Dear AuntMinnie Insider,
Reduced-prep virtual colonoscopy, tagging agents, and computer-aided detection (CAD) schemes that can handle a challenge are prominent subjects in VC research today.
We explore them from more angles than a cubed display in this issue of the Insider, beginning with a study from Harvard Medical School and Massachusetts General Hospital in Boston.
There, researchers have tweaked their work-in-progress VC CAD system to operate under the difficult imaging conditions imposed by reduced-prep virtual colonoscopy. Based on previously developed CAD features and couple of new ones, the team solved several problems associated with minimal-prep VC datasets, achieving statistically equivalent accuracy compared to regular cleansed VC images. You'll find more details in our Insider Exclusive story.
For background on reduced-prep scans and the science required to perform them, don't miss the talk by radiologist and electronic cleansing expert Dr. Michael Zalis, also from Boston, who spoke at last week's International Symposium on Multidetector-Row CT.
Today colorectal cancer screening compliance is low, while screening-age populations are growing. If research succeeds in creating a patient-friendly, easy-to-interpret exam, will more patients avail themselves of it? A new study says they will, though a prominent researcher has doubts.
We invite you to fly through your Virtual Colonoscopy Digital Community for the rest of the news about colon cancer and screening.
![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)