A machine learning lung cancer risk prediction model outperformed logistic regression, supporting improved risk assessment and more efficient radiology based lung cancer screening.

Novel Graph Neural Network (N-GNN) Model Achieves Superior Accuracy in Early Lung Cancer Detection, Paving the Way for Enhanced Diagnostic Capabilities. The research, a collaboration between BioMark's ...

Please provide your email address to receive an email when new articles are posted on . Machine learning models can predict which patients receiving lung cancer therapy may need urgent care visits.

Electronic nose breath analysis achieved 80-92% accuracy for detecting lung cancer in patients with suspicious findings. Detection accuracy remained consistent across tumor characteristics, disease ...

FREMONT, Calif.--(BUSINESS WIRE)--Personalis, Inc. (Nasdaq: PSNL), in collaboration with Professor Charles Swanton and his colleagues at London’s Francis Crick Institute and University College London, ...

Cancer diagnoses traditionally require invasive or labor-intensive procedures such as tissue biopsies. Researchers at the Ludwig-Maximilians-Universität München (LMU) have now reported on a method ...

NeXT Personal identifies circulating tumor DNA in 81% of early-stage lung adenocarcinoma cases, offering potential for better disease stratification and outcome prediction. Study: Ultrasensitive ctDNA ...