health care …

Artificial intelligence is a hot topic in medical imaging. The development of deep learning
methods and in particular the use of convolutional neural networks (CNNs), have led to …

The evaluation of the efficacy of different therapies is of paramount importance for the
patients and the clinicians in oncology, and it is usually possible by performing imaging …

Breast cancer is a common disease that causes great health concerns to women worldwide.
During the diagnosis and treatment of breast cancer, medical imaging plays an essential …

Image quantitation methods including quantitative MRI, multiparametric MRI, and radiomics
offer great promise for clinical use. However, many of these methods have limited clinical …

Purpose: Radiomic features extracted from medical images acquired in different countries
may demonstrate a batch effect. Thus, we investigated the effect of harmonization on a …