Purpose To compare biparametric contrast-free radiomic machine learning (RML), mean
apparent diffusion coefficient (ADC), and radiologist assessment for characterization of …

Objectives The aims of this study were to discriminate among prostate cancers (PCa's) with
Gleason scores 6, 7, and≥ 8 on biparametric magnetic resonance imaging (bpMRI) of the …

Background Radiomics or computer‐extracted texture features derived from MRI have been
shown to help quantitatively characterize prostate cancer (PCa). Radiomics have not been …

Purpose To compare the diagnostic accuracy of biparametric MRI (bpMRI) and
multiparametric MRI (mpMRI) for prostate cancer (PCa) and clinically significant prostate …

Objective To investigate whether machine learning-based analysis of MR radiomics can
help improve the performance PI-RADS v2 in clinically relevant prostate cancer (PCa) …

Background Multiparametric MRI (mp‐MRI) combined with machine‐aided approaches
have shown high accuracy and sensitivity in prostate cancer (PCa) diagnosis. However …

Prostate cancer (PCa) is a pernicious tumor with high heterogeneity, which creates a
conundrum for making a precise diagnosis and choosing an optimal treatment approach …

Simple Summary Early diagnosing clinically significant prostate cancer (csPCa) through
Magnetic Resonance Imaging (MRI) is very challenging and, nowadays, csPCa confirmation …

Objectives To investigate a previously developed radiomics-based biparametric magnetic
resonance imaging (bpMRI) approach for discrimination of clinically significant peripheral …

Objectives To create a radiomics approach based on multiparametric magnetic resonance
imaging (mpMRI) features extracted from an auto-fixed volume of interest (VOI) that …