Radiomics is an emerging field of image analysis with potential applications in patient risk
stratification. This study developed and evaluated machine learning models using …

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

Background To evaluate the potential of clinical-based model, a biparametric MRI-based
radiomics model and a clinical-radiomics combined model for predicting clinically significant …

Multiparametric prostate magnetic resonance imaging (mpMRI) is widely used as a triage
test for men at a risk of prostate cancer. However, the traditional role of mpMRI was confined …

Purpose To develop and validate a classifier system for prediction of prostate cancer (PCa)
Gleason score (GS) using radiomics and texture features of T2-weighted imaging (T2w) …

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

Purpose To investigate the efficiency of radiomics signature in discriminating between
benign and malignant prostate lesions with similar biparametric magnetic resonance …

This paper presents a quantitative radiomics feature model for performing prostate cancer
detection using multiparametric MRI (mpMRI). It incorporates a novel tumor candidate …

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

Multiparametric magnetic resonance imaging (mpMRI) has become increasingly important
for the clinical assessment of prostate cancer (PCa), but its interpretation is generally …