The American College of Radiology (ACR) is urging the U.S. Preventive Services Task Force (USPSTF) to finalize its draft recommendation on CT lung cancer screening.
On July 29, USPSTF issued a draft recommendation (grade B) in favor of CT lung cancer screening of high-risk patients, including those 55 to 79 years old who have at least a 30-pack-year history of smoking. Dr. Paul Ellenbogen, chair of the ACR Board of Chancellors, has submitted comments to USPSTF that highlight ACR's strong support for the draft recommendation and urge the task force to move quickly to finalize its guidelines.
Ellenbogen agreed with the USPSTF criteria for who should be screened, acknowledging that the task force's criteria closely match those used in the National Lung Screening Trial, which found that screening produced a 20% mortality reduction in high-risk individuals. At the same time, however, Ellenbogen pointed out there could be other patient populations that would benefit from CT lung screening, based on factors such as occupational exposure, family history, radon exposure, etc.
The letter noted that ACR is developing practice guidelines and appropriateness criteria to guide patient management. Ellenbogen also stated that the availability of screening does not reduce the need for tobacco cessation.
Ellenbogen's letter can be found here.




















![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&fit=crop&h=112&q=70&w=112)