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.

The application of deep learning techniques in lung nodule detection represents a significant advance in the early diagnosis and management of lung cancer.

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 ...

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 ...

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 ...

Tests we’ve run show that our machine learning models are able to detect lung cancer biomarkers with over 90 percent accuracy – rates up to three times better than reported DNA-based blood tests. When ...

Scientists trained a machine-learning algorithm to predict accurately brain metastasis using biopsy samples from early-stage non-small cell lung cancer patients.

Early cancer detection is crucial in boosting patients’ survival rates and preventing metastasis, or the spread of cancer throughout the body. But what if doctors can’t see the early signs of ...