Prostate Cancer

Multimodal Artificial Intelligence Prediction of Abiraterone Efficacy in Two STAMPEDE Phase 3 Trials of Non-Metastatic Very High-Risk Prostate Cancer.

BACKGROUND: Long-term androgen deprivation therapy (LT-ADT) with radiotherapy is standard-of-care for high-risk localized prostate cancer, with abiraterone added for clinically very high-risk disease. Given the toxicity and cost of abiraterone, a predictive biomarker to refine patient selection is needed. We evaluated a digital pathology multimodal artificial intelligence (MMAI) model, previously ...

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 prostate cancer. However, its inherent lack of a stable tertiary structure and highly dynamic conformational ensemble pose formidable challenges for rational drug design. This study introduces an integrated computational workflow that combines enhance...

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...

Development and validation of a noninvasive machine learning model using urinary extracellular vesicle physical parameters for prostate cancer diagnosis.

Urinary extracellular vesicles (uEVs) are promising biomarkers for prostate cancer (PCa). Although novel uEV-based biomarkers have advanced early diag...

PGFD-YOLO: A dual-modal object detection framework with progressive gated fusion and foreground-guided distillation.

Effective waste classification is essential for improving resource recovery and reducing landfill burden. However, existing methods predominantly rely...

Representation of Ordinal Features: Supervised Embeddings in the Survival Prediction of Prostate Cancer Patients.

Prostate cancer (PCa) is the most common cancer in men. Treatment decisions for PCa consider factors like age, tumor stage, and grade, along with spec...

Novel biomarkers driving treatment selection in metastatic castration-sensitive prostate cancer: fundamentals of personalized clinical decision making.

INTRODUCTION: Prostate cancer is well known to be androgen-dependent, with growth directly relying on the androgen receptor signaling pathway. In fact...

The evolving PI-RADS paradigm.

Prostate MRI is central to the diagnostic pathway for prostate cancer (PCa), reducing unnecessary biopsies, improving the detection of clinically sign...

Transforming Psoriatic Disease Care Through Innovation and Real-World Data: GRAPPA 2025 Soapbox Highlights.

The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2025 annual meeting, held in Bogotá, Colombia, featured soapbox pr...

A Bioinspired Three-Dimensional High-Curvature Nano-Interface Integrated Microfluidic Chip for Small Extracellular Vesicles Enrichment and Machine Learning-Assisted Prostate Cancer Precision Diagnosis.

The efficient and unbiased isolation of small extracellular vesicles (sEVs) from complex biological fluids remains a major obstacle for clinical diagn...

Semen RNA-Based Biomarkers for Prostate Cancer Detection and Risk Stratification: A Prospective Multi-Center Validation Study.

OBJECTIVE: To assess the diagnostic performance of semen RNA-based biomarkers for detecting prostate cancer and differentiating cancer grade groups. M...

Automated Detection of Patent Ductus Arteriosus in Pediatric Patients Using Doppler Ultrasonography Videos Based on a Transformer Model.

Patent ductus arteriosus (PDA) is a common congenital heart defect that requires timely and accurate detection to guide clinical management. Although ...

Prostate Cancer Detection on Micro-Ultrasound Raw Data Using a Deep Learning Neural Network.

BACKGROUND: Micro-ultrasound (micro-US) is a clinically available novel high-resolution imaging technology for guiding prostate biopsies. However, cli...

Explainable and visualizable machine learning model development and validation for 5-year postoperative survival prediction in prostate cancer patients aged ≥ 65 years.

BACKGROUND: Machine Learning (ML) models have achieved outstanding performance in predicting post-surgical survival. However, the "black-box" nature o...

Urine-Based Noninvasive Detection of Prostate Cancer Using Human Olfactory Receptor-Embedded Nanodiscs.

The diagnosis of prostate cancer (PCa) is limited by the low specificity of prostate-specific antigen (PSA) testing, which contributes to overdiagnosi...

Enhancing Target Recognition Performance in SSVEP-Based Brain-Computer Interfaces via Deep Neural Networks with Pyramid Squeeze Attention.

Steady state visual evoked potential (SSVEP)-based brain-computer interfaces have been widely studied for their fast response speeds and high informat...

Physics-informed deep learning for rapid marine radionuclide dispersion forecasting in nuclear emergency response.

In the event of a severe nuclear accident at a coastal nuclear power plant, the rapid and accurate assessment of radionuclide dispersion in surroundin...

Cost-effectiveness of AI-enhanced breast cancer screening in Iran: a microsimulation study.

BACKGROUND: Recognized worldwide as a major health threat and Sustainable Development Goal priority, breast cancer places an especially heavy clinical...

Web-Based Personalized Machine Learning Recommendations to Enhance Shared Decision-Making in Prostate-Specific Antigen Screening: Randomized Controlled Trial.

BACKGROUND: Prostate‑specific antigen (PSA) screening involves complex trade‑offs between early detection and the risks of overdiagnosis. For older ad...

Using causal machine learning and real world data to improve dose response decision making for secukinumab in psoriatic arthritis.

Personalized treatment in psoriatic arthritis (PsA) remains challenging, particularly in guiding dose escalation decisions. We applied a causal machin...