Lymphoma

Protective Dietary Antioxidants Intake Attenuate Cardiovascular-Kidney-Metabolic Syndrome Progression

Cardiovascular-kidney-metabolic (CKM) syndrome is characterized by complex pathophysiological interactions among cardiovascular diseases, and chronic kidney disease. Although there is evidence linking dietary antioxidants to the reduction of oxidative stress, comprehensive studies investigating relationships between CKM syndrome and antioxidants remain limited. This cross-sectional study analyzed ...

Large Language Models Improve Cancer Survival Prediction Using Real-World Clinical Notes

In medical documentation, vast amounts of unstructured text are generated that are still underutilized in current prognostic models. We investigate the potential of self-hosted large language models (LLM) to extract clinically meaningful, patient-specific information from routine clinical notes for personalized risk stratification in cancer care. We collected real-world medical notes from 2,708 no...

Non-Traditional Lipid Ratios Predict Cardiovascular-Kidney-Metabolic Syndrome: Insights from Machine Learning Model Using NHANES Data

Cardiovascular-kidney-metabolic (CKM) syndrome is a newly defined multisystem disease continuum characterized by the coexistence of metabolic dysfunct...

Glomerular Segmentation, Classification, and Pathomic Feature-based Prediction of Clinical Outcomes in Minimal Change Disease and Focal Segmental Glomerulosclerosis

Conventional assessment of Focal Segmental Glomerulosclerosis and Minimal Change Disease focuses on the presence/extent of segmental (SS) and global (...

Non-contiguous Computed Tomography Lung Scans Can be Manipulated to Permit Artificial Intelligence Analyses for Interstitial Lung Disease in Systemic Sclerosis

Artificial Intelligence can analyse high resolution CT lung scans (HRCT) in various interstitial lung diseases (ILD) including Systemic Sclerosis (SSc...

Combined High-Resolution MRSI and [18F]-FACBC PET to Improve the Presurgical Diagnostic Accuracy in Gliomas

Medical imaging is crucial for glioma management. Combined with MRI, amino acid PET may improve glioma diagnosis, biopsy targeting, and tumor delineat...

Large language models in radiologic numerical tasks: A thorough evaluation and error analysis

To investigate the performance of LLMs in radiology numerical tasks and perform a comprehensive error analysis. We defined six tasks: extracting 1-min...

Artificial Intelligence-assisted reader evaluation in acute CT head interpretation (AI-REACT): a multireader multicase study

Non-contrast CT head scans (NCCTH) are the most frequently requested cross-sectional imaging in the Emergency Department. While AI tools have been dev...

Patient-Reported Challenges in Lymphoma Diagnosis: Analysis of Online Forum Narratives Using Artificial Intelligence

Lymphoma diagnosis remains challenging due to diverse subtypes and nonspecific presentations. While prior research focused primarily on clinical accur...

Optimizing Lightweight Medical AI for Chest CT Classification: A Distillation and Quantization Approach

Medical imaging has been crucial in the diagnostics of pulmonary diseases and the use of chest CT scans is a fundamental diagnostic tool in lung cance...

Accurate, Race-Free LDL-C Estimation in Non-Fasting Settings: A Machine-Learning Study in 3,477 Adults

Traditional LDL-C testing barriers—mandatory 9–12 hour fasting and inperson visits—disproportionately limit access for rural populations (60% of US co...

Data Extraction from Oncology Imaging Reports by Large Language Models: A Comparative Accuracy Study

Manual data extraction from clinical text is resource intensive. Locally hosted large language models (LLMs) may offer a privacy-preserving solution, ...

Operational Survival Deficit of Neoadjuvant Chemotherapy in Early-Stage Breast Cancer: A Target Trial Emulation and Causal Machine Learning Study

Neoadjuvant chemotherapy (NAC) is the standard of care for locally advanced breast cancer. However, the disconnect between efficacy in randomized tria...

Identification of Risk Factors for Glaucoma Progression in Free-Text Clinical Notes using a Local Large Language Model

To evaluate the performance of a large language model (LLM) in identifying medication non-adherence, visit non-adherence, and family history of glauco...

Machine Learning-Based Prediction of Cell-type Resolved Brain eQTLs Enhances Discovery of Variants Explaining Alzheimer’s Disease Heritability

The majority of causal genome-wide association studies (GWAS) variants for Alzheimer’s disease (AD) are believed to reside in noncoding regions of the...

Deep learning enhanced magnetocardiography enables multi-task detection of coronary, ventricular, and rhythm disorders

Magnetocardiography (MCG) captures the magnetic fields generated by myocardial currents, theoretically preserving electrophysiological details lost in...

Exploring the suitability of piecewise-linear dynamical system models for cognitive neural dynamics.

Dynamical system models have proven useful for decoding the current brain state from neural activity. So far, neuroscience has largely relied on eithe...

Recent progress in tuberculosis diagnosis: insights into blood-based biomarkers and emerging technologies.

Tuberculosis (TB) remains a global health challenge, with timely and accurate diagnosis being critical for effective disease management and control. R...

Comparison of Different Machine Learning Methodologies for Predicting the Non-Specific Treatment Response in Placebo Controlled Major Depressive Disorder Clinical Trials.

Placebo effect represents a serious confounder for the assessment of treatment effect to the extent that it has become increasingly difficult to devel...

Single and Multi-Objective Optimization of the Red Pine Mushroom Lactarius deliciosus (Agaricomycetes) Extraction Conditions Using Artificial Intelligence Methods and Biological Activities of Optimized Extracts.

In this study, the biological activities of Lactarius deliciosus were determined. Experimental studies were carried out using a soxhlet device, in the...