Lymphoma

An AI-Based Solution for Denoising Fast-Acquisition [18F]FDG PET: Clinical Feasibility and Quantitative Assessment.

Benefits in patient comfort, efficiency, and sustainability can come from reducing positron emission tomography (PET) scan's acquisition duration. This study assesses the clinical adequacy of restoring fast-acquisition 18F-fluorodeoxyglucose ([18F]FDG) PET to its standard-of-care image quality through deep-learning-based (DL) methods. Fast and standard whole-body [18F]FDG PET acquisitions of 117 o...

Digital versus analogue PET in parathyroid imaging: comparison of PET metrics and machine learning-based characterisation of hyperfunctioning lesions (the DIGI-PET study).

PURPOSE: To compare PET-derived metrics between digital and analogue PET/CT in hyperparathyroidism, and to assess whether machine learning (ML) applied to quantitative PET parameters can distinguish parathyroid adenoma (PA) from hyperplasia (PH). METHODS: From an initial multi-centre cohort of 179 patients, 86 were included, comprising 89 PET-positive lesions confirmed histologically (74 PA, 15 PH...

Spatial imaging features derived from SUVmax location in resectable NSCLC are associated with tumor aggressiveness.

PURPOSE: Accurate non-invasive prediction of histopathologic invasiveness and recurrence risk remains a clinical challenge in resectable non-small cel...

Development and validation of a transformer-based deep learning model for predicting distant metastasis in non-small cell lung cancer using 18FDG PET/CT images.

BACKGROUND: This study aimed to develop and validate a hybrid deep learning (DL) model that integrates convolutional neural network (CNN) and vision t...

Fingernail-based metabolomics reveals a stepwise decline in dodecanoic acid associated with Alzheimer's disease progression.

BACKGROUND: Fingernail metabolomics provides a novel, non-invasive platform that captures long-term biochemical fluctuations for identifying reliable ...

Predicting Sleep and Sleep Stage in Children Using Actigraphy and Heartrate via a Long Short-Term Memory Deep Learning Algorithm: A Performance Evaluation.

Children's ambulatory sleep is commonly measured via actigraphy. However, traditional actigraphy measured sleep (e.g., Sadeh algorithm) struggles to p...

Machine learning-driven programmed cell death signature for prognosis and drug candidate discovery in diffuse large B-cell lymphoma: Multi-cohort study and experimental validation.

BACKGROUND: Relapse and drug resistance are major contributor to chemotherapy failure in diffuse large B-cell lymphoma (DLBCL). Programmed cell death ...

Shape matters: inferring the motility of confluent cells from static images.

Cell motility in dense cell collectives is pivotal in various diseases like cancer metastasis and asthma. A central aspect in these phenomena is the h...

Multiple omics-based machine learning reveals peripheral blood immune cell landscape during acute rejection of kidney transplantation and constructs a precise non-invasive diagnostic strategy.

Kidney transplantation is the optimal treatment for end-stage renal disease (ESRD), but acute rejection (AR) remains a major factor affecting graft su...

Computational modelling of aggressive B-cell lymphoma.

Decades of research into the molecular signalling determinants of B cell fates, and recent progress in characterising the genetic drivers of lymphoma,...

Simultaneous determination of 7 thiols associated proteins in lymphoma patients'serum and cerebrospinal fluid by UHPLC-HRMS technique.

Thiol compounds can serve as markers for the antioxidant and prognostic status of lymphoma, playing a crucial role in early tumor diagnosis. However, ...

Data-driven approach to the deep learning of the dynamics of a non-integrable Hamiltonian system.

The dynamics of non-integrable Hamiltonian systems, described by area-preserving mappings, are regulated by the KAM theorem. This states that the phas...

Identification of exosome-related genes in NSCLC via integrated bioinformatics and machine learning analysis.

Exosomes are crucial in the development of non-small cell lung cancer (NSCLC), yet exosome-associated genes in NSCLC remain insufficiently explored. T...

LncRNAs regulates cell death in osteosarcoma.

Despite improvements, prognosis in osteosarcoma patients remains poor, making it essential to identify additional and more robust therapeutic targets....

A novel double machine learning approach for detecting early breast cancer using advanced feature selection and dimensionality reduction techniques.

In this paper, three Double Machine Learning (DML) models are proposed to enhance the accuracy of breast cancer detection using machine learning techn...

Automatic cell classification and quantification with machine learning in immunohistochemistry images.

The incidence of lymphoma, a cancer that affects both humans and animals, has witnessed a significant increase. In response, immunohistochemistry (IHC...

Prediction of axillary lymph node metastasis in triple negative breast cancer using MRI radiomics and clinical features.

To develop and validate a machine learning-based prediction model to predict axillary lymph node (ALN) metastasis in triple negative breast cancer (TN...

Brain-wide input-output analysis of tuberal nucleus somatostatin neurons reveals hierarchical circuits for orchestrating feeding behavior.

Feeding is an innate behavior critical for survival but is also influenced by many non-nutritional factors such as emotion, social context and environ...

A novel UHPLC-HRMS method for simultaneous determination of 20 amino metabolites and proteins in lymphoma patients' cells and serum.

Highly sensitive and selective monitoring of amino metabolites such as glutamine, arginine, tryptophan and related proteins played significant roles i...

Perilesional dominance: radiomics of multiparametric MRI enhances differentiation of IgG4-Related ophthalmic disease and orbital MALT lymphoma.

BACKGROUND: To develop and validate a diagnostic framework integrating intralesional (ILN) and perilesional (PLN) radiomics derived from multiparametr...