Lung Cancer

Artificial intelligence-assisted risk prediction of postoperative pulmonary complications in non-small cell lung cancer surgery.

BACKGROUND: Pulmonary complications are the most frequent adverse events following surgery for non-small cell lung cancer (NSCLC), influencing both short-term clinical outcomes and long-term prognosis. This study aimed to develop and evaluate artificial intelligence (AI) models to predict postoperative pulmonary complications in patients undergoing surgical treatment for NSCLC. MATERIALS AND METHO...

Multimodal AI approach combining deep learning imaging and clinical machine learning for pancreatic cancer detection.

Pancreatic ductal adenocarcinoma (PDAC) has poor prognosis due to late diagnosis, limitations of computed tomography (CT) imaging, and low accuracy of clinical biomarkers. This study aimed to develop and validate a multimodal artificial intelligence (AI) approach integrating imaging-based deep learning (DL) and clinical data-driven machine learning (ML) to improve PDAC diagnosis. A retrospective c...

Clinical validation and implementation of mSTOP, a machine learning-based, longitudinal prediction model for the early identification of non-small cell lung cancer patients who not benefit from immune checkpoint inhibitor treatment.

OBJECTIVES: There has been a lot of interest in the field of laboratory medicine regarding the use of machine learning (ML)-based prediction models. T...

An agentic framework for autonomous scientific discovery in cancer pathology.

Artificial intelligence has advanced cancer pathology, but many systems still depend on hand-crafted features, are hard to explain and rely on fragmen...

A cross-sectional protocol for experimental tongue high-density surface electromyography to detect and classify radiation-associated hypoglossal neuropathy.

BACKGROUND: Hypoglossal neuropathy is the most common lower cranial neuropathy detected as a delayed sequelae of Human Papillomavirus (HPV) -driven or...

Machine learning-based disentanglement of meteorological and anthropogenic drivers of ozone pollution in the Chengdu-Chongqing urban agglomeration.

To clarify the relative contributions of meteorological conditions and anthropogenic activities to ozone (O3) pollution in the Chengdu-Chongqing urban...

Next-generation AI for visually occult pancreatic cancer detection in a low-prevalence setting with longitudinal stability and multi-institutional generalisability.

BACKGROUND: Failure of conventional imaging to detect pancreatic ductal adenocarcinoma (PDA) at its visually occult pre-diagnostic stage is a primary ...

Face aging rate quantifies change in biological age to predict cancer outcomes.

Chronological age predicts cancer survival but does not capture differences in biological aging rates. We apply FaceAge, an artificial intelligence al...

Automated Detection of Pediatric Pneumonia via Clinically Driven AI Analysis of Lung Ultrasound.

Lung ultrasound (LUS) is increasingly utilized for diagnosing pediatric pneumonia due to its bedside accessibility, radiation-free nature, and high di...

Assessing large language models in radiation risk communication: susceptibility, cultural-linguistic effects, and ethical reasoning.

PURPOSE: To assess the extent to which large language models (LLMs) amplify or attenuate inaccurate or contested narratives in radiation contexts and ...

Identification of key genes as diagnostic biomarkers for lung adenocarcinoma using bioinformatics and machine learning.

Interpretable Deep Learning with Multi-Scale CT for Predicting Occult Lymph Node Metastasis in Early-Stage NSCLC: A Multicenter Study.

Accurate preoperative prediction of occult lymph node metastasis (OLNM) in early-stage non-small cell lung cancer (NSCLC) is crucial for treatment pla...

Machine learning-advanced hydrogel-based transcription-coupled positive-feedback CRISPR/Cas13a analysis for novel microRNA signatures in differential diagnosis of non-small cell lung cancer.

MicroRNAs (miRNAs) hold significant potential as biomarkers for the precise diagnosis of non-small cell lung cancer (NSCLC). However, miRNAs remain un...

Deep feature optimization using fusion of multiple self-supervised learning approaches and filter-based feature selection for lung cancer histopathology classification.

One of the most deadly illnesses in the world is lung cancer, and increasing survival rates require early detection. Lung cancer diagnostics from the ...

Radiomics based on multiphasic contrast-enhanced CT combined with clinical features for differentiating hypovascular pancreatic neuroendocrine tumors and pancreatic ductal adenocarcinoma.

Artificial intelligence-based reclassification of gastric adenocarcinoma enables prognostic stratification via diffuse-type patch proportion.

BACKGROUND: Gastric adenocarcinoma (GAC) remains a major global health burden with marked heterogeneity, complicating diagnosis and prognostic assessm...

Machine learning-based identification of key genes underlying sex differences in hepatocellular carcinoma and targeted drug screening.

Hepatocellular carcinoma (HCC) shows a marked predominance in men, yet the molecular basis for this sex disparity remains unclear. The present study l...

3D vessel reconstruction from sparse-view dynamic DSA images via vessel probability guided attenuation learning.

Digital Subtraction Angiography (DSA) is one of the gold standards for vascular disease diagnosis. With the help of a contrast agent, time-resolved 2D...

A structure-based virtual screening approach to identify novel anaplastic lymphoma kinase inhibitors.

CONTEXT: Anaplastic lymphoma kinase (ALK) is a receptor tyrosine kinase that plays a crucial role in cellular signaling and is implicated in several c...

Fast Occupational Upper-Limb Radiation Dose Prediction Using Machine Learning and Monte Carlo Simulation.

OBJECTIVE: Interventional procedures expose physicians to scattered radiation, particularly to their upper extremities, posing occupational health ris...