Urology

Metabolomic machine learning predictor for arsenic-associated hypertension risk in male workers.

Arsenic (As)-induced hypertension is a significant public health concern, highlighting the need for early risk prediction. This study aimed to develop a predictive model for occupational As exposure and hypertension using metabolomics and machine learning. A total of 365 male smelting workers from southern regions were selected. Forty workers from high and low urinary arsenic (U-As) exposure group...

Real-Time Dose-Guided Radiation Therapy.

Dramatic strides have been made in real-time adaptive radiation therapy, where treating single tumors as dynamic but rigid bodies has demonstrated a halving of toxicities for prostate cancer. However, the human body is much more complex than a rigid body. This review explores the ongoing development and future potential of dose-guided radiation therapy, where the three core process steps of volume...

DeepPSA: A Geometric Deep Learning Model for PROTAC Synthetic Accessibility Prediction.

Proteolysis-targeting chimeras (PROTACs) have garnered significant attention in drug design due to their ability to induce the degradation of the targ...

MeD-3D: A Multimodal Deep Learning Framework for Precise Recurrence Prediction in Clear Cell Renal Cell Carcinoma (ccRCC)

Accurate prediction of recurrence in clear cell renal cell carcinoma (ccRCC) remains a major clinical challenge due to the disease complex molecular...

Segmentation Regularized Training for Multi-Domain Deep Learning Registration applied to MR-Guided Prostate Cancer Radiotherapy

Background: Accurate deformable image registration (DIR) is required for contour propagation and dose accumulation in MR-guided adaptive radiotherap...

DpDNet: An Dual-Prompt-Driven Network for Universal PET-CT Segmentation

PET-CT lesion segmentation is challenging due to noise sensitivity, small and variable lesion morphology, and interference from physiological high-m...

Mitigating Multi-Sequence 3D Prostate MRI Data Scarcity through Domain Adaptation using Locally-Trained Latent Diffusion Models for Prostate Cancer Detection

Objective: Latent diffusion models (LDMs) could mitigate data scarcity challenges affecting machine learning development for medical image interpret...

Prompt-Free Conditional Diffusion for Multi-object Image Augmentation

Diffusion models has underpinned much recent advances of dataset augmentation in various computer vision tasks. However, when involving generating m...

Self-supervised Deep Learning for Denoising in Ultrasound Microvascular Imaging

Ultrasound microvascular imaging (UMI) is often hindered by low signal-to-noise ratio (SNR), especially in contrast-free or deep tissue scenarios, w...

Patient-Centered RAG for Oncology Visit Aid Following the Ottawa Decision Guide

Effective communication is essential in cancer care, yet patients often face challenges in preparing for complex medical visits. We present an inter...

Beyond Olfaction: New Insights into Human Odorant Binding Proteins

Until today, the exact function of mammalian odorant binding proteins (OBPs) remains a topic of debate. Although their main established function lac...

Hybrid-View Attention Network for Clinically Significant Prostate Cancer Classification in Transrectal Ultrasound

Prostate cancer (PCa) is a leading cause of cancer-related mortality in men, and accurate identification of clinically significant PCa (csPCa) is cr...

Hybrid-View Attention for csPCa Classification in TRUS

Prostate cancer (PCa) is a leading cause of cancer-related mortality in men, and accurate identification of clinically significant PCa (csPCa) is cr...

Fermi calculations enable quick downselection of target genes and process optimization in photosynthetic systems.

Understanding how photosynthetic organisms including plants and microbes respond to their environment is crucial for optimizing agricultural practices...

Combining Spatial Transcriptomics, Pseudotime, and Machine Learning Enables Discovery of Biomarkers for Prostate Cancer.

UNLABELLED: Early cancer diagnosis is crucial but challenging owing to the lack of reliable biomarkers that can be measured using routine clinical met...

MACHINE LEARNING AND BIOINFORMATICS TO IDENTIFY COAGULATION BIOMARKERS IN SEPSIS-RELATED KIDNEY INJURY.

Background: Sepsis-associated acute kidney injury (SA-AKI) is a life-threatening complication with mortality rates exceeding 50%, yet its molecular dr...

Quantitative CT biomarkers for renal cell carcinoma subtype differentiation: a comparison of DECT, PCT, and CT texture analysis.

OBJECTIVE: To evaluate and compare the diagnostic performance of CT texture analysis (CTTA), perfusion CT (PCT), and dual-energy CT (DECT) in distingu...

Agreement between Routine-Dose and Lower-Dose CT with and without Deep Learning-based Denoising for Active Surveillance of Solid Small Renal Masses: A Multiobserver Study.

Purpose To assess the agreement between routine-dose (RD) and lower-dose (LD) contrast-enhanced CT scans, with and without Digital Imaging and Communi...

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