title: Automated computer-derived prostate volumes from MR imaging data: comparison with radiologist-derived MR imaging and pathologic specimen volumes.
abstract: OBJECTIVE
  To compare prostate gland volume (PV) estimation of automated computer-generated multifeature active shape models (MFAs) performed with 3-T magnetic resonance (MR) imaging with that of other methods of PV assessment, with pathologic specimens as the reference standard.
  
  
  METHODS
  All subjects provided written informed consent for this HIPAA-compliant and institutional review board-approved study. Freshly weighed prostatectomy specimens from 91 patients (mean age, 59 years; range, 42-84 years) served as the reference standard. PVs were manually calculated by two independent readers from MR images by using the standard ellipsoid formula. Planimetry PV was calculated from gland areas generated by two independent investigators by using manually drawn regions of interest. Computer-automated assessment of PV with an MFA was determined by the aggregate computer-calculated prostate area over the range of axial T2-weighted prostate MR images. Linear regression, linear mixed-effects models, concordance correlation coefficients, and Bland-Altman limits of agreement were used to compare volume estimation methods.
  
  
  RESULTS
  MFA-derived PVs had the best correlation with pathologic specimen PVs (slope, 0.888). Planimetry derived volumes produced slopes of 0.864 and 0.804 for two independent readers when compared with specimen PVs. Ellipsoid formula-derived PVs had slopes closest to one when compared with planimetry PVs. Manual MR imaging and MFA PV estimates had high concordance correlation coefficients with pathologic specimens.
  
  
  CONCLUSIONS
  MFAs with axial T2-weighted MR imaging provided an automated and efficient tool with which to assess PV. Both MFAs and MR imaging planimetry require adjustments for optimized PV accuracy when compared with prostatectomy specimens.
authors.affiliation: Georgetown University School of Medicine, Washington, DC, USA.
authors.forename: Julie C
authors.initials: JC
authors.lastname: Bulman
authors.forename: Robert
authors.initials: R
authors.lastname: Toth
authors.forename: Amish D
authors.initials: AD
authors.lastname: Patel
authors.forename: B Nicolas
authors.initials: BN
authors.lastname: Bloch
authors.forename: Colm J
authors.initials: CJ
authors.lastname: McMahon
authors.forename: Long
authors.initials: L
authors.lastname: Ngo
authors.forename: Anant
authors.initials: A
authors.lastname: Madabhushi
authors.forename: Neil M
authors.initials: NM
authors.lastname: Rofsky
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D006801:Humans; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated; D011468:Prostatectomy; D011471:Prostatic Neoplasms / Q000473:pathology* / Q000601:surgery; D012189:Retrospective Studies; D018709:Statistics, Nonparametric