Lymphoma

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-invasive alternatives to biopsy for diagnosis, staging, and follow-up. This review summarizes current imaging techniques used in metabolic liver disease, including ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), and advanced mod...

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

Annotation-free prediction of immunotherapy response in melanoma using single-cell transcriptomic data.

Immune checkpoint inhibitors (ICIs) have transformed the advanced melanoma treatment landscape; however, a subset of patients achieve durable response...

A graph neural network-based method to identify lncRNA subcellular localizations.

The subcellular localizations of long non-coding RNAs (lncRNAs) are closely related to their biological functions and disease mechanisms. Existing met...

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...

Plasma neurotransmitter-related metabolite alterations in adolescent major depressive disorder with and without psychotic features.

BACKGROUND: Whether adolescent major depressive disorder (MDD) with psychotic features has a distinct peripheral metabolite signature remains uncertai...

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...

Artificial Intelligence Impact on Radiologist Performance in Breast Cancer Screening with Digital Breast Tomosynthesis.

PURPOSE: Assess impact of artificial intelligence (AI) on radiologists' detection of cancer on digital breast tomosynthesis (DBT) exams based on densi...

A Unified Machine Learning Model for Relapse Prediction in Clinical Stage I Testicular Cancer.

BACKGROUND: Approximately one-fourth of patients with clinical stage I testicular cancer relapse. For decades, risk stratification has been based on d...

Post-treatment infection prediction in CLL using domain adaptation of lymphoma electronic health records.

BACKGROUND AND PURPOSE: Infections are the leading cause of morbidity and mortality in patients with chronic lymphocytic leukemia (CLL) and occur duri...