Cumulative Doses of Ionizing Radiation From Computed Tomography: A Population-Based Study
Konrad H Stopsack1, James R Cerhan2
1Department of Internal Medicine, Mayo Clinic, Rochester, MN; Department of Health Sciences Research, Mayo Clinic, Rochester, MN.
Mayo Clinic Proceedings
|June 29, 2019
Summary
Computed tomography (CT) scans expose specific patient groups to disproportionately high cumulative radiation doses. Abdominal and pelvic CTs contribute most, with cancer staging and pain evaluation being common indications for high-dose exposures.
Area of Science:
- Radiology and Imaging
- Public Health
- Medical Physics
Background:
- Computed tomography (CT) is a widely used diagnostic imaging tool.
- Assessing cumulative radiation exposure from CT is crucial for understanding long-term health risks.
- Population-based data on CT radiation doses and influencing factors are limited.
Purpose of the Study:
- To evaluate cumulative radiation doses from CT examinations in a population-based sample.
- To identify patient characteristics associated with higher CT radiation doses.
- To determine the clinical indications for CT scans in individuals receiving high radiation doses.
Main Methods:
- A cohort study utilized medical records linkage (Rochester Epidemiology Project) in Olmsted County, Minnesota.
- All adult CT examinations from 2004-2013 were analyzed.
- Ten-year cumulative effective ionizing radiation doses were estimated; high-dose CTs (≥100 mSv) underwent detailed review.
Main Results:
- Nearly half of 54,447 adults underwent at least one CT; 1.9% received ≥100 mSv over 10 years.
- Abdomen and pelvis CTs accounted for 67.2% of the total estimated radiation dose.
- Higher doses correlated with age, BMI, education, smoking status, and race; common indications included cancer restaging and abdominal pain.
Conclusions:
- CT radiation exposure is disproportionately distributed within the population.
- A small set of clinical indications drives the majority of high radiation doses among adults.
- Understanding these patterns is vital for optimizing CT utilization and patient safety.
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