Lymphoma

Iodine Density of Lymphoma, Metastatic SCCA, and Normal Cervical lymph nodes: A Comparative Analysis Based on DLSCT.

OBJECTIVE: To compare iodine density (ID) and contrast-enhanced attenuation value (CEAV) from dual-layer spectral computed tomography (DLSCT) scans of lymphomatous, metastatic squamous cell carcinoma (SCCA), and normal cervical lymph nodes.

Comparative performance of artificial neural networks and support vector Machines in detecting adulteration of apple juice concentrate using spectroscopy and time domain NMR.

The detection of adulteration in apple juice concentrate is critical for ensuring product authenticity and consumer safety. This study evaluates the effectiveness of artificial neural networks (ANN) and support vector machines (SVM) in analyzing spectroscopic data to detect adulteration in apple juice concentrate. Four techniques-UV-visible, fluorescence, near-infrared (NIR) spectroscopy, and time...

Radiomics for differentiating adenocarcinoma and squamous cell carcinoma in non-small cell lung cancer beyond nodule morphology in chest CT.

Distinguishing between primary adenocarcinoma (AC) and squamous cell carcinoma (SCC) within non-small cell lung cancer (NSCLC) tumours holds significa...

Robust and interpretable deep learning system for prognostic stratification of extranodal natural killer/T-cell lymphoma.

PURPOSE: Extranodal natural killer/T-cell lymphoma (ENKTCL) is an hematologic malignancy with prognostic heterogeneity. We aimed to develop and valida...

Integrating machine learning with bioinformatics for predicting idiopathic pulmonary fibrosis prognosis: developing an individualized clinical prediction tool.

Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease with a poor prognosis. Its non-specific clinical symptoms make accurate pre...

Artificial neural network systems to predict the response to sintilimab in squamous-cell non-small-cell lung cancer based on data of ORIENT-3 study.

BACKGROUND: Existing biomarkers and models for predicting response to programmed cell death protein 1 monoclonal antibody in advanced squamous-cell no...

Rapid detection of microplastics in chicken feed based on near infrared spectroscopy and machine learning algorithm.

The main objective of this study was to evaluate the potential of near infrared (NIR) spectroscopy and machine learning in detecting microplastics (MP...

Deep learning-based intratumoral and peritumoral features for differentiating ocular adnexal lymphoma and idiopathic orbital inflammation.

OBJECTIVES: To evaluate the value of deep-learning-based intratumoral and peritumoral features for differentiating ocular adnexal lymphoma (OAL) and i...

Deep learning detected histological differences between invasive and non-invasive areas of early esophageal cancer.

The depth of invasion plays a critical role in predicting the prognosis of early esophageal cancer, but the reasons behind invasion and the changes oc...

Lymphoma triage from H&E using AI for improved clinical management.

AIMS: In routine diagnosis of lymphoma, initial non-specialist triage is carried out when the sample is biopsied to determine if referral to specialis...

Exploratory study of extracellular matrix biomarkers for non-invasive liver fibrosis staging: A machine learning approach with XGBoost and explainable AI.

BACKGROUND: Novel circulating markers for the non-invasive staging of chronic liver disease (CLD) are in high demand. Although underutilized, extracel...

Hybrid SEM-ANN model for predicting undergraduates' e-learning continuance intention based on perceived educational and emotional support.

Based on the Expectation Confirmation Model (ECM), this study explores the impact of perceived educational and emotional support on university student...

BUSClean: Open-source software for breast ultrasound image pre-processing and knowledge extraction for medical AI.

Development of artificial intelligence (AI) for medical imaging demands curation and cleaning of large-scale clinical datasets comprising hundreds of ...

Non-invasive Prediction of Lymph Node Metastasis in NSCLC Using Clinical, Radiomics, and Deep Learning Features From F-FDG PET/CT Based on Interpretable Machine Learning.

PURPOSE: This study aimed to develop and evaluate a machine learning model combining clinical, radiomics, and deep learning features derived from PET/...

Enhancing automatic sleep stage classification with cerebellar EEG and machine learning techniques.

Sleep disorders have become a significant health concern in modern society. To investigate and diagnose sleep disorders, sleep analysis has emerged as...

Model-informed approach to estimate treatment effect in placebo-controlled clinical trials using an artificial intelligence-based propensity weighting methodology to account for non-specific responses to treatment.

In randomized, placebo controlled clinical trials (RCT) in major depressive disorders (MDD), treatment response (TR) is estimated by the change from b...

Diagnostic modalities in the mediastinum and the role of bronchoscopy in mediastinal assessment: a narrative review.

BACKGROUND AND OBJECTIVE: Diagnosis of pathology in the mediastinum has proven quite challenging, given the wide variability of both benign and malign...

A multi-view prognostic model for diffuse large B-cell lymphoma based on kernel canonical correlation analysis and support vector machine.

BACKGROUND AND OBJECTIVE: Positron emission tomography/computed tomography (PET/CT) is recommended as the standard imaging modality for diffuse large ...

A non-linear modelling approach to predict the dissolution profile of extended-release tablets.

This study proposes a novel non-linear modelling approach to predict the dissolution profiles of extended-release tablets, by combining a full-factori...

Rapid diagnosis of latent and active pulmonary tuberculosis by autofluorescence spectroscopy of blood plasma combined with artificial neural network algorithm.

The existing clinical diagnostic methods of pulmonary tuberculosis (TB) usually have some of the following limitations, such as time-consuming, invasi...