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 ...

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

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 ...

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 ...

DELFI helps identify the presence of cancer using machine learning, a type of artificial intelligence, to examine millions of cfDNA fragments for abnormal patterns, including the size and amount ...

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 ...

The study demonstrates that machine learning can achieve high performance on a challenging image classification task and has the potential to greatly assist pathologists in lung cancer classification.

New research from Google shows how machine learning could one day be used to detect signs of lung cancer earlier than often occurs today. Early warning: Danial Tse, a researcher at Google ...

Using AI to Detect Cancer, Not Just Cats Neural networks are great at recognizing faces and objects in photos. Now they're being deployed to similarly identify signs of disease and illness.