Dear AuntMinnie Member,
If your imaging facility performs mammography screening, you're acutely aware of the role that legal risk plays in your daily operations. Helping you reduce that risk is the subject of a new story we're featuring today in our Women's Imaging Digital Community.
In the article, staff writer Kate Madden Yee describes the types of malpractice risk that breast imaging facilities typically encounter, as well as what kinds of mammography pathology most likely leads to a missed diagnosis -- and legal action.
The story also provides advice from several breast imaging experts on steps you can take now to reduce your legal exposure. Learn more by clicking here, or visit the Women's Imaging Digital Community at women.auntminnie.com.
CT screening imbroglio
In other news, controversy over a 2006 lung cancer screening study in the New England Journal of Medicine has erupted again with news that the journal has revised its policies on financial disclosure.
The NEJM was criticized over its publication of a paper by Dr. Claudia Henschke and colleagues because the journal failed to publish disclosures provided by the authors that included financial ties to a major CT manufacturer. Now the journal is requiring all authors to submit a form listing potential financial conflicts.
Get the rest of the details by clicking here, or visit the CT Digital Community at ct.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&dpr=2&fit=crop&h=167&q=70&w=250)