Lymphoma

Prediction of Retinopathy of Prematurity and Treatment in Very Low Birth Weight Infants Using Machine Learning on Nationwide Non-Imaging Clinical Data.

INTRODUCTION: Retinopathy of prematurity (ROP) remains a leading cause of preventable blindness in preterm infants. This study aimed to develop machine learning (ML) models using non-imaging clinical data to predict ROP, severe ROP (sROP), and treated ROP (tROP) in very low birth weight (VLBW) infants. METHODS: We utilized nationwide clinical data from the Korean Neonatal Network, including 44 per...

Deciphering the molecular landscape of Sjögren's disease, mucosa-associated lymphoid tissue lymphoma, and thyroid cancer: unraveling the complexities of disease mechanisms and diagnostic biomarkers.

BACKGROUND: Sjögren's disease (SjD), mucosa-associated lymphoid tissue lymphoma (MALT lymphoma), and thyroid cancer (THCA) are clinically distinct yet immunologically intertwined diseases characterized by chronic inflammation and aberrant immune activation. However, the shared molecular basis linking autoimmunity and oncogenesis remains poorly defined. METHODS: We performed an integrative transcri...

Multiomics Profiling of T-cell Leukemia and Lymphoma Enables Targeted Therapeutic Discovery.

UNLABELLED: T-cell leukemias and lymphomas (TCL) form a heterogeneous group of rare and often aggressive malignancies. Because of the rarity and heter...

Multisequence MRI Enables High-Fidelity FDG-PET Synthesis for Epilepsy Using GANs.

BACKGROUND: FDG-PET aids presurgical epilepsy evaluation but is limited by access and radiation exposure. PURPOSE: To evaluate synthetic FDG-PET gener...

Predictive analysis and performance assessment of coal bottom ash in recycled aggregate concrete under elevated temperatures.

This study investigates the viability of utilizing coal bottom ash (BA) as a replacement for natural fine aggregate (NFA) in concrete containing recyc...

Potential link between dioxin induced progression of diffuse large B-cell lymphoma: New insights from machine learning and in vitro experiments.

BACKGROUND: Dioxin has emerged as a major and modifiable determinant of tumor burden worldwide. Building on this premise, we investigated whether diox...

Self-supervised learning on graphs predicts non-coding RNA and disease associations.

Non-coding RNAs (ncRNAs) play crucial roles in regulating the initiation and progression of various cancers. Accurate identification disease-related n...

Harnessing artificial intelligence for the assessment of liver fibrosis and steatosis via multiparametric ultrasound.

Artificial intelligence (AI) is revolutionizing medical imaging, particularly in chronic liver diseases assessment. AI technologies, including machine...

CT-free attenuation and scatter correction of [11C]CFT brain PET using a Bi-directional matching network.

Quantitative PET imaging requires accurate attenuation and scatter correction (ASC), but the standard CT-based method introduces additional radiation ...

Deep learning in enhanced CT imaging: predicting invasion depth of rectal adenocarcinoma.

PURPOSE: This study aims to develop and evaluate a deep learning model, RectoDepthAI, that leverages enhanced CT images to accurately assess the tumor...

Non-invasive Prediction of CYP11B2-Defined Subtypes in Primary Aldosteronism Using 18F-Pentixafor PET/CT and Machine Learning.

PURPOSE: This study aims to develop and validate an interpretable machine learning model that integrates clinical data, radiomics, and deep learning (...

Utilizing artificial intelligence for the diagnosis of ocular surface squamous neoplasia with ultrasound biomicroscopy images.

PURPOSE: This study aims to develop an artificial intelligence (AI) model to assist ophthalmologists in distinguishing ocular surface squamous neoplas...

Markers predicting lymphoma development in Sjögren disease: current status and future perspectives.

We aim to present recent advancements in predictive markers for lymphomagenesis in SjD, concisely organize existing knowledge, and identify correspond...

MRI-to-PET synthesis via deep learning for amyloid-β quantification in Alzheimer's disease.

OBJECTIVES: Amyloid-β (Aβ) PET is crucial for diagnosing and monitoring Alzheimer's disease (AD), but its high cost and radiation exposure limit its u...

From bench to bedside: The clinical relevance of atrial electrical remodeling in atrial fibrillation therapy.

Atrial electrical remodeling spans molecular, electrical, and structural alterations that shorten refractoriness, facilitate reentry, and ultimately c...

Evaluation of Image-Level Harmonization Methods for Multi-Center MR Neuroimaging.

BACKGROUND: Multi-center imaging studies create large-scale data that are useful for identifying pathological patterns and robust training of deep lea...

Instantaneous Abdominal T2* Mapping via Single-Shot MOLED Under Free-Breathing: A Preliminary Study of Hepatic Glycometabolism Imaging.

PURPOSE: To propose a gradient-echo multiple overlapping-echo detachment (GRE-MOLED) method for rapid abdominal T2* mapping, and to systematically val...

Deep learning-based cognitive impairment brain imaging analysis: New methods, new technologies, and new paradigms.

Cognitive impairment arising from ischemic stroke, Alzheimer's disease, and Parkinson's disease presents distinct structural and network-level alterat...

Diagnostic accuracy of ¹⁸F-FDG PET/CT radiomics for non-invasive prediction of PD-L1 expression in non-small cell lung cancer: A systematic review and meta-analysis.

To evaluate the diagnostic performance, methodological quality, and clinical feasibility of ¹⁸F-FDG PET/CT-based radiomics machine learning models for...

cncFinder: A graph-attention-network-based interpretable learning model to identify bifunctional long non-coding RNAs.

Certain RNAs exhibit both protein-coding and regulatory non-coding functions, termed bifunctional RNAs or coding and non-coding RNAs. Long non-coding ...