Urology

Diet-microbiota-kidney axis reprogramming: mechanistic insights into microbial metabolite-driven precision nutrition for chronic kidney disease.

Medical nutrition therapy serves as the cornerstone in the management of chronic kidney disease (CKD). While conventional approaches emphasize macronutrient restriction and meal timing adjustments, research highlights the critical mediating role of gut microbiota in translating dietary patterns into physiological effects through metabolite production. Meanwhile, CKD progression is closely associat...

Whole-body magnetic resonance imaging for cancer screening in asymptomatic adults: a multicenter study.

OBJECTIVES: Whole-body MRI is increasingly used for preventive health screening; however, the prevalence and distribution of incidental oncologically relevant findings in asymptomatic individuals remain incompletely characterized. The primary objective of this multicentre study was to describe the frequency and anatomical distribution of clinically relevant findings detected by whole-body MRI in a...

Evaluation of explainable machine learning models for predicting mid-term stone recurrence after percutaneous nephrolithotomy: a retrospective observational cohort study.

OBJECTIVE: To develop an explainable machine learning (ML) model to predict mid-term (3 years) stone recurrence (SR) after percutaneous nephrolithotom...

Transformer-based deep learning model for real-time prediction of intraoperative hypotension using dynamic time-series vital signs: A retrospective study.

BACKGROUND: The clinical importance of transient intraoperative hypotension (IOH) remains debated, and existing models often rely on high-resolution w...

Artificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review.

Artificial intelligence (AI) has been transforming many aspects of medical care. In prostate cancer, ongoing progress in AI has improved research and ...

International Delphi consensus on acute kidney injury: Foundations for AI-driven digital twin development in critical care nephrology.

BACKGROUND: Acute kidney injury (AKI) in critically ill patients is clinically complex and heterogeneous, limiting the development of structured simul...

Integrating Artificial Intelligence into Prostate MR Imaging: Technical Foundations, Clinical Applications, and Workflow Implications.

Prostate MRI has become a cornerstone of contemporary prostate cancer diagnosis, enabling improved detection of clinically significant disease while r...

AI-discovered cellular morphometric biomarkers in needle biopsy of prostate cancer predict neoadjuvant androgen deprivation therapy response and enable therapeutic targeting of mTOR in androgen deprivation therapy-resistant tumors.

It is imperative to identify patients with prostate cancer (PCa) who will not benefit from androgen receptor signaling inhibitors and to improve their...

Large-scale plasma proteomics for predicting future cardiovascular and all-cause mortality among individuals with cardiovascular-kidney-metabolic syndrome stage 0-3.

BACKGROUND: Identifying high-risk individuals for cardiovascular and all-cause mortality among individuals with cardiovascular-kidney-metabolic (CKM) ...

Integrating liquid biopsies and artificial intelligence for early cancer detection: A systematic review and meta-analysis.

INTRODUCTION: The latest generation of liquid biopsies incorporates multi-omic features, including genomics, methylomics, and fragmentomics. Machine l...

Development and validation of an interpretable machine learning model integrating inflammatory blood biomarkers for predicting postoperative breast cancer-related lymphedema: A prospective cohort study.

BACKGROUND: Breast cancer-related lymphedema is a frequent and debilitating complication. Early identification of high-risk individuals is critical, y...

Overall risk of cancer incidence attributable to adult body CT examinations: impact of a seven-year continuous quality improvement program.

OBJECTIVES: To estimate the impact of a continuous dose reduction and quality improvement program on radiation-induced cancer risk in adult computed t...

Artificial intelligence and radiomics in bladder cancer MRI: a scoping review of applications, performance, and barriers to clinical translation.

Artificial intelligence (AI) and radiomics show significant potential to augment bladder cancer (BC) MRI but face a critical translational gap. This s...

A Comprehensive Pipeline for Electric-Field-Guided Transcranial Magnetic Stimulation Targeting and Optimization.

OBJECTIVE: Transcranial magnetic stimulation (TMS), as a non-invasive means of neuromodulation, plays a crucial role in rehabilitation. Recent studies...

Using artificial intelligence to predict sexual health outcomes in endometriosis: a decision tree model algorithm.

BACKGROUND: Endometriosis profoundly impairs sexual function through complex interactions between pain, hormonal disturbances, psychological distress,...

Severe COVID-19 in the Republic of Korea: Epidemiology, Risk Factors, Therapeutics, and Prognostic Models From Nationwide Data.

Severe coronavirus disease 2019 (COVID-19) has posed ongoing clinical and public health challenges worldwide, with Korea providing a unique perspectiv...

Heat and hearts: An exposure-anchored computational phenotyping framework for assessing cardiovascular vulnerability during extreme heat.

OBJECTIVE: Extreme heat is associated with increased cardiovascular vulnerability. We developed and validated a heat exposure-anchored cardiovascular ...

Probing uric acid-related prognostic genes and their molecular mechanisms in prostate cancer based on transcriptomic data.

Recent studies highlight the role of uric acid in tumor development, but its impact on prostate cancer (PCa) remains underexplored. This study aimed t...

LLM-powered prostate cancer staging from PSMA-PET/CT reports using PROMISE v2.

Accurate staging of prostate cancer is essential for guiding therapy and predicting outcomes. Prostate-specific membrane antigen (PSMA) PET/CT has bec...

Can ensemble methods improve predictive performance of existing models estimating chronic kidney disease among patients with diabetes?

BACKGROUND: Clinical prediction models often suffer from poor model transportability and/or subgroup performance resulting from using a single data so...