Latest Advances in Lung Cancer Research

Dr. Steven Dubinett, member of LCFA’s Scientific Advisory Board, has been appointed as the new Dean of the David Geffen School of Medicine at the University of California, Los Angeles (UCLA). In this role, Dr. Dubinett will oversee the medical school’s operations and work towards advancing its mission of excellence in education, research, and patient care.

Bristol-Myers Squibb’s CheckMate-9LA trial shows combining nivolumab, ipilimumab, and chemotherapy improves outcomes in advanced NSCLC patients.

A groundbreaking study has revealed that a new lung cancer pill significantly cuts the risk of death by 50%.

In a recent study, patients who took Keytruda before and after surgery had better results than those who only had surgery.

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

A new study found staggering differences in the toll of death among Black and White Americans over a 22-year period. According to the researchers, while all racial groups saw decreases in death rates over that time, rates for Black people remained unchanged.

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.

A study conducted by Penn Medicine researchers has shown that some patients with advanced lung cancer may benefit from a shorter duration of immunotherapy treatment, a type of cancer treatment that helps the immune system fight cancer cells.

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

New treatments like immunotherapy and targeted therapy are improving post-surgery outcomes for lung cancer, with research ongoing to optimize these methods.

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.