Urology

Predicting End-Stage Renal Disease and Mortality in Chronic Kidney Disease Using Machine Learning: Retrospective Cohort Study.

BACKGROUND: Chronic kidney disease (CKD) is a global health burden characterized by heterogeneous progression trajectories. Without timely and appropriate management, CKD can lead to increased morbidity and mortality and a reduced quality of life. Therefore, early identification of patients at high risk of developing end-stage renal disease (ESRD) or mortality is essential to facilitate timely int...

Artificial intelligence in prostate cancer imaging: A mini-review of current applications and future directions.

This mini-review synthesizes evidence from recent studies to provide an updated perspective on current applications, methodological challenges, and future directions for artificial intelligence (AI) in multiparametric magnetic resonance imaging (mpMRI) based prostate cancer (CaP) imaging. CaP remains the most frequently diagnosed noncutaneous malignancy among men worldwide and a leading cause of c...

Targeting the intrinsically disordered AR-NTD through a machine learning-based enhanced sampling workflow.

Targeting the intrinsically disordered N-terminal domain of the androgen receptor (AR-NTD) represents a promising strategy to overcome resistance in p...

Deep learning based on multiparametric MRI for differentiating benign from malignant PI-RADS 3 prostate lesions: a dual-center study.

To address the diagnostic challenge of indeterminate PI-RADS 3 prostate lesions, we developed a non-invasive deep learning radiomics model using multi...

Deep learning models built from PSMA PET of the primary tumor can predict synchronous and metachronous prostate cancer metastases.

OBJECTIVE: The objective was to develop prognostic models that included convolutional neural networks (CNN) derived from 18F-DCFPyL (PSMA) PET imaging...

A decision-oriented framework for genomic testing across the prostate cancer continuum.

Genomic testing is now embedded in contemporary prostate cancer care, yet the clinical meaning of different genomic platforms varies substantially by ...

Computational exploration of quercetin derivatives from Azadirachta indica leaf as threonine tyrosine kinase inhibitors for potential lung and pancreatic cancer treatment.

Lung and pancreatic adenocarcinomas account for substantial cancer-related mortality worldwide, with current therapeutic efficacy severely limited by ...

Response to Comment on "Machine learning model for predicting interfraction motion of the seminal vesicles in prostate cancer radiotherapy".

Correction to "A deep-learning model using automatedperformance metrics and clinical features topredict urinary continence recovery afterrobot-assisted radical prostatectomy".

Navigating PI-RADS v2.1 in clinical practice: pitfalls, variability, and the supportive role of AI.

Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 has substantially advanced the standardization of prostate MRI acquisition, interpret...

Deep learning for clinically significant prostate cancer detection on MRI: a systematic review, HSROC meta-analysis, and direct comparison with PI-RADS-based interpretation.

OBJECTIVES: To estimate patient-level diagnostic accuracy of deep learning (DL) for MRI-based detection of clinically significant prostate cancer (csP...

Longitudinal serum uric acid transition patterns and their clinical, genetic, and dietary determinants: prospective prediction of incident hyperuricemia and gout in a Korean population-based cohort.

BACKGROUND: Longitudinal serum uric acid (SUA) transition patterns and their clinical, genetic, and dietary determinants remain poorly characterized. ...

DEHP-induced male reproductive toxicity: Evidence from population studies, animal experiments, and multi-omics profiling.

OBJECTIVE: To evaluate the association between di(2-ethylhexyl) phthalate (DEHP) exposure and testosterone deficiency in adult men, and to explore mol...

Electrocardiogram-based short-term risk stratification for acute kidney injury using time-frequency deep learning analysis.

BACKGROUND AND OBJECTIVES: Acute kidney injury (AKI) is a common and clinically important complication in critically ill and perioperative patients. C...

Diagnostic yield and cost of three-level H&E sectioning in prostate biopsies.

AIM: The traditional three-level H&E sectioning protocol for prostate biopsies was developed for ultrasound-guided systematic sampling and predates le...

Reply to Kristoffer Mazanti Cold, Lars Konge, and Sarah Hjartbro Bube's Letter to the Editor re. Roessler N, Miszczyk M, Miyajima K, et al. Harnessing Artificial Intelligence for Risk Stratification and Outcome Prediction in Urologic Cancers: A Systematic Review. Eur Urol Focus. In press. http://dx.doi.org/10.1016/j.euf.2025.12.007.

Single-cell-based analysis establishes C1QA-mediated promotion of high glucose-induced tubular epithelial injury via ERS in DN.

BACKGROUND: Diabetic nephropathy (DN) poses a growing worldwide health challenge as a leading cause of end-stage renal disease, a condition that arise...

An artificial intelligence solution for evaluation of prostate needle core biopsy specimens: impact in a non-specialist setting.

The diagnosis of prostate cancer rests on the histopathological evaluation of prostate needle core biopsy specimens (NCBS) supplemented by immunohisto...

Comparative effectiveness and cost-effectiveness of an artificial intelligence workflow for small renal mass diagnosis on computed tomography.

INTRODUCTION: Small renal masses are increasingly detected on imaging, but their accurate classification as benign, indolent, or aggressive remains ch...

Cost-Effectiveness of AI-Assisted Kellgren-Lawrence Grading of Knee Osteoarthritis in the South Korean Health-Care System.

BACKGROUND: Early diagnosis of knee osteoarthritis (KOA) is often delayed due to reliance on subjective interpretation of radiographs. Recent advances...