Lung Cancer

ProphDR: An Interpretable Deep Learning Model for Predicting Cancer Drug Response via Multi-Omics and Cross-Attention Mechanisms.

Predicting cancer drug responses (CDRs) accurately remains a significant challenge due to the complexity of tumor biology and the limitations of existing "black-box" machine learning models. To address this, we propose ProphDR, an interpretable deep learning framework that integrates multiomics data and drug structural information using a hierarchical attention mechanism. ProphDR incorporates a Cr...

Explainable hourly global solar radiation forecasting using a CNN-BiLSTM model with temperature scaled softmax attention.

Accurate global solar radiation (GSR) forecasting is vital for smart grids and resilient energy systems. However, the nonlinear and non-stationary nature of meteorological drivers challenges conventional approaches. This study proposes a lightweight, explainable hybrid deep learning architecture, CNN-BiLSTM-STAM, which integrates convolutional layers for inter-feature pattern extraction, bidirecti...

Novel application of attention-enhanced hybrid efficient net for multi-scale histopathological cancer classification.

Examination of high-resolution whole-slide images requires an analysis of the histopathological images, which is essential in the precise diagnosis of...

Machine learning and multi-omic empowered risk stratification and therapeutic framework targeting nucleotide metabolism and mast cell for stomach adenocarcinoma patients.

BACKGROUND: Mast cell and nucleotide metabolism(NM) encodes the Stomach adenocarcinoma (STAD) progression and tumor immune microenvironment(TME) heter...

Specimen quality shapes the actionable genomic landscape in comprehensive cancer genomic profiling.

Comprehensive genomic profiling (CGP) is widely used to identify actionable alterations and guide precision oncology, yet only a minority of tested pa...

Multiple machine learning models for predicting major adverse cardiovascular events in dialysis with clinical and echocardiographic parameters: a retrospective cohort study.

BACKGROUND: Patients undergoing dialysis are at an elevated risk of cardiovascular events. This study aimed to develop machine learning (ML) predictio...

Artificial intelligence-based calcium scoring using 3D transesophageal echocardiography in aortic stenosis: a pilot study.

BACKGROUND: Aortic valve calcium scoring by computed tomography (CT) is an established method for assessing aortic stenosis severity but is limited by...

RadiolGAN: Multicenter Feasibility Study of Synthetic CT From 3D Ultra-Short Echo Time MRI for Enhanced Pulmonary Radiologic Sign Visualization.

BACKGROUND: Chest CT requires breath-holding and ionizing radiation. 3D ultrashort echo time (UTE) MRI allows radiation-free imaging, but the image qu...

Combining pathology artificial intelligence and genomic biomarkers to refine long-term postprostatectomy outcome prediction.

BACKGROUND: A multimodal AI (MMAI) model has been validated in prostate biopsy specimens to guide treatment intensification in men receiving radiation...

Center-specific Federated Learning for Radiation Pneumonitis:A Cross-Center Adaptive Alternating Framework.

BACKGROUND AND PURPOSE: Accurate prediction of symptomatic radiation pneumonitis (RP) is critical for radiotherapy, yet the generalization of deep lea...

Role of MRI-based synthetic CT and low-dose CT for the assessment of structural damage in axial spondyloarthritis - a review.

Structural lesions, including erosions, sclerosis, and pathological new bone formation, are key features of disease progression in axial spondyloarthr...

Unveiling Large-Scale Kinase-Centric Protein-Protein Interactions through a Knowledge-Informed Workflow.

Protein phosphorylation regulates signaling, yet atomic-level substrate specificity remains elusive due to sparse structural data and phosphorylation-...

Association and predictive value of preoperative high-sensitivity C-reactive protein for postoperative atrial fibrillation after video-assisted thoracoscopic lobectomy: a cohort study using the INSPIRE database.

BACKGROUND: Postoperative atrial fibrillation (POAF) is a common and serious complication following video-assisted thoracoscopic surgery (VATS), which...

Time-dependent radiological risk assessment following a severe NPP accident using Monte Carlo simulation, BEIR VII, and interpretable AI.

Nuclear reactor accidents can result in prolonged radiation exposure with complex and uncertain health consequences. Existing studies often focus on d...

Anatomically adaptive feature-wise linear modulation for deep learning-based low-dose CT denoising.

Low-dose computed tomography (LDCT) reduces radiation dose but, introduces heterogeneous noise due to different photon attenuation based on anato...

Current progress and future directions of intraoperative frozen section for spread through air spaces in lung cancer.

Spread through air spaces (STAS) is a recently recognized pattern of invasion in lung cancer that is strongly linked to postoperative recurrence and p...

MERWACS: Development and external validation of a non-invasive machine learning tool for identifying subjects to be screened for CKD.

Early detection of chronic kidney disease (CKD) is a critical public health priority. However, a gap exists for non-invasive tools to guide screening ...

Spiky Magnetic Titania Particles for Integrated Exosome Capture and Metabolic Profiling Toward Cancer Diagnosis.

Exosomal metabolite profiling represents a promising non-invasive approach for cancer diagnosis. However, its widespread application has been constrai...

Risk prediction of non-small cell lung cancer in patients with pulmonary nodules: a single-center cohort study based on six machine learning algorithms.

OBJECTIVE: Lung cancer is a major global public health concern. Non-small cell lung cancer (NSCLC), the most common subtype, has drawn increasing atte...

Efficient Diagnosis of Spatiotemporal Evolution and Driving Factors of Surface Ozone Pollution Episodes: An Application in Jiangsu Province, China.

Severe ozone (O3) pollution is a major challenge for air quality improvement in China, primarily due to its spatiotemporal heterogeneity and nonlinear...