Dear AuntMinnie Member,
After winning a steel drill hammering competition against a drilling machine, American folk hero John Henry died after his heart gave out from the superhuman effort, according to legend.
In the second column of his two-part series on AI and the future of radiology, radiologist Joshua Ewell, DO, describes how the John Henry metaphor resonates powerfully with this generation of radiologists and also serves as a parable for the liminal space between human- and machine-based medical diagnoses.
AI was also the subject of our second most highly-viewed story this week. Researchers have found that a deep-learning algorithm can predict the risk of prostate cancer progression using MRI and clinical information.
Neuroradiology-oriented content also generated substantial page views. PET was judged to be effective at tracking long-term changes in the brains of patients with dementia. Furthermore, stroke patients with large total myelin volume measured on synthetic MRI appear to have good functional outcomes on follow-up.
In other news, researchers have reported that women with high-risk pathological variants for breast cancer as identified by genetic testing and risk assessment are about 10 times more likely to undergo breast MRI. And Y-90 radioembolization was deemed safe in people living with HIV.
See the list below for all of our top stories from the week.
- The John Henry Generation: The last of the radiologists, Part 2
- Deep-learning model based on MRI data predicts prostate cancer risk
- PET tracks long-term changes in patients with dementia
- Measuring myelin volume with synthetic MRI helps predict stroke outcomes
- AI will transform early cancer detection, save lives in 2025
- Genetic testing, risk assessment tied to more breast MRI uptake
- Y-90 radioembolization safe in people living with HIV
- Top 5 predictions for the imaging IT and AI markets in 2025
- USPSTF boosts DEXA in osteoporosis screening
- CEM has slightly higher mean glandular dose than DBT, mammography
- CEM-, radiomics-based models predict breast biopsy outcomes
- ML model using CCTA, MRI data predicts MACE in cardiac patients
- FAPI-PET shows promise for imaging gastric cancers
- POCUS useful for diagnosis of retained conception tissue
Erik L. Ridley
Editor in Chief
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)




