Lymphoma

The diagnostic accuracy of MRI radiomics in axillary lymph node metastasis prediction: a systematic review and meta-analysis.

BACKGROUND: Breast cancer is the most prevalent malignancy in women and a leading cause of mortality...

Decoding the interactions and functions of non-coding RNA with artificial intelligence.

In addition to encoding proteins, mRNAs have context-specific regulatory roles that contribute to ma...

Accurate prediction of disease-free and overall survival in non-small cell lung cancer using patient-level multimodal weakly supervised learning.

With the rapid progress in artificial intelligence (AI) and digital pathology, prognosis prediction ...

Enhancing non-invasive early detection of gastric cancer: a critical reflection on exosome ncRNA and machine learning.

Large Language Model-Assisted Surgical Consent Forms in Non-English Language: Content Analysis and Readability Evaluation.

BACKGROUND: Surgical consent forms convey critical information; yet, their complex language can limi...

Development and validation of an AI-driven radiomics model using non-enhanced CT for automated severity grading in chronic pancreatitis.

OBJECTIVE: To develop and validate the chronic pancreatitis CT severity model (CATS), an artificial ...

Non-Invasive Diagnosis of Chronic Myocardial Infarction via Composite In-Silico-Human Data Learning.

Myocardial infarction (MI) continues to be a leading cause of death worldwide. The precise quantific...

Assessment of PD-L1 expression and tumour infiltrating lymphocytes in early-stage non-small cell lung carcinoma with artificial intelligence algorithms.

AIMS: To study programmed death ligand 1 (PD-L1) expression and tumour infiltrating lymphocytes (TIL...

Interpretable deep fuzzy network-aided detection of central lymph node metastasis status in papillary thyroid carcinoma.

PURPOSE: The non-invasive assessment of central lymph node metastasis (CLNM) in patients with papill...

Advances in molecular pathology and therapy of non-small cell lung cancer.

Over the past two decades, non-small cell lung cancer (NSCLC) has witnessed encouraging advancements...

Thermodynamics Analysis of a Reaction-Diffusion Matrix Multiplication Computing Unit under the Linear Non-Equilibrium Regime.

Implementations of matrix multiplication via diffusion and reactions, thus eliminating the need for ...

A case study of forensic psychiatry experts' reports analysis through large language models.

The integration of artificial intelligence (AI) technologies in forensic psychiatry has gained signi...

Long-term prognostic value of the CT-derived fractional flow reserve combined with atherosclerotic burden in patients with non-obstructive coronary artery disease.

OBJECTIVES: The long-term prognostic significance of the coronary computed tomography angiography (C...

Computational Histology Artificial Intelligence (CHAI) Enhances Risk Stratification of High-grade Ta Non-muscle-invasive Bladder Cancer in a Multicenter Cohort: Comparison to Current European Association of Urology and American Urological Association Stratification Schemes.

A strategy for the automatic diagnostic pipeline towards feature-based models: a primer with pleural invasion prediction from preoperative PET/CT images.

BACKGROUND: This study aims to explore the feasibility to automate the application process of nomogr...

Non-invasive multi-phase CT artificial intelligence for predicting pre-treatment enlarged lymph node status in colorectal cancer: a prospective validation study.

OBJECTIVES: Benign lymph node enlargement can mislead surgeons into overstaging colorectal cancer (C...

Summary Report of the SNMMI AI Task Force Radiomics Challenge 2024.

In medical imaging, challenges are competitions that aim to provide a fair comparison of different m...

Providing context: Extracting non-linear and dynamic temporal motifs from brain activity.

Approaches studying the dynamics of resting-state functional magnetic resonance imaging (rs-fMRI) ac...

Next-Generation Sequencing-Based Testing Among Patients With Advanced or Metastatic Nonsquamous Non-Small Cell Lung Cancer in the United States: Predictive Modeling Using Machine Learning Methods.

BACKGROUND: Next-generation sequencing (NGS) has become a cornerstone of treatment for lung cancer a...

Non-end-to-end adaptive graph learning for multi-scale temporal traffic flow prediction.

Accurate traffic flow prediction is vital for intelligent transportation systems but presents signif...

Detecting the clinical features of difficult-to-treat depression using synthetic data from large language models.

Difficult-to-treat depression (DTD) has been proposed as a broader and more clinically comprehensive...