Latest Advances in Lung Cancer Research

The recent study published supports a more standardized approach to incidental findings (IFs) in lung cancer screenings. This could help make it easier for healthcare professionals to explain potentially serious findings to patients and provide guidance on their clinical importance.

The AI tool can identify whether abnormal growths found on CT scans are cancerous, improving early detection and potentially make cancer treatment more successful.

A deep learning model called Sybil has been developed to predict an individual’s future lung cancer risk from a low-dose chest computed tomography scan.

Estimated rates of lung cancer screening with low-dose CT were extremely low among eligible patients across insurance types in 2017.

Deep-learning model takes a personalized approach to assessing each patient’s risk of lung cancer based on CT scans.

Lung cancer screening may accelerate the shift of the lung cancer stage from an advanced incurable stage toward an early curable disease, which may improve survival.

The diagnosis of early-stage lung cancer with low-dose CT screening led to high rates of lung cancer-specific survival two decades later.

Lung cancer screening saves lives. So why so do many at high risk not get one? Only 5.8% of people eligible for a free, low-dose CT scan actually get screened.

Younger lung cancer patients, ineligible for screening, are often diagnosed at advanced stages, unlike their older counterparts, highlighting a need for early detection methods.

Penn Medicine receives $9 million for tumor-illuminating technology to improve lung cancer surgery outcomes.

Memorial Sloan Kettering researchers find a new SCLC subtype with normal Rb protein, potentially treatable with existing CDK4/6 inhibitor drugs.

Study shows nearly half of high-risk lung cancer screenings have delayed follow-up, with current smokers at higher risk of delays in care.