Urology

Re: Navid Roessler, Marcin Miszczyk, Keiichiro Miyajima, et al. Harnessing Artificial Intelligence for Risk Stratification and Outcome Prediction in Urologic Cancers: A Systematic Review. Eur Urol Focus 2025; S2405-4569(25)00362-1. In press. http://dx.doi.org/10.1016/j.euf.2025.12.007.

[MRI-based deep learning model for preoperative prediction of urothelial carcinoma with variant histology of bladder: a retrospective, multicenter study].

Objectives: To develop a deep learning model based on magnetic resonance imaging (MRI) for the preoperative prediction of urothelial carcinoma with variant histology (VUC), and to evaluate its predictive performance and prognostic stratification value. Methods: This is a retrospective cohort study. Data from 1 221 patients with bladder cancer across seven centers of China between June 2013 and May...

Evaluating Iterative Deep Learning as a Labeling-Efficient Strategy for Tubular Segmentation in Digital Nephropathology.

Chronic kidney disease (CKD) is a prevalent condition worldwide and a significant global health burden that is expected to increase in the coming deca...

Diagnostic value of integrated albumin-to-leukocyte ratio risk modeling stratification and machine learning approaches for prediction of spontaneous bacterial peritonitis in cirrhotic patients.

BACKGROUND: Spontaneous bacterial peritonitis (SBP) remains a life-threatening complication of liver cirrhosis, requiring accurate and rapid predictio...

Epigenetic-Metabolic interplay in chronic kidney disease mortality: insights from grimage acceleration and transcriptomic profiling.

BACKGROUND: Chronic kidney disease (CKD) is associated with a substantially elevated risk of mortality. Although GrimAge acceleration (GAA) and metabo...

Integrative advances in biomarker-driven prostate cancer management from genomic discovery to precision oncology.

Prostate cancer (PCa) is the second most common malignancy in men worldwide, with rising mortality linked to late-stage diagnoses. While current diagn...

Elucidating the pathogenic mechanism underlying plasticizer-induced chronic kidney disease via integrated network toxicology and machine learning.

Plasticizers are ubiquitous plastic additives that have been linked to multiple adverse health outcomes and elevated risks of chronic kidney disease (...

Aleatoric uncertainty in accelerated prostate MRI reconstruction: echo-train dropout versus Gaussian noise Monte Carlo sampling.

PURPOSE: Although undersampling combined with deep learning (DL)-based reconstruction shortens MRI acquisition, it increases the chance of inaccuracie...

Overdiagnosis in renal cancer: the hidden consequence of modern imaging.

The global burden of renal cell carcinoma (RCC) has risen substantially over the past three decades, while mortality rates have remained largely stabl...

Development and validation of an early prediction model for bee-sting-induced acute kidney injury using machine learning.

Acute kidney injury (AKI) is a severe and frequent complication following bee stings, with a reported incidence of 30-50%. Early identification is cli...

AI-Informed Architectural Insights of Three-Dimensional Glandular Networks Identify Prostate Cancer Patients at a Higher Risk of Biochemical Recurrence.

Pathologists diagnose and grade prostate cancer using thin two-dimensional (2D) histological sections, but these 3-5 micron sections are too thin to v...

Explainable Electronic Medical Record-Based Machine Learning to Predict 1-Year Incident Protein-Energy Wasting in Non-Dialysis Chronic Kidney Disease: A Single-Center Development Study.

BACKGROUND: Protein-energy wasting (PEW) is common in chronic kidney disease (CKD) and is linked to poor outcomes. Early risk stratification may enabl...

Quantum Dot Encoding for In-Solution Single-Molecule Biomarker Counting in Metastatic Prostate Cancer.

Digital assays are in wide development for biomarker quantification at the single-molecule level, but the common use of surface pull-down steps limits...

Integrative transcriptomic and single-cell analysis reveals metabolic reprogramming and hexosamine biosynthesis-mediated tumor microenvironment remodeling in prostate cancer.

BACKGROUND: Prostate cancer is one of the most common malignancies in men and exhibits substantial clinical heterogeneity. A considerable proportion o...

Optimizing Deep Learning for Renal Mass Characterization in Challenging Cases: A Comparative Study of Spatial-Input Strategies and a Validated ROI-Only DLR Nomogram.

PURPOSE: Our purpose was to develop and validate an integrated clinical deep learning radiomics (DLR) nomogram for differentiation of fat-poor angiomy...

Optimizing Renal Fibrosis Assessment in Patients with Chronic Kidney Disease: Feature and Decision-Level Fusion in Machine Learning Using Clinical and Ultrasound Information.

PURPOSE: Machine learning has been extensively applied in nephrology. This study aims to evaluate and compare the effectiveness of various information...

AI based prediction of post prostatectomy urinary incontinence and its impact on quality of life: development and validation study.

Urinary incontinence can affect up to 53% of patients after radical prostatectomy, yet prediction tools rely on binary outcomes, missing heterogeneous...

Multi expert integrated algorithm for kidney biopsy triage.

Clinical decision-making often exhibits substantial inter-physician variability when evaluating identical patient data, limiting the reliability of co...

In silico discovery of novel small-molecule PD-L1 inhibitors through a multi-stage computational workflow integrating machine learning and molecular dynamics.

Cancer immunotherapy targeting the PD-1/PD-L1 pathway has transformed modern oncology; however, developing small-molecule inhibitors as viable alterna...

Integrating single-cell and bulk transcriptomic analyses to explore key lactylation-related genes in benign prostatic hyperplasia.

Benign prostatic hyperplasia (BPH) is a prevalent age-related disorder characterized by chronic inflammation, metabolic dysregulation, and abnormal ce...