Simple Summary The integration of artificial intelligence (AI) into radiomic models has
become increasingly popular due to advances in computer-aided diagnosis tools. These …

A multitude of studies have explored the role of artificial intelligence (AI) in providing
diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection …

Background Multiparametric MRI can help identify clinically significant prostate cancer
(csPCa)(Gleason score≥ 7) but is limited by reader experience and interobserver variability …

Image processing technology has always been a hot and difficult topic in the field of artificial
intelligence. With the rise and development of machine learning and deep learning …

Purpose The purpose of this study was to perform a systematic review of the literature on the
diagnostic performance, in independent test cohorts, of artificial intelligence (AI)-based …

Simple Summary In approximately 50% of prostate cancer patients undergoing surgical
treatment, cancer has extended beyond the prostate boundary (ie, extraprostatic extension) …

Objective To evaluate the effectiveness of a self-adapting deep network, trained on large-
scale bi-parametric MRI data, in detecting clinically significant prostate cancer (csPCa) in …

Simple Summary Magnetic resonance imaging (MRI) has allowed the early detection of PCa
to evolve towards clinically significant PCa (csPCa), decreasing unnecessary prostate …

Background Identifying patients with clinically significant prostate cancer (csPCa) before
biopsy helps reduce unnecessary biopsies and improve patient prognosis. The diagnostic …

Image registration can map the ground truth extent of prostate cancer from histopathology
images onto MRI, facilitating the development of machine learning methods for early …