Lymphoma

Denoising of 4D dynamic PET images using spatiotemporal regularization with integrated temporal restoration (SPRINTER).

PURPOSE: High-quality 4D dynamic PET imaging is often compromised by noise, especially in low-count frames, which limits clinical utility and quantitative accuracy. This study proposes a novel spatiotemporal denoising method (SPRINTER) that integrates anatomical priors, self-supervised adaptive principal component analysis (aPCA), and deep learning to enhance image quality and preserve time activi...

Human serum albumin profiling by top-down analysis enables multi-class liver fibrosis staging: a cross-platform validation study.

Chronic liver disease (CLD) affects millions worldwide, yet accurately staging its progression without liver biopsy remains a major clinical challenge. Human serum albumin (HSA), the most abundant blood protein synthesized exclusively by the liver, undergoes measurable structural modifications as liver disease advances, making it a potential molecular marker of disease severity. Using high-resolut...

Grey wolf optimized neural network with hybrid feature extraction for heart disease prediction.

With an increasing mortality rate due to heart diseases, there is a critical need for early and reliable cardiovascular disease prediction. However, w...

Deep integration of clinical metadata with [18F]FDG PET/CT imaging for histological subtyping in non-small cell lung cancer: a multi-center study.

PURPOSE: To develop and validate a multimodal deep learning framework that integrates clinical metadata with [18F]FDG PET/CT imaging to resolve overla...

Single-cell transcriptomic analysis reveals the T lymphocyte microenvironment and identifies HOPX as a hub gene in membranous nephropathy.

BACKGROUND: Membranous nephropathy (MN) is an autoimmune disease characterized by immune complex deposition and progressive renal function impairment....

Repeatability of AI-quantified incidental findings on lung cancer screening CT scans in the NELSON trial.

OBJECTIVES: Computed tomography (CT) scans for lung cancer screening provide the opportunity of quantifying incidental findings. We evaluated the repe...

Application of Hi-C sequencing to detect oncogene rearrangements for diagnosis and treatment of large B-cell lymphoma.

Diffuse large B-cell lymphoma (DLBCL), the most common type of lymphoma, arises from various pathogenic mechanisms including gene translocations and f...

Total monomeric anthocyanin concentration estimation of red wines based on color data using multivariate statistical analysis and machine learning.

BACKGROUND: Red wine is a high-value grape derivative containing complex chemical compounds that dictate its quality. Qualitative and quantitative det...

Filling the Gaps: Generating 4D Dense Cardiac Anatomy from Sparse CMR for Enhanced Tetralogy of Fallot Assessment.

BACKGROUND: Accurate assessment of 3D four chamber cardiac anatomy is essential for managing repaired Tetralogy of Fallot (rToF), yet standard cardiac...

Investigating AD and non-AD [18F]flortaucipir distribution patterns in a memory clinic cohort.

PURPOSE: Amyloid (A) deposition represents a specific pathological hallmark of Alzheimer's disease (AD). Clinical diagnostic protocols frequently rely...

A radio-pathological fusion model for predicting PD-L1 expression and immunotherapy response in non-small cell lung cancer.

OBJECTIVE: This study aims to construct a multimodal fusion model (FM) based on CT and hematoxylin and eosin (H&E) stained slices to predict the PD-L1...

Multimodal multitask deep learning for grading management system in non-small cell lung cancer.

Accurate histologic subtyping, tumor node metastasis classification (TNM) staging and prognostic assessment are central to clinical management of non-...

Non-invasive imaging diagnostic techniques in metabolic dysfunction-associated steatotic liver disease: a roadmap for clinicians.

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously termed nonalcoholic fatty liver disease (NAFLD), is the most prevalent ch...

Comparative machinability and neural network-driven optimization of AISI D2 steel using chamfered and conventional tooling.

Dry turning of AISI D2 steel requires a balance between productivity, surface integrity, thermal loading, and energy demand. This study compares the m...

Development of a prediction model for infectious mononucleosis using machine learning algorithms based on blood cell analysis parameters.

BACKGROUND: Infectious mononucleosis (IM) presents with nonspecific clinical manifestations, leading to frequent misdiagnosis or delayed diagnosis, an...

DVNDTA: a dual virtual node based heterogeneous interaction model for accurate prediction of drug-target affinity.

Efficient prediction of drug-target affinity (DTA) is crucial for accelerating drug discovery. Recently, deep learning approaches leveraging 3D comple...

Comprehensive framework for evaluation of deep neural networks in detection and quantification of lymphoma from PET/CT images: Clinical insights, pitfalls, and observer agreement analyses.

PURPOSE: This study addresses critical gaps in automated lymphoma segmentation from PET/CT imaging, often overlooked in prior work. While deep learnin...

New Frontiers of Skin Ultrasound in Precision Medicine.

Ultrasound has emerged as a versatile, non-invasive imaging technique in dermatology, offering real-time, high-resolution visualization of cutaneous s...

Performance of GPT-based large language models in hepatocellular carcinoma stratification: liver function assessment, BCLC staging, and treatment recommendations.

Large language models (LLMs) like GPT have been proposed to support complex clinical decision-making. This study evaluated the performance of GPT-base...

Interpretable machine learning distinguishes skip from continuous metastasis in N1b papillary thyroid carcinoma.

Skip metastasis-defined as lateral lymph node metastasis(N1b) in the absence of central lymph node involvement-represents a distinct yet underrecogniz...