More than 20% of U.S. military veterans who had a negative baseline CT lung cancer screening exam did not return for their follow-up exam, according to a study presented at the 2018 American College of Chest Physicians (CHEST) meeting in San Antonio.
In the retrospective study, researchers from the Medical University of South Carolina (MUSC) reviewed the adherence rate of individuals who underwent CT lung screening at one of eight hospitals involved in the Veterans Health Administration Lung Cancer Screening Demonstration Project from July 2013 to June 2015. Screening participants were current and former smokers between 55 and 80 years old with a smoking history of at least 30 pack-years.
Among all veterans who underwent baseline CT exams, 60% had negative scans for lung cancer and were scheduled for follow-up exams within 15 months. Of the 1,120 individuals eligible for repeat screening tests, only 880 (77.6%) showed up for the exams.
"Even within the context of a well-designed, implemented, and guideline-adherent [CT lung cancer] screening program, adherence is not optimal and does not reach the reported 95% of the [National Lung Screening Trial] when the baseline scan is negative," lead investigator Dr. Paul Brasher said in a statement from CHEST. "Both mortality benefit and cost-efficacy are likely to suffer without better adherence."
![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&dpr=2&fit=crop&h=167&q=70&w=250)
