Additionally, the detection of methylation patterns in circulating tumor DNA using next-generation sequencing methods could be used to non-invasively screen for lung cancer.

Machine learning models to identify the simplest way to screen for lung cancer have been developed by researchers from UCL and the University of Cambridge, bringing personalized screening one step ...

A promising new study has identified a highly accurate, non-invasive method to diagnose non-small cell lung cancer (NSCLC) ...

Aspyre Lung is a targeted biomarker panel of 114 genomic variants across 11 guideline-recommended genes with simultaneous DNA and RNA for non–small cell lung cancer (NSCLC). In this study, we ...

This study employs the concept of liquid biopsy, utilizing next-generation sequencing (NGS) to gather miRNA profiles, aiming to construct a machine learning-driven model for the detection of lung ...

In a proof-of-concept study, the team used machine learning to analyze blood plasma from more than 2,000 participants and link molecular patterns to lung cancer, extrapolating a potential ...

Researchers suggest DNA nanosensor-based PATROL technology could feasibly be adapted to detect other lung disorders and infections.

AI-powered solutions for opportunistic lung cancer detection using routine chest X-rays offer hope for improved early diagnosis and survival rates.

A study highlighted a mismatch between current lung cancer screening eligibility criteria and actual lung cancer risk.

Researchers conducted a prospective, longitudinal cohort study involving individuals with current or former habit, who had low-dose CT screening, in London.