Impact of Endorectal Coil Use on Extraprostatic Extension Detection in Prostate MRI: A Retrospective Monocentric Study.

Rationale And Objectives: Accurate preoperative mpMRI-based detection of extraprostatic extension (EPE) in prostate cancer (PCa) is critical for surgical planning and patient outcomes. This study aims to evaluate the impact of endorectal coil (ERC) use on the diagnostic performance of mpMRI in detecting EPE.

Materials And Methods: This retrospective study with prospectively collected data included participants who underwent mpMRI and subsequent radical prostatectomy for PCa between 2007 and 2024.

Masked Image Modeling Meets Self-Distillation: A Transformer-Based Prostate Gland Segmentation Framework for Pathology Slides.

Detailed evaluation of prostate cancer glands is an essential yet labor-intensive step in grading prostate cancer. Gland segmentation can serve as a valuable preliminary step for machine-learning-based downstream tasks, such as Gleason grading, patient classification, cancer biomarker building, and survival analysis. Despite its importance, there is currently a lack of a reliable gland segmentation model for prostate cancer.

A Prostate Imaging-Reporting and Data System version 2.1-based predictive model for clinically significant prostate cancer diagnosis.

Objectives: To develop and validate a Prostate Imaging-Reporting and Data System (PI-RADS) version 2.1 (v2.1)-based predictive model for diagnosis of clinically significant prostate cancer (csPCa), integrating clinical and multiparametric magnetic resonance imaging (mpMRI) data, and compare its performance with existing models.

Multidisciplinary care for children with cleft lip and palate and their families: Family-Centered Care.

Objective: To understand the multidisciplinary care provided to children with cleft lip and palate and their families.

Method: Descriptive, exploratory and qualitative study, based on the theoretical-philosophical framework of Family-Centered Care, conducted with professionals from the multidisciplinary team of an association supporting individuals with cleft lip and palate. The data were processed using the Iramuteq® software and systematized using Similarity Analysis.