Lymphoma

MRI In Vivo Detection of Amyloid-β Protein Deposition in Different Brain Regions of Patients with AD and MCI.

To investigate a non-invasive magnetic resonance imaging (MRI)-based method for detecting amyloid-β (Aβ) protein deposition in different brain regions of patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). This study included 80 patients with MCI and 62 patients with AD, who were randomly divided into training and testing sets at an 8:2 ratio. All participants underwent 18 ...

Early electrocardiographic repolarization changes are associated with subclinical cancer therapy-related cardiac dysfunction in lymphoma patients: A machine learning-assisted longitudinal study.

UNLABELLED: Anthracycline-induced cardiotoxicity remains a significant clinical challenge. We evaluated longitudinal electrocardiographic (ECG) repolarization changes in 36 lymphoma patients receiving doxorubicin-based chemotherapy and explored their association with subclinical cancer therapy-related cardiac dysfunction (CTRCD) using an exploratory machine learning-assisted approach. Standard 12‑...

Neutrophil extracellular trap-related genes in PTCL: identification, prognosis and drug interaction prediction via bioinformatics-machine learning.

OBJECTIVE: This study aimed to identify neutrophil extracellular trap-related genes (NET-RGs), explore their prognostic significance, and predict drug...

18F-FDG PET/CT and receptor-positive circulating tumor cells-based machine learning model for predicting poorly differentiated lung adenocarcinoma.

INTRODUCTION: This study aimed to develop and validate a machine learning model that integrates radiomic features from 2-[18F]fluoro-2-deoxy-D-glucose...

Advances and challenges of PET imaging in bladder cancer-An update and future trends.

Molecular imaging with positron emission tomography (PET) is a powerful tool in the clinical management of bladder cancer, providing functional inform...

Chest Computed Tomography-Based Radiomics and Machine Learning for Classifying Mediastinal Lymphadenopathy Caused By Hematologic Malignancies and Metastatic Abdominopelvic Solid Cancers.

PURPOSE: To evaluate the role of chest CT radiomics in classifying mediastinal lymphadenopathy caused by hematologic malignancies and abdominopelvic s...

Metabolic liver imaging: What you need to know.

Metabolic liver diseases represent a growing global health concern with significant diagnostic and prognostic implications. Imaging offers non-invasiv...

Automated RECOMIA AI-based total metabolic tumor volume in lymphoma - a retrospective study.

Current Status and Future Perspective for Bladder Cancer MR Imaging and the Vesical Imaging-Reporting and Data System (VI-RADS) in Japan: Challenges and Solutions.

Bladder cancer carries one of the highest lifetime costs among malignancies, and accurate distinction between non-muscle-invasive and muscle-invasive ...

Radiopharmaceuticals: Status, Regulatory Landscape and Future Perspective.

Radiopharmaceuticals are biologically active molecules labeled with radionuclides that have advanced the possibility of the nuclear medicine. They sup...

Attenuation correction of cardiac 82Rb pet using deep learning generated synthetic CT.

Ischemic heart disease remains a leading cause of mortality worldwide. Myocardial perfusion imaging (MPI) using Rubidium-82 (82Rb) positron emission t...

Development and validation of interpretable multimodal clinical-radiomics models for predicting epileptogenic foci and surgical outcomes in tuberous sclerosis complex: A multicenter study.

Precise localization and resection of epileptogenic (epi) foci from multiple cortical foci determine surgical outcomes in the tuberous sclerosis compl...

Targeting signaling pathways in lymphoma: From molecular mechanisms to clinical breakthroughs.

The treatment paradigm for lymphoma, a highly heterogeneous group of hematologic malignancies, has been revolutionized by the development of therapies...

Analysis of federated learning on non-independent and identically distributed sleep data.

We investigate the application of Federated Learning (FL) across heterogeneous, non-independent and identically distributed (non-IID) sleep data. We e...

Deep Learning-Based Diagnostic Model for Ocular Surface Neoplastic Diseases.

PURPOSE: To develop a deep learning (DL) model for diagnosing ocular surface tumors and evaluating its diagnostic performance. SETTING: Development of...

An AI-based IHC quantification technique for assisting in the differentiation of MCL from CLL/SLL.

This study explores the application of artificial intelligence technology for the quantitative analysis of immunohistochemical markers to differentiat...

Environmental PET-microplastic exposure and risk of non-alcoholic fatty liver disease: An integrated computational toxicology and multi-omics study.

The escalating severity of global microplastic pollution has triggered significant public health concerns. Polyethylene terephthalate (PET), a ubiquit...

Feasibility of two-dimensional multi-segmented late gadolinium enhancement combined with artificial intelligence reconstruction deep-learning noise reduction in patients with non-ischemic cardiomyopathy.

OBJECTIVE: Although two-dimensional (2D) single-segmented late gadolinium enhancement (2D-SSLGE) sequences are the gold standard for LGE acquisition, ...

End-to-end integrative segmentation and radiomics prognostic models for risk stratification of high-grade serous ovarian cancer: a retrospective multicohort study.

BACKGROUND: Valid stratification factors for patients with epithelial ovarian cancer are still lacking and individualisation of care remains an unmet ...

Accelerating MRI With Longitudinally-Informed Latent Posterior Sampling.

PURPOSE: To accelerate MRI acquisition by incorporating the previous scans of a subject during reconstruction. Although longitudinal imaging constitut...