Risk prediction model does not accurately identify lung cancer risks in diverse population

www.news-medical.net
3 min read
difficult
Lung cancer is the third most common cancer in the U.S. and the leading cause of cancer death, with about 80% of the total 154,000 deaths recorded each year caused by cigarette smoking. Black men are more likely to develop and die from lung cancer than persons of any other racial or ethnic group, pointing to severe racial disparities.
For example, research has shown that Black patients are less likely to receive early diagnosis and life-saving treatments like surgery.

Now researchers at Jefferson have found that a commonly used risk prediction model does not accurately identify high-risk Black patients who could gain life-saving benefit from early screening, and paves the way for improving screenings and guidelines. The research was published in JAMA Network Open on April 6.

"Black individuals develop lung cancer at younger ages and with less intense smoking histories compared to white individuals," explains Julia Barta, MD, Assistant Professor of Medicine in the Division of Pulmonary and Critical Care Medicine at Thomas Jefferson University, and researcher at the Jane and Leonard Korman Respiratory Institute.

"Updated guidelines now recommend screening eligible patients beginning at age 50, but could still potentially exclude higher-risk Black patients. We are interested in finding methods that could help identify at-risk patients who are under-screened."

Screening for lung cancer is an annual CT scan to detect the presence of lung cancer in otherwise healthy people with a high risk of lung cancer. Current guidelines do not require a risk score for screening eligibility, but some researchers think that risk models could improve care.

Risk prediction models are mathematical equations that take into account risk factors like smoking history and age to produce a risk score, which indicates the risk for developing lung cancer. Existing risk prediction models are derived from screening data…
Read full article