The Spanish Society of Medical Radiology (SERAM) has made it clear it does not agree with a recent health technology assessment that has recommended not implementing low-dose CT (LDCT) screening for lung cancer.
In a statement issued on 5 September, the society shared its thoughts on the assessment, which it said maximizes the potential negative aspects of screening while minimizing the positive ones. It also said that reduced radiation doses given off by LDCT and the low rate of false positives are not being considered with these assessments.
SERAM also said the report fails to take adequate account of evidence showing that lung cancer screening is important in bringing down cancer-related deaths.
However, the society did acknowledge the limitations of not having data about screening experiences in Spain, as outlined in the assessment. The report said the CASSANDRA project would provide this information, which could influence the implementation of a population-based lung cancer screening program.
You can get the full story in Spanish 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)
