The U.K. Royal College of Radiologists (RCR) has published a tribute to Dr. Joseph John Kaczmarczyk, a 28-year-old radiology trainee in Manchester who committed suicide on 25 November.
"He was happy as a radiologist and had a brilliant future ahead of him," noted Dr. Mark Regi, a consultant vascular interventional radiologist in Sheffield. "I think what I have learnt from his passing is that depression or mental illness can affect anyone and come on very quickly. If it can happen to someone as bright and 'normal' as Joe with a great support network and close family around him, then suicide can affect any one of us."
Known to everyone as Joe, Kaczmarczyk completed his foundation training in Sheffield and spent an additional year as a trust doctor in Sheffield before being appointed to the radiology training scheme in Manchester. Regi wrote that Kaczmarczyk was the brightest radiology trainee he had ever met. He was also the first medical student representative for the British Society of Interventional Radiology and attended the annual scientific meeting on a couple of occasions with posters and presentations, Regi said.
Kaczmarczyk's suicide has had a profound effect on the radiology community and all of those who knew him, according to Regi. He didn't have a history of depression or mental illness and was always able to apply common sense to any given situation and with a smile on his face, Regi said.
"Joe was part of a new breed of radiologists who strongly believed in radiology as a clinical specialty that should own its patients, and planned to use radiology to access sports and trauma medicine," wrote Regi, adding that Kaczmarczyk was a Manchester United fan. "His dream job would have been working at Old Trafford."
In memory of his loss, Kaczmarczyk's family is asking for donations to the Louise Tebooth Foundation, which aims to provide financial assistance to projects and services that support the mental well-being of doctors in England and Wales. It also supports initiatives assisting the bereaved families of doctors who have died by suicide.
The full tribute can be found here.
![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)
