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Predicting in-hospital mortality in ICU patients with lymphoma using machine learning models.
PloS one
Aug 20, 2025
BACKGROUND: Lymphoma is a severe condition with high mortality rates, often requiring ICU admission. Traditional risk stratification tools like SOFA and APACHE scores struggle to capture complex clinical interactions. Machine learning (ML) models off...
Deep learning-based spatial analysis on tumor and immune cells of pathology images predicts MIBC prognosis.
PloS one
Aug 20, 2025
OBJECTIVE: Muscle-invasive bladder cancer (MIBC) is a highly aggressive disease with a poor prognosis. This study aims to explore the correlation between the spatial distribution of lymphocyte aggregates and the prognosis of MIBC using deep learning.
Anoikis-related genes predicts prognosis and therapeutic response in renal cell carcinoma.
Annals of medicine
Aug 19, 2025
BACKGROUND: Metastasis represents the primary cause of cancer-related mortality, with a high incidence observed in renal cell carcinoma (RCC). Anoikis, a specialized form of apoptosis, plays a crucial role in preventing displaced cells from adhering ...
Improving risk stratification of PI-RADS 3 + 1 lesions of the peripheral zone: expert lexicon of terms, multi-reader performance and contribution of artificial intelligence.
Cancer imaging : the official publication of the International Cancer Imaging Society
Aug 19, 2025
BACKGROUND: According to PI-RADS v2.1, peripheral PI-RADS 3 lesions are upgraded to PI-RADS 4 if dynamic contrast-enhanced MRI is positive (3+1 lesions), however those lesions are radiologically challenging. We aimed to define criteria by expert cons...
Metabolomics and nutrient intake reveal metabolite-nutrient interactions in metabolic syndrome: insights from the Korean Genome and Epidemiology Study.
Nutrition journal
Aug 19, 2025
BACKGROUND: Despite advances in metabolomics, the complex relationship between metabolites and nutrient intake in metabolic syndrome (MetS) remains poorly understood in the Korean population.
A machine learning approach to predict self-efficacy in breast cancer survivors.
BMC medical informatics and decision making
Aug 19, 2025
PURPOSE: To determine predictors of self-efficacy in breast cancer survivors and identify vulnerable groups.
Factors that influence technophobia in Chinese older patients with ischemic stroke: a cross-sectional survey.
BMC geriatrics
Aug 19, 2025
BACKGROUND: Older patients with ischemic stroke often have a large number of medical needs, technophobia refers to the irrational anxiety and fear of digital technologies such as mobile communication equipment, artificial intelligence and robots, res...
Application of deep learning reconstruction at prone position chest scanning of early interstitial lung disease.
BMC medical imaging
Aug 19, 2025
AIM: Timely intervention of interstitial lung disease (ILD) was promising for attenuating the lung function decline and improving clinical outcomes. The prone position HRCT is essential for early diagnosis of ILD, but limited by its high radiation ex...
Multimodal imaging deep learning model for predicting extraprostatic extension in prostate cancer using MpMRI and 18 F-PSMA-PET/CT.
Cancer imaging : the official publication of the International Cancer Imaging Society
Aug 19, 2025
OBJECTIVE: This study aimed to construct a multimodal imaging deep learning (DL) model integrating mpMRI and F-PSMA-PET/CT for the prediction of extraprostatic extension (EPE) in prostate cancer, and to assess its effectiveness in enhancing the diagn...
Predictive value of anthropometric indices for incident of dyslipidemia: a large population-based study.
Population health metrics
Aug 19, 2025
INTRODUCTION: Dyslipidemia as a modifiable risk factor for chronic non-communicable diseases has become a worldwide concern. We aim to explore different anthropometric measures as predictors of dyslipidemia using various machine learning methods.
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