The U.K. Royal College of Radiologists' (RCR) Faculty of Clinical Radiology has released a 30-page document that underscores the importance of professional directness and honesty in radiology.
"Every healthcare professional must be open and honest with patients when something that goes wrong with their treatment or care causes, or has the potential to cause, harm or distress," wrote RCR Vice President Dr. William Ramsden in a foreword to the document. "They must also be open and honest with their colleagues, employers, and relevant organizations, taking part in reviews when required. This is our professional duty of candour."
Although it's not possible for the RCR to offer guidance for every situation, it hopes the report will offer an integrated approach.
"Patients and their families expect openness and honesty from healthcare providers," the authors wrote. "It is no longer considered acceptable for doctors to keep information from patients that patients themselves might consider important. It is incumbent on the radiology community to ensure that future relationships with patients and their families are built on trust and mutual understanding."
The document was cowritten by Prof. Mark Callaway, Dr. Giles Maskell, Dr. Christopher Hammond, Dr. Robin Evans, and Mr. Carl Flint. You can download it free-of-charge via the RCR website.
![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)
