Dear AuntMinnie Member,
Detecting flat polyps in the colon is one of the toughest jobs for virtual colonoscopy -- and conventional colonoscopy, for that matter. A new clinical study by Italian researchers suggests that small flat polyps are a problem capable of frustrating even virtual colonoscopy's most able practitioners.
The good news is that the study in question set the bar pretty high for virtual colonoscopy, according to an article by staff writer Eric Barnes that we're featuring in our Virtual Colonoscopy Digital Community.
First off, the researchers declined to use cathartic preparation for the bowel, in order to provide a comfortable screening environment attractive to the largest number of patients. Then they compared virtual colonoscopy not to standard colonoscopy, but to an advanced version that typically does a better job of finding flat polyps.
Virtual colonoscopy detected only about a third of the flat polyps found with the advanced colonoscopy technique. But the news isn't all bad. Most of the missed lesions were very small; VC found most of the larger ones. And technologies like IV contrast and computer-aided detection -- which weren't used in the study -- might improve the technique's performance in the future.
Read more about this preliminary study and its implications by clicking here, or visit our Virtual Colonoscopy Digital Community, at vc.auntminnie.com.




















![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)