Lymphoma

Combining computed tomography radiomics and clinical features to predict lymph node metastasis in patients with lung cancer.

BACKGROUND: Accurate preoperative assessment of lymph node metastasis (LNM) is crucial for treatment planning and prognostic stratification in patients with lung cancer. This study aimed to develop and validate a predictive model for LNM using radiomic features derived from non-contrast computed tomography (CT) combined with clinical characteristics. METHODS: A total of 403 patients with pathologi...

Handling distribution shifts on dynamic graphs via causal invariance principles.

Dynamic Graph Neural Networks (DyGNNs) typically assume that training and test data follow an independent and identically distributed assumption. However, in real-world scenarios, the evolving nature of dynamic graph structures violates the assumption, causing classic deep learning models to fail to generalize to test data. In this paper, we propose DCIP to handle Distribution Shifts on Dynamic Gr...

Enhancing heat and mass transfer in hybrid nanofluid with gyrotactic microbes and local thermal non equlibrium effects using artificial neural network.

This study analyzes the impact of local thermal non-equilibrium on the bioconvection flow of hybrid nanofluid across a slender extending sheet contain...

Development and temporal validation of an interpretable point score for in-hospital mortality in CLL/SLL using the U.S. National Inpatient Sample, 2016-2022.

In-hospital mortality among patients with chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) is ∼6%, yet no validated, transparent beds...

AI in the Prediction of Hepatic Fibrosis Progression Using Non-Coding RNAs.

Hepatic fibrosis is a dynamic and progressive condition that can lead to cirrhosis and hepatocellular carcinoma (HCC) if left untreated. Appropriate a...

Advanced Prompting Techniques Informed by Clinical Expertise Improve the Accuracy of LLM Data Extraction but Increase Non-Determinism.

Prompt engineering techniques which aid in the use of generative artificial intelligence to address classification tasks have expanded considerably in...

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....

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...