Dear PACS Insider,
The current state of radiology report communication in the U.K. has again drawn the attention of the Royal College of Radiologists (RCR), which has released new standards to ensure that urgent findings are communicated and acted upon in a timely manner.
The RCR document includes 10 reporting standards and describes in detail the responsibilities of radiologists, referring physicians, and organizations in all stages of the radiology reporting process. Importantly, the document also emphasizes the need for fail-safe alert systems in imaging studies with critical, urgent, or significant findings. Click here to learn more.
The recent experience of the Republic of Ireland with its radiology quality improvement plan also might offer some valuable lessons for U.K. hospitals seeking to improve reporting performance, according to a recent column by David Howard of McKesson. Click here to learn how Irish public and private hospitals have benefitted from the initiative, and how a similar approach might pay off in the U.K.
Teleradiology has been widely adopted in Europe, but not everyone agrees that's a positive development. The European Society of Radiology (ESR) separately surveyed European national radiology societies as well as ESR radiologist members and found that teleradiology was commonly used in both hospital networks and for outsourcing purposes. The organization also found most national societies did not report a positive effect from teleradiology outsourcing, and ESR radiologist members had varied opinions on the threat teleradiology poses to radiologists. What else did the ESR learn? Click here for all of the details.
The 2016 Olympics are fast approaching, and London 2012 imaging lead Dr. Phil O'Connor has some suggestions for any radiologists and radiographers who will be part of the healthcare team in Rio. He shared his experience and tips in a recent video interview with Editor-in-Chief Philip Ward at a London workshop organized by the British Institute of Radiology in collaboration with AuntMinnieEurope.com.
In another video interview from the workshop, Dr. Sarath Bethapudi shared how his work at the polyclinic in London changed his life. Click here to view the video.
If you have any tips or suggestions for topics you'd like to see covered in the PACS Community, please feel free to drop me a line.
![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&dpr=2&fit=crop&h=167&q=70&w=250)
