Dear AuntMinnie Insider,
Virtual colonoscopy providers are increasingly using multidetector-row CT scanners to screen for colorectal cancer. In theory, their results should begin to reflect MDCT's improved speed, and better spatial and temporal resolution compared to single-slice machines.
Of course, virtual colonoscopy results are improving for many reasons. And isolating the clinical contributions of different scanner types would require patients to undergo CT imaging twice. Instead, investigators are making indirect comparisons, and are learning to look for subtle improvements using MDCT.
In this issue's Insider Exclusive, Dr. Alice Gillams from Middlesex Hospital in London examined 139 elderly patients using both multidetector-row VC and conventional colonoscopy. Based on surgical results, both techniques found the same number of cancers, 56/57, though VC's sensitivity dropped predictably for the smaller polyps.
In another study, Dr. Hiro Yoshida from the University of Chicago performed a retrospective review of two sets of virtual colonoscopy results: one from single-detector CT subjects and another from the multidetector crowd. According to the results, MDCT images were sharper and produced fewer false positives, an important consideration as colon CAD usage becomes more commonplace. Get the whole story here.
Speaking of CAD, another recent story in our VC community discusses CAD's emerging role as a second pair of eyes in virtual colonoscopy. Find out how computers are learning to share the search for lesions with their human counterparts, here.
As an Insider, you'll have access to important virtual colonoscopy stories before they're available to other AuntMinnie members. We hope you'll find the Virtual Colonoscopy Digital Community to be the best single source of information available on radiology's total colon exam. And as our community continues to grow, we look forward to hearing your comments and suggestions.

















![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)


