Literature DB >> 29804826

Comparative Analysis of Body Radiologist to Neuroradiologist Evaluation of the Spine in Trauma Settings.

Alice L Zhou1, Luke W Bonham1, Franco Verde2.   

Abstract

INTRODUCTION: CT is routinely performed to evaluate trauma patients. When a radiologist misses an acute finding, there could be serious adverse consequences. In many subspecialty settings, body radiologists and neuroradiologists both interpret the thoracic and lumbar spine. RADPEER has estimated general disagreement rates between radiologists to be 2.9%, but the disagreement rate between neuroradiologists and body radiologists in trauma settings remains unknown.
METHODS: This retrospective case review examined reports from the past 10 years of adult CT scans of the chest, abdomen, and pelvis interpreted by body radiologists, with concurrent thoracic and lumbar spine reconstructions interpreted by neuroradiologists. Reports were scrutinized for disagreement on the presence of acute fractures visible to both radiologists.
RESULTS: 1,497 report pairs were analyzed. Of them, 33 pairs (2.2%) disagreed on the presence of an unequivocal acute fracture. In scans where only one miss occurred, the body radiologist and neuroradiologist were attributed with 27 (82%) and 6 (18%) of 32 disagreements, respectively. One scan contained a miss by both the body radiologist and neuroradiologist. Transverse processes were most commonly missed, followed by vertebral body fractures.
CONCLUSION: Misses by body radiologists comprised the majority of disagreements. Neuroradiologists are more sensitive for detecting spinal fractures likely secondary to experience, education, small field of view reconstructed, and more detailed reporting protocols. Additional studies are needed to determine whether emulating neuroradiology practices may help body radiologists detect subtle fractures.
Copyright © 2018 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  CT; Peer review; spine; trauma

Mesh:

Year:  2018        PMID: 29804826     DOI: 10.1016/j.jacr.2018.03.002

Source DB:  PubMed          Journal:  J Am Coll Radiol        ISSN: 1546-1440            Impact factor:   5.532


  1 in total

1.  A deep learning-based method for the diagnosis of vertebral fractures on spine MRI: retrospective training and validation of ResNet.

Authors:  Lee-Ren Yeh; Yang Zhang; Jeon-Hor Chen; Yan-Lin Liu; An-Chi Wang; Jie-Yu Yang; Wei-Cheng Yeh; Chiu-Shih Cheng; Li-Kuang Chen; Min-Ying Su
Journal:  Eur Spine J       Date:  2022-01-28       Impact factor: 2.721

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.