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Development and validation of machine learning models for early diagnosis and prognosis of lung adenocarcinoma using miRNA expression profiles.
Cancer biomarkers : section A of Disease markers
Apr 2, 2025
ObjectiveStudy aims to develop diagnostic and prognostic models for lung adenocarcinoma (LUAD) using Machine learning（ML）algorithms, aiming to enhance clinical decision-making accuracy.MethodsData from The Cancer Genome Atlas (TCGA) for LUAD patients...
Automated Whole-Liver Fat Quantification with Magnetic Resonance Imaging-Derived Proton Density Fat Fraction Map: A Prospective Study in Taiwan.
Gut and liver
Apr 1, 2025
BACKGROUND/AIMS: Magnetic resonance imaging (MRI) with a proton density fat fraction (PDFF) sequence is the most accurate, noninvasive method for assessing hepatic steatosis. However, manual measurement on the PDFF map is time-consuming. This study a...
Gene age gap estimate (GAGE) for major depressive disorder: A penalized biological age model using gene expression.
Neurobiology of aging
Apr 1, 2025
Recent associations between Major Depressive Disorder (MDD) and measures of premature aging suggest accelerated biological aging as a potential biomarker for MDD susceptibility or MDD as a risk factor for age-related diseases. Residuals or "gaps" bet...
Multiparametric MRI-based Interpretable Machine Learning Radiomics Model for Distinguishing Between Luminal and Non-luminal Tumors in Breast Cancer: A Multicenter Study.
Academic radiology
Apr 1, 2025
RATIONALE AND OBJECTIVES: To construct and validate an interpretable machine learning (ML) radiomics model derived from multiparametric magnetic resonance imaging (MRI) images to differentiate between luminal and non-luminal breast cancer (BC) subtyp...
90-day mortality prediction in elective visceral surgery using machine learning: a retrospective multicenter development, validation, and comparison study.
International journal of surgery (London, England)
Apr 1, 2025
BACKGROUND: Machine Learning (ML) is increasingly being adopted in biomedical research, however, its potential for outcome prediction in visceral surgery remains uncertain. This study compares the potential of ML methods for preoperative 90-day morta...
Machine Vision Augmentation to Detect Detrusor Overactivity in Overactive Bladder: A Frontier of Artificial Intelligence Application in Functional Urology-Proof of Concept Clinical Study.
Neurourology and urodynamics
Apr 1, 2025
INTRODUCTION: Overactive bladder (OAB) is a common urological condition with increasing prevalence, especially in an aging population. Diagnosing and treating OAB can be challenging. While urodynamic study (UDS) is useful to confirm involuntary detru...
Uncertainty quantification for CT dosimetry based on 10 281 subjects using automatic image segmentation and fast Monte Carlo calculations.
Medical physics
Apr 1, 2025
BACKGROUND: Computed tomography (CT) scans are a major source of medical radiation exposure worldwide. In countries like China, the frequency of CT scans has grown rapidly, thus making available a large volume of organ dose information. With modern c...
A CNN-transformer fusion network for predicting high-grade patterns in stage IA invasive lung adenocarcinoma.
Medical physics
Apr 1, 2025
BACKGROUND: Invasive lung adenocarcinoma (LUAD) with the high-grade patterns (HGPs) has the potential for rapid metastasis and frequent recurrence. Therefore, accurately predicting the presence of high-grade components is crucial for doctors to devel...
Predictive models of epidermal growth factor receptor mutation in lung adenocarcinoma using PET/CT-based radiomics features.
Medical physics
Apr 1, 2025
BACKGROUND: Lung adenocarcinoma (LAC) comprises a substantial subset of non-small cell lung cancer (NSCLC) diagnoses, where epidermal growth factor receptor (EGFR) mutations play a pivotal role as indicators for therapeutic intervention with targeted...
Identifying progression subphenotypes of Alzheimer's disease from large-scale electronic health records with machine learning.
Journal of biomedical informatics
Apr 1, 2025
OBJECTIVE: Identification of clinically meaningful subphenotypes of disease progression can enhance the understanding of disease heterogeneity and underlying pathophysiology. In this study, we propose a machine learning framework to identify subpheno...
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