Dear CT Insider,
Pending the results of large, randomized trials, the jury is, of course, still out on the question of whether CT lung cancer screening would reduce mortality and morbidity population-wide. Studies have conflicted famously on the issue, while a more recent report from Massachusetts General Hospital in Boston found that modest mortality gains would result from the routine screening of smokers.
On the other hand, if your early-stage lung cancer is detected and resected successfully, you will almost certainly live longer.
It's also clear that CT needs help to improve the odds. On one level this can mean diligence on the part of radiologists to discern suspicious nodules. Regarding this topic, Dr. Charles White from the University of Maryland in College Park surveys the pitfalls that can lead to missed lung cancers.
On another level it means pairing CT with other promising modalities such as PET. Finally, optimizing CT screening means getting the most out of the image data that's already there, through computer-aided detection (CAD).
Researchers from Catholic University of America and Georgetown University Medical Center in Washington, DC, found surprisingly good results in a pilot study using discrete wavelet transform analysis, a method rarely applied to CAD algorithms.
The researchers compared their method to a laundry list of classical CAD feature parameters to determine which method found the most lung cancers. Their intriguing results are the subject of this issue's Insider Exclusive, brought to you before it's made available to other AuntMinnie.com members.
For another laundry list, including a report on crack lung, and a careful comparison of fourth-generation CT scanners, be sure to scroll down through all of the stories in your CT Digital Community.















![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)


