Endocrinology

Prediction of Central Lymph Node Metastasis in Papillary Thyroid Carcinoma Using Multi-Ultrasound Radiomics of Intratumoral and Peritumoral Regions.

OBJECTIVE: The aim of this study was to integrate multimodal intratumoral and peritumoral ultrasound radiomic features with clinical data to construct a predictive model for central lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) and to explore its clinical utility. METHODS: A total of 470 PTC patients who underwent 2-dimensional (2D) ultrasound, color Doppler flow ...

Development and Validation of a Multimodal-Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR-Positive Early Breast Cancer.

Late distant recurrence (DR) remains a persistent risk in hormone receptor-positive (HR+) early breast cancer after completion of 5 years of endocrine therapy. We developed and validated a multimodal artificial intelligence (AI) model to improve long-term risk stratification and to explore heterogeneity in benefit from extended letrozole therapy (ELT). The deep learning model integrating digitized...

Machine-learning-guided control of a pulsed cold atmospheric plasma jet reveals a voltage-dependent discharge transition relevant to diabetic wound healing.

Cold atmospheric plasma (CAP) technology is promising for biomedical treatment, but its translation is limited by the difficulty of identifying operat...

Radiation-induced erectile dysfunction in prostate cancer: A systematic review of pathophysiology, clinical radiobiology, and predictive modeling.

BACKGROUND: Radiation-induced erectile dysfunction (RIED) remains one of the most prevalent and distressing late toxicities among prostate cancer surv...

Current Perspectives on Thyroid Nodule Evaluation: Integrating Cytomorphology, Ultrasound Risk Stratification, and Molecular Testing.

Fine-needle aspiration biopsy (FNAB) remains the cornerstone of preoperative evaluation of thyroid nodules and represents one of the most enduring and...

Identification of genomic features that uniquely impact estrogen receptor alpha binding and its effects on gene expression in endometrial cancer.

Estrogen receptor 1 (ESR1) is an established oncogenic transcription factor in breast and endometrial cancer; however, more is known about the mechani...

Multimodal Deep Fusion of Ultrasound Images and Clinical Factors for Pre-operative Prediction of Lateral Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma.

OBJECTIVE: Lateral cervical lymph node metastasis (LLNM) is missed in up to 30% of papillary thyroid carcinoma (PTC) patients pre-operatively, leading...

Interpretable diabetes risk prediction: a comparative study of tree-based algorithms using SHAP and LIME.

The global burden of diabetes mellitus continues to increase, particularly in low- and middle-income countries. Prior studies have explored the use of...

Aging-driven transcriptional programs in diabetic kidney disease: multi-omics discovery of diagnostic biomarkers and drug-repurposing targets.

BACKGROUND: Diabetic kidney disease (DKD) is the primary global cause of end-stage renal disease. However, the aging-related gene networks driving its...

Natural language processing identifies symptoms predicting complete coronary artery occlusion in patients with NSTEMI.

AIMS: One in 10 patients present to the emergency department (ED) with symptoms of acute coronary syndrome (ACS). The 13-item ACS Symptom Checklist is...

A hybrid model for early diagnosis of ophthalmology diseases leveraging CNNs, SBOA optimization, and XAI for visualization.

Ophthalmology diseases are among the leading causes of vision loss worldwide. Glaucoma, diabetic retinopathy, and cataracts are the most common diseas...

A Generative Multi-modal Deep Learning Framework for Non-invasive Prediction of Ki-67 Expression in Papillary Thyroid Microcarcinoma Using Preoperative Ultrasound.

Accurate assessment of Ki-67 expression plays a critical role in the risk stratification and prognosis of papillary thyroid microcarcinoma (PTMC). How...

Machine Learning Analysis of Sex Differences in Cardiovascular-Kidney-Metabolic Risk Factors and Prognosis Among Patients With Moderate-to-Severe Coronary Artery Calcification: Prospective Cohort Study.

BACKGROUND: Patients with coronary artery calcification exhibit notable sex differences in clinical presentation, particularly concerning the role of ...

Identifying sex-specific sub-phenotypes of Alzheimer's disease progression using longitudinal electronic health records.

BACKGROUND: Alzheimer's Disease (AD) is a complex neurodegenerative disorder, with women comprising nearly two-thirds of individuals with AD. However,...

The Triglyceride Glucose-a Body Shape Index: A Novel Composite Index for Prediction of Periodontitis Risk.

INTRODUCTION AND AIM: Insulin resistance and obesity are significant metabolic risk factors for periodontitis. This study aimed to systematically inve...

Machine learning evaluation of clinical, social and behavioural factors influencing progression from pre-diabetes to type 2 diabetes: a retrospective cohort study in southeast Michigan.

OBJECTIVE: We aimed to use machine learning (ML) models to investigate the impact of clinical, social and behavioural factors on 1-year progression fr...

Machine learning-based model for identifying liver injury in patients with thyroid-associated ophthalmopathy.

PURPOSE: To explore the relationship between thyroid-associated ophthalmopathy (TAO) and liver injury and establish a model for identifying liver inju...

RCAF for patient-level thyroid ultrasound malignancy prediction under leakage-free evaluation and calibration.

Accurate differentiation between benign and malignant thyroid nodules on ultrasound remains clinically important, yet interpretation is operator-depen...

Causal association and shared mechanisms between Graves' disease and prostate cancer: insights from Mendelian randomization, machine learning, and comprehensive bioinformatics.

BACKGROUND: Observational studies link hyperthyroidism to increased prostate cancer (PCa) risk, but causality and mechanisms remain unclear. Graves' d...

Machine Learning-Based Early Risk Stratification for Sepsis-Related Troponin-Defined Myocardial Injury Using Routine Metabolic Indicators.

BACKGROUND: Early recognition of sepsis-related myocardial injury during sepsis remains difficult, partly because harmonized echocardiographic phenoty...