Dear AuntMinnieEurope Member,
We hope you are enjoying your summer. We here at AuntMinnieEurope.com are still scouring the Web for important and interesting news for you so you can relax on your holiday.
For instance, did you see the study in BMJ on how the aggressive use of imaging could be leading to overdiagnosis of thyroid disorders?
Researchers believe that low-risk small papillary thyroid cancers -- the prime culprit behind this increased diagnostic incidence -- are being significantly overtreated. They are calling for a new nomenclature that removes "cancer" from the term to signify the low likelihood that these lesions will ever pose a risk to patients. Click here to learn more.
Italian researchers have reported that several components of the Mediterranean diet are associated with significant reductions in the incidence of lung cancer in individuals with heavy smoking histories who received CT screening. Go to our CT Digital Community or click here for the details.
In Finland, researchers are concerned about whether financial considerations could be leading to more patients receiving unnecessary MRI exams. They found that only a small percentage of MRI exams were deemed inappropriate, but education and the regular use of referral guidelines could help improve decision-making about the appropriateness of a scan. What else did the researchers conclude? Visit our MRI Digital Community or click here to find out.
In welcome news, the global x-ray market is set to boom, according to a new report. This is being driven primarily by continued digitization of x-ray systems and increasing healthcare investments in emerging regions. Be sure to read more of market research firm IMS Research's predictions; you won't be sorry.
Last but not least, it is often assumed that women younger than 40 with breast cancer must have different imaging characteristics than their older counterparts. French researchers have nipped this in the bud, but they did find younger women with breast cancer do have similar genetic profiles, which have a different distribution in young women compared with the general population.
Be sure to check back regularly at our website for more news -- you can trust we will always have you covered.



![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=100&q=70&w=100)






![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)








