Wednesday, November 30 | 3:00 p.m.-3:10 p.m. | SSM05-01 | Room S404AB
In a study that analyzed the risk of malignancy of screen-detected solid lung nodules, researchers from the Netherlands found that nodule size remains by far the most important predictor of malignancy. But when a nodule is discovered also matters.Data on 7,557 screening subjects came from Europe's largest low-dose CT lung cancer screening study, the Dutch-Belgian Longkanker Screenings Onderzoek (NELSON) trial, and focused on solid nodules detected in incidence screening rounds subsequent to baseline screening.
After baseline screening with low-dose CT, incidence screenings were conducted at one, three, and five and a half years. About 11% of screening subjects developed nodules, with a little over half proving malignant.
"The results suggest that new solid nodules have a higher probability of being lung cancer than do baseline nodules, even at a smaller size," wrote presenter Joan Walter in an email to AuntMinnie.com.
Beyond that, nodule volume remained the most important predictor even after correction for potential confounding variables. Adding multiple risk factors to nodule volume didn't improve the team's ability to predict malignancy. But the importance of growth rate in nodule volume may require adjustment in nodule size thresholds for lesions detected after baseline.


















![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)
