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.

Female Faculty Representation in Anesthesiology: A Retrospective Cross-Sectional Analysis.

Introduction: The presence of female faculty members in anesthesia residency programs is pivotal for enhancing and supporting diversity and gender equity in medical education. This study probes the intricate interplay between the percentage of female faculty and whether program type and geographical location impact the composition of female faculty.

Methods: For this retrospective cross-sectional analysis, we collected data from the Fellowship and Residency Electronic Interactive Database Access (FREIDA) system to assess the percentages of female faculty in anesthesia residency programs.

Predicting the tumor microenvironment composition and immunotherapy response in non-small cell lung cancer from digital histopathology images.

Immune checkpoint inhibitors (ICI) have become integral to treatment of non-small cell lung cancer (NSCLC). However, reliable biomarkers predictive of immunotherapy efficacy are limited. Here, we introduce HistoTME, a novel weakly supervised deep learning approach to infer the tumor microenvironment (TME) composition directly from histopathology images of NSCLC patients.

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.