Goal: Artificial intelligence applied to medical image analysis has been extensively used to
develop non-invasive diagnostic and prognostic signatures. However, these imaging …

The classification of extramural vascular invasion status using baseline magnetic resonance
imaging in rectal cancer has gained significant attention as it is an important prognostic …

In this study, we evaluate the influence of normalization on the performance of deep learning
networks for tumor segmentation and the prediction of the pathological response of locally …

Colon and rectal cancer imaging has traditionally been performed to assess for distant
disease (typically lung and liver metastases) and to assess the resectability of the primary …

Objectives Achieving a consensus on a definition for different aspects of radiomics
workflows to support their translation into clinical usage. Furthermore, to assess the …

This study aimed to construct an imaging genomics nomogram based on intravoxel
incoherent motion diffusion-weighted imaging (IVIM-DWI) to predict the status of the alpha …

With the increasing number of medical images, deep learning is being used more and more
in radiomics, but it suffers from small and heterogeneous datasets. To address this, a …

Neural networks promise automated prostate segmentation for the development of precise
and quantifiable image-based biomarkers in modern personalized oncology. Before clinical …

Jet production is the dominant process at high transverse momentum in proton-proton
collisions at the Large Hadron Collider and allows for a precise test of the strong interaction …