Second Reading May Be Warranted with Whole-Body CT for Multiple Traumas
Patients with an initial clinical severity class of 1 also had a higher likelihood of missed injury.
Whole-body CT is associated with missing multiple injuries, particularly among patients over the age of 30 or who had two or more injured body parts, according to a study published in the journal Radiology.
Researchers from France performed a retrospective study to determine both radiologic and clinical markers that are predictive of missed injuries at early whole-body CT image interpretation.
Related article: Whole-Body CT Can Miss Injuries
A total of 2,354 consecutive whole-body CT examinations performed on 639 women and 1,715 men with multiple traumas from 26 hospitals were included in the study. The patients’ median age was 34 years. The images were interpreted at a teleradiology center study during an on-call period from February 2011 to September 2016 by the on-call radiologist. The images were also reviewed within 12 to 48 hours by another radiologist who looked for missed injury as the standard of reference.
The results showed 304 (12.9%) missed injuries, 59 (2.5%) of which were clinically significant. On a per-injury basis, the missed injury rate was 530 of 5,979 (8.8%). Independent predictive factors for missed injury included:
- More than two injured body parts
- Patient age older than 30 years
- Initial clinical severity class of 1
The researchers concluded that patients who had two or more injured body parts, were age older than 30 years, or had an initial clinical severity class of 1 had a higher likelihood of missed injury at whole-body CT.
Newsletter
Stay at the forefront of radiology with the Diagnostic Imaging newsletter, delivering the latest news, clinical insights, and imaging advancements for today’s radiologists.
Study Shows Enhanced Diagnosis of Coronary Artery Stenosis with Photon-Counting CTA
July 10th 2025In a new study comparing standard resolution and ultra-high resolution modes for patients undergoing coronary CTA with photon-counting detector CT, researchers found that segment-level sensitivity and accuracy rates for diagnosing coronary artery stenosis were consistently > 89.6 percent.
Can CT-Based Deep Learning Bolster Prognostic Assessments of Ground-Glass Nodules?
June 19th 2025Emerging research shows that a multiple time-series deep learning model assessment of CT images provides 20 percent higher sensitivity than a delta radiomic model and 56 percent higher sensitivity than a clinical model for prognostic evaluation of ground-glass nodules.
