Dr. Giles Maskell[email protected]Medical, Legal, and PracticeTeleradiology: Here to stay, but still second best?Teleradiology can bring extra capacity and value for a department and has personal benefits for radiologists, but Dr. Giles Maskell remains convinced that for most purposes, local is best.February 2, 2026MRIIntroducing Maskell’s Law of CancerIf the patient doesn’t have cancer, then what you’re looking at probably isn’t a metastasis, Dr. Giles Maskell writes in a new column.September 2, 2025MRIMaskell's musings: What makes a good radiologist?There is a lot more to good radiology than having a sharp pair of eyes and a long differential, Dr. Giles Maskell writes in a new column.January 27, 2025Clinical NewsWhat can road safety tell us about radiological error?Too many discussions about avoiding error in radiology fall back on the notion of encouraging “vigilance,” Dr. Giles Maskell writes in a new column.April 2, 2024AIReferee! The impact of new technology in football and radiologyRadiologists can learn some important lessons from the recent experiences of the sporting world, particularly football, Dr. Giles Maskell writes in a new column.January 31, 2024Clinical NewsIs your radiology team still happy after the pandemic?Happy staff are more productive, and high staff morale leads to better outcomes for patients. But post-COVID, how can you tell if your department is a joyful place to work? And what can you do boost happiness? Dr. Giles Maskell addresses these questions.November 5, 2023Clinical NewsAn ugly new word for today's clinical radiologists: TaskificationTaskification involves breaking down a complex professional role into a series of smaller tasks, but what does it mean for radiology? Dr. Giles Maskell investigates, using injection of intravenous contrast in CT as an example.September 4, 2023Clinical NewsWhy radiologists and policymakers need to communicate betterRadiology plays a central role in modern medical care, but those in charge rarely seek the advice of radiologists when it comes to healthcare service planning, writes Dr. Giles Maskell. He explains why this must change.February 22, 2023Clinical NewsMaskell: Demand management for radiology isn't our problemCoping with the increasing demand for imaging is very much our problem, but limiting this demand is largely down to the actions of others elsewhere in the healthcare system, writes Dr. Giles Maskell.November 14, 2022Clinical News5 things that only a radiologist will know and understandGood clinical practice demands that medical doctors share their knowledge to support patient care, but there are still some things that only a radiologist can really understand, writes Dr. Giles Maskell. In his latest column, he provides five examples.July 4, 2022Page 1 of 3Next PageTop StoriesDigital X-RayDEXA confirms benefits of football on bone healthPlayers underwent a DEXA scan at the start of the season, at four months, and again at nine months.HomeRadiology's utilization problem sustainability still missedCTWelding fumes linked to distinct lung CT changes, functionMolecular ImagingHow AI can help with PET/CT guideline deviationsSponsor ContentTurning Innovation into Everyday Efficiency
![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)
