Lymphoma

Prediction of MYC/BCL-2 co-expression in diffuse large B-cell lymphoma using a multimodal fusion model: a retrospective study based on PET/CT habitat radiomics and deep learning.

A lightweight CNN for enhanced non-small cell lung cancer classification using CT scan image.

Lung cancer is a leading cause of cancer-related mortality worldwide, and its early and accurate detection is critical for improving patient outcomes. Computed tomography (CT) scans are widely used to diagnose lung cancer; however, the accuracy of diagnosis often depends on the expertise of radiologists. Recently, deep learning-based clinical decision support systems have shown promise in assistin...

Comparative analysis of ANFIS, ANN, and BBD for enhanced prediction of methyl orange adsorption in water treatment.

This work focused on the enhanced prediction of methyl orange removal (MO) from water by activated carbon synthesized from banana peels. Characterizat...

Revolutionizing hepatic fibrosis staging: A machine learning approach combining clinical, biochemical, and microbiome insights.

Non-alcoholic Steatohepatitis (NASH) is a common disease that not only affects adults but has also been seen to affect all ages. This includes young a...

Artificial intelligence for accurate tumor size assessment and non-invasive adenocarcinoma prediction in small-sized lung cancer.

INTRODUCTION: Accurate preoperative imaging is essential for improving the treatment of small lung cancers. Precise identification of non-invasive ade...

[Deep learning-based assessment of periodontal ligament area changes in maxillary central incisors under different orthodontic regimens using cone-beam CT images].

Objective: To investigate the differences in the changes of periodontal ligament area (PDLA) and related clinical indicators before and after maxillar...

AI-based quality control was associated with improved fetal ultrasound image quality in low-resource settings: a real-world multicenter study from West China.

BACKGROUND: Despite rapid advances in medical artificial intelligence (AI), robust evidence for real-world clinical application-particularly in low-re...

Exploring feature importance in machine learning for neuroimaging traits in Alzheimer's disease across a multiethnic cohort.

BackgroundAlzheimer's disease (AD) affects 55 million people worldwide, projected to reach 139 million by 2050; yet, most machine learning (ML)-based ...

Improving MRI-based differentiation of brain metastasis, glioblastoma, and primary CNS lymphoma using deep learning.

Fragmentomic liquid biopsy enables early breast cancer detection, molecular subtyping and lymph node assessment.

Breast cancer remains a leading global health concern in women, while screening is still limited by imaging accessibility and reduced sensitivity in d...

Leveraging Kappa-Lambda Signatures in a Multi-Stage Machine Learning Pipeline for B-Cell Lymphoma Detection by Flow Cytometry.

Flow cytometry immunophenotyping is essential for diagnosing B-cell lymphomas, but manual interpretation of high-dimensional data remains subjective, ...

CAR T-cells in hematologic malignancies: Advances, challenges, and future directions.

Chimeric antigen receptor (CAR) T-cell therapy has transformed the management of hematologic malignancies, achieving high remission rates in relapsed ...

A hybrid RSM-PSO-ANN approach for developing cleaner-label emulsified sausages with roasted Lyophyllum decastes mushroom powder: Optimization, quality, and antioxidant activity.

The development of cleaner-label meat products with reduced fat and enhanced nutritional value is a key trend in the food industry. This study develop...

Impact of Deep Learning-Based Time-of-Flight PET Images of Small Tumors Using a Human Anatomic Phantom.

Time-of-flight (ToF) in PET improves image quality by enhancing the signal-to-noise ratio, and recent deep learning (DL)-based ToF (DL-ToF) methods fu...

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...

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) repola...

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...