title: Mathematical modeling improves computed tomography diagnosis of traumatic aortic injury.
abstract: OBJECTIVE
  Acute traumatic aorta injuries (ATAIs) following blunt thoracic trauma require rapid and accurate diagnosis for institution of lifesaving treatment. The use of computed tomography (CT) in the diagnosis of such injuries continues to improve and has the potential to become the diagnostic modality of choice in many trauma centers. A standardized diagnostic model may contribute to improvements in radiologist interpretation of CT for ATAIs.
  
  
  METHODS
  The following diagnostic criteria were used to develop a diagnostic model for ATAIs: 11 areas of potential hematoma formation were identified in the mediastinum. Maximum short- and long-axis cross-sectional diameters of the aorta were measured. Qualitative morphologic information (contour change, intimal flap) was recorded. Smoothness of the aorta wall was assessed. These characteristics were quantified and analyzed for statistical significance, allowing for the development of an injury assessment model.
  
  
  RESULTS
  The diagnostic model was used to score 69 blunt thoracic trauma patient cases. Average weighted kappa was 0.74, showing strong agreement among two observers and reproducibility of the model. The model improved injury assessment by classifying equivocal cases as either positive or negative. The ROC curve calculated from the original radiologist interpretation contained 86.1% area under the curve, while the curve for the new model contained 97.5%. The likelihood ratio increased from 30.06 to 48.67. The degree to which the new measure improved prediction over the original radiologist reading was tested using a nested model and yielded a reliable increment in model fit (chi2 analysis: Deltachi2(3) = 20.929, P < or = .0001). Finally, beta weights calculated from each variable were used to create a quantitative best-fit diagnostic model for future use.
  
  
  CONCLUSIONS
  We have developed a diagnostic tool that may help radiologists better evaluate CT for ATAIs.
authors.affiliation: University of Texas Health Science Center at Houston Medical School, Diagnostic and Interventional Imaging, 6431 Fannin, MSB 2.100, Houston, TX 77030, USA.
authors.forename: David T
authors.initials: DT
authors.lastname: Fetzer
authors.forename: Charles
authors.initials: C
authors.lastname: Green
authors.forename: O Clark
authors.initials: OC
authors.lastname: West
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001011:Aorta / Q000293:injuries* / Q000473:pathology*; D003198:Computer Simulation; D005260:Female; D006335:Heart Injuries / Q000175:diagnosis*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D008297:Male; D008875:Middle Aged; D008955:Models, Cardiovascular; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013898:Thoracic Injuries / Q000175:diagnosis; D014057:Tomography, X-Ray Computed / Q000379:methods*; D014949:Wounds, Nonpenetrating / Q000175:diagnosis*