Background: To develop a radiomics model based on multiparametric MRI (mpMRI) for
preoperative prediction of extraprostatic extension (EPE) in patients with prostate cancer …

Background To investigate whether magnetic resonance (MR) radiomic features combined
with machine learning may aid in predicting extraprostatic extension (EPE) in high-and non …

Background Radiomics approaches based on multiparametric MRI (mp‐MRI) have shown
high accuracy in prostate cancer (PCa) management. However, there is a need to apply …

Purpose Prediction of extraprostatic extension (EPE) is essential for accurate surgical
planning in prostate cancer (PCa). Radiomics based on magnetic resonance imaging (MRI) …

Background Preoperative prediction of extracapsular extension (ECE) of prostate cancer
(PCa) is important to guide clinical decision‐making and improve patient prognosis …

Objectives To build a machine learning (ML) model to detect extraprostatic extension (EPE)
of prostate cancer (PCa), based on radiomics features extracted from prostate MRI index …

Purpose To retrospectively assess the use of the European Society of Urogenital Radiology
(ESUR) Prostate Imaging Reporting and Data System (PI-RADS) criteria and 3-T …

Purpose: To investigate the diagnostic performance of using quantitative assessment with
multiparametric MRI (mpMRI) for prediction of extraprostatic extension (EPE) in patients with …

Purpose To evaluate MRI features associated with pathologically defined extraprostatic
extension (EPE) of prostate cancer and to propose an MRI grading system for pathologic …

Objective To investigate the diagnostic accuracy of the PI-RADS v2. 1 multiparametric
magnetic resonance imaging (mpMRI) features in predicting extraprostatic extension …