Urology

Granular Machine Learning-Based Computed Tomography Contrast Phase Prediction.

OBJECTIVE: To develop and evaluate a machine learning framework that detects intravenous contrast and distinguishes eight granular renal contrast phases on abdominal computed tomography (CT) scans to improve renal assessment. PATIENTS AND METHODS: This retrospective study included abdominal CT scans obtained at Mayo Clinic from January 1, 2001, to December 31, 2009. In total, 3033 scans from 1017 ...

Senescence-related gene signature predicts prostate cancer progression and identifies PCNA as a therapeutic target via multi-omics machine learning integration.

BACKGROUND: Senescence plays a critical role in prostate cancer, influencing disease onset and progression. However, the alterations of senescence-associated genes during prostate cancer progression and their potential value in predicting disease advancement remain to be further elucidated. METHODS: 117 machine learning methods were applied to construct the senescence-related gene signature (SRGS)...

Navigating the winding road toward precision prostate cancer care.

Prostate cancer (PCa) remains the second leading cause of cancer-related mortality among U.S. men, driven in large part by metastatic castration-resis...

Dosage adjustments in renal impairment among medical ward patients: ChatGPT® and DeepSeek® models' effectiveness in assessing those adjustments.

BACKGROUND: Due to altered drug clearance, renal impairment necessitates drug dose adjustments to prevent toxicity or therapeutic failure, yet inappro...

Biparametric MRI-Based Habitat Analysis Integrated With Deep Learning for Predicting Clinically Significant Prostate Cancer in PI-RADS Category 3 Lesions.

BACKGROUND: Detection of clinically significant prostate cancer (csPCa) within PI-RADS category 3 lesions remains a major diagnostic challenge. PURPOS...

Literature-informed ensemble machine learning for three-year diabetic kidney disease risk prediction in type 2 diabetes: Development, validation, and deployment of the PSMMC NephraRisk model.

INTRODUCTION: Diabetic kidney disease (DKD) and diabetic nephropathy (DN) affect around 40% of diabetic patients but lack accurate risk prediction too...

Clinical practice, barriers to implementation, and priorities for equitable access of Stereotactic Body Radiation Therapy: An analysis of the global status by the ESTRO SBRT Focus Group.

BACKGROUND: Stereotactic Body Radiation Therapy (SBRT) has become an established treatment for several primary and metastatic malignancies; however, c...

Real-world evidence in metabolic dysfunction-associated steatotic liver disease (MASLD): insights, challenges, and future directions.

Real-world evidence (RWE), derived from real-world data, offers key insights into metabolic dysfunction-associated steatotic liver disease (MASLD). RW...

Multifactor machine learning models for predicting urinary tract infections: a pilot study.

PURPOSE: Vitamin D, a fat-soluble prohormone essential for calcium-phosphate homeostasis and bone health, also regulates innate and adaptive immunity ...

Urinary steroid metabolome shows adrenal, gonadal, and neuroactive steroid dysregulation in adolescents with depressive symptoms.

BACKGROUND: Steroid hormone profiles in affective disorders suggest hypothalamic-pituitary-adrenal (HPA) axis dysregulation and may reveal novel thera...

Application value of prostate-specific antigen density combined with multiparametric MRI in early diagnosis of prostate cancer.

BACKGROUND: Diagnosis of prostate cancer in the PSA gray zone (4-10 ng/mL) and PI-RADS 3 cases remains challenging. Although multiparametric MRI (mpMR...

Developing explainable machine learning models from biochemical and clinical data to predict all-cause and cause-specific mortality in CVD-cancer comorbidity: A longitudinal study based on NHANES.

BACKGROUND: Cardiovascular disease (CVD) and cancer are leading causes of mortality, often coexisting in aging populations. Patients with comorbiditie...

Toxicophore-informed machine learning integrating Tox21 assay readouts for organ system-specific carcinogenicity prediction.

Accurate identification of carcinogenic hazards is essential for public health protection, yet traditional animal-based assays are time-consuming, exp...

Enhancing clinicians' trust in large language models via transparent source attribution: A randomized controlled evaluation in uro-oncology.

INTRODUCTION: Large language models (LLMs) are utilized to answer queries in urology and oncology, yet the performance is limited due to outdated data...

Accelerating Prostate Cancer Detection Through Histopathological Image Analysis Using Artificial Intelligence.

Prostate cancer is a prevalent and serious health concern, ranking among the most frequently diagnosed cancers and a leading cause of cancer-related d...

Explainable machine learning-driven predictive modelling of incident cognitive impairment in individuals with early-stage cardiovascular-kidney-metabolic syndrome: Insights from a longitudinal CHARLS cohort study.

AIMS: While cardiovascular-kidney-metabolic (CKM) syndrome has been recognised as a continuum of interconnected metabolic, renal, and cardiovascular d...

Modern integrative prostate cancer diagnostics.

PURPOSE OF REVIEW: To review contemporary applications, performance, and implementation challenges of artificial intelligence (AI) in the radiological...

Deep learning algorithm assisting diagnosis of prostate cancer extracapsular extension based on [18F]PSMA-1007 PET/CT and multiparametric MRI: A multicenter study.

BACKGROUND: This study aimed to develop and validate deep learning (DL) models based on multiparametric MRI (mpMRI) and [18F]PSMA-1007 PET/CT to predi...

Automated assessment of right heart function by artificial intelligence: A systematic review and meta-analysis.

BACKGROUND: Accurate assessment of right ventricular (RV) size and function is critical for managing cardiac diseases but is challenged by the limitat...

Evaluation of AI for prostate cancer detection in biparametric-MRI screening population data.

OBJECTIVE: The goal of this study was to curate a prostate MRI dataset from a screening population and to train and evaluate a deep-learning segmentat...