Latest Advances in Lung Cancer Research

Ohio State launched a mobile lung cancer screening unit to increase early detection statewide by bringing expert staff & the latest diagnostic tech to underserved areas. The goal is to boost screening rates.

Changing the current lung cancer guidelines to a 2-year smoking duration cutoff vs a 20 pack-year cutoff would significantly improve the rate of cancer detection and reduce racial disparities with screenings.

Artificial intelligence (AI) assessments of chest X-rays identified 28% of never-smokers as being at high-risk for lung cancer, according to research to be presented at the annual Radiological Society of North America (RSNA) conference.

UCLA researchers received a $6 million grant to refine a liquid biopsy blood test for early, accurate lung cancer detection and treatment guidance.

Researchers developed a test detecting proteins in blood that indicate lung cancer spread, promising for early metastasis detection and treatment guidance.

Experts recommend enhanced lung cancer screening, accessible treatments, and increased research funding to prevent deaths and support affected individuals.

New US guidelines lower lung cancer screening age to 50 and reduce smoking history requirement to 20 pack-years, expanding preventative scans to save more lives.

A recent study sheds light on the challenges faced by black veterans when it comes to receiving timely referrals for lung cancer. The study, conducted by researchers from the University of Michigan and the VA Ann Arbor Healthcare System, examined the referral patterns for lung cancer among black and white veterans.

Severe asthma and lung cancer may be linked, according to a new study.

Current screening guidelines may prevent early diagnosis of lung cancer in many cases, according to research presented at the American Association for Thoracic Surgery (AATS) Annual Meeting.

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.