Medical imaging can assess the tumor and its environment in their entirety, which makes it
suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in …

Radiomics is an emerging area in quantitative image analysis that aims to relate large‐scale
extracted imaging information to clinical and biological endpoints. The development of …

With this review, we aimed to provide a synopsis of recently proposed applications of
machine learning (ML) in radiology focusing on prostate magnetic resonance imaging (MRI) …

Context Although magnetic resonance imaging (MRI) is broadly implemented into active
surveillance (AS) protocols, data on the reliability of serial MRI in order to help guide follow …

Advances in our understanding of the role of magnetic resonance imaging (MRI) for the
detection of prostate cancer have enabled its integration into clinical routines in the past two …

Summary Background Biparametric MRI (comprising T2-weighted MRI and apparent
diffusion coefficient maps) is increasingly being used to characterise prostate cancer …

Objective To investigate the applications of artificial intelligence (AI) in diagnosis, treatment
and outcome predictionin urologic diseases and evaluate its advantages over traditional …

Background Radiomics have the potential to further increase the value of MRI in prostate
cancer management. However, implementation in clinical practice is still far and concerns …

Objectives To develop an automatic method for identification and segmentation of clinically
significant prostate cancer in low-risk patients and to evaluate the performance in a routine …

Purpose To evaluate the performance of a multi-parametric MRI (mp-MRI)-based radiomics
signature for discriminating between clinically significant prostate cancer (csPCa) and …