title: Comparison of the accuracy of CT volume calculated by circumscription to prolate ellipsoid volume (bidimensional measurement multiplied by coronal long axis).
abstract: OBJECTIVE
  Tumor volume is one of the most important factors in evaluating the response to treatment of patients with cancer. The objective of this study was to compare computed tomographic (CT) volume calculation using a semiautomated circumscribing tracing tool (manual circumscription [MC]) to prolate ellipsoid volume calculation (PEVC; bidimensional measurement multiplied by coronal long axis) and determine which was more accurate and consistent.
  
  
  METHODS
  The study included six patients with nine neoplasms, six phantoms, and two radiologists. The neoplasms and phantoms of varying sizes and shapes were imaged using multidetector CT scanners, with slice thicknesses ranging from 0.5 to 3 mm. Measurements were performed using a TeraRecon 3D workstation. Each lesion and phantom was manually circumscribed, and its three dimensions were measured. The measurements were repeated 2 weeks later.
  
  
  RESULTS
  MC of the phantoms deviated from their true volumes by an average of 3.0 +/- 1%, whereas PEVC deviated by 10.1 +/- 3.99%. MC interobserver readings varied by 1.2 +/- 0.6% and PEVC by 4.8 +/- 3.3%. MC intraobserver readings varied by 1.95 +/- 1.75% and PEVC by 2.5 +/- 1.55%. Patient tumor volume predicted by MC and PEVC varied greatly; MC interobserver readings differed by 3.3 +/- 2.1% and PEVC by 20.1 +/- 10.6%. MC intraobserver readings varied by 2.5 +/- 1.9% and PEVC by 5.5 +/- 3.2%. Variability was greater for complex shapes than for simple shapes. Bidimensional analysis demonstrated an interobserver difference of 12.1 +/- 8.7% and an intraobserver difference of 5.05 +/- 3.3%. These results demonstrate large interobserver and intraobserver variability. Variability was greater for complex shapes than for simple shapes.
  
  
  CONCLUSIONS
  MC of neoplasms provided more accurate and consistent volume predictions than PEVC. More complicated shapes demonstrated the superiority of MC over PEVC.
authors.affiliation: Departments of Radiology, University of Arizona College of Medicine, Tucson, AZ 85724-5067, USA.
authors.forename: Ali M
authors.initials: AM
authors.lastname: Rkein
authors.forename: Chivonne
authors.initials: C
authors.lastname: Harrigal
authors.forename: Arnold C
authors.initials: AC
authors.lastname: Friedman
authors.forename: Daniel
authors.initials: D
authors.lastname: Persky
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Krupinski
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008297:Male; D009369:Neoplasms / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods*