title: Comparison of diameter and perimeter methods for tumor volume calculation.
abstract: OBJECTIVE
  Lesion volume is often used as an end point in clinical trials of oncology therapy. We sought to compare the common method of using orthogonal diameters to estimate lesion volume (the diameter method) with a computer-assisted planimetric technique (the perimeter method).
  
  
  METHODS
  Radiologists reviewed 825 magnetic resonance imaging studies from 219 patients with glioblastoma multiforme. Each study had lesion volume independently estimated via the diameter and perimeter methods. Cystic areas were subtracted out or excluded from the outlined lesion. Inter- and intrareader variability was measured by using multiple readings on 48 cases. Where serial studies were available in noncystic cases, a mock response analysis was used.
  
  
  RESULTS
  The perimeter method had a reduced interreader and intrareader variability compared with the diameter method (using SD of differences): intrareader, 1.76 mL v 7.38 mL (P < .001); interreader, 2.51 mL v 9.07 mL (P < .001) for perimeter and diameter results, respectively. Of the 121 noncystic cases, 23 had serial data. In six (26.1%) of those 23, a classification difference occurred when the perimeter method was used versus the diameter method.
  
  
  CONCLUSIONS
  Variability of measurements was reduced with the computer-assisted perimeter method compared with the diameter method, which suggests that changes in volume can be detected more accurately with the perimeter method. The differences between these techniques seem large enough to have an impact on grading the response to therapy.
authors.affiliation: MGH NMR Center and Neuroradiology Division, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA. sorensen@nmr.mgh.harvard.edu
authors.forename: A G
authors.initials: AG
authors.lastname: Sorensen
authors.forename: S
authors.initials: S
authors.lastname: Patel
authors.forename: C
authors.initials: C
authors.lastname: Harmath
authors.forename: S
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authors.lastname: Bridges
authors.forename: J
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authors.lastname: Synnott
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authors.lastname: Sievers
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authors.initials: YH
authors.lastname: Yoon
authors.forename: E J
authors.initials: EJ
authors.lastname: Lee
authors.forename: M C
authors.initials: MC
authors.lastname: Yang
authors.forename: R F
authors.initials: RF
authors.lastname: Lewis
authors.forename: G J
authors.initials: GJ
authors.lastname: Harris
authors.forename: M
authors.initials: M
authors.lastname: Lev
authors.forename: P W
authors.initials: PW
authors.lastname: Schaefer
authors.forename: B R
authors.initials: BR
authors.lastname: Buchbinder
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authors.lastname: Barest
authors.forename: K
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authors.lastname: Yamada
authors.forename: J
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authors.lastname: Ponzo
authors.forename: H Y
authors.initials: HY
authors.lastname: Kwon
authors.forename: J
authors.initials: J
authors.lastname: Gemmete
authors.forename: J
authors.initials: J
authors.lastname: Farkas
authors.forename: A L
authors.initials: AL
authors.lastname: Tievsky
authors.forename: R B
authors.initials: RB
authors.lastname: Ziegler
authors.forename: M R
authors.initials: MR
authors.lastname: Salhus
authors.forename: R
authors.initials: R
authors.lastname: Weisskoff
mesh_terms: D001921:Brain / Q000473:pathology*; D001932:Brain Neoplasms / Q000175:diagnosis*; D005909:Glioblastoma / Q000175:diagnosis*; D006801:Humans; D008279:Magnetic Resonance Imaging; D008962:Models, Theoretical; D009716:Numerical Analysis, Computer-Assisted; D015588:Observer Variation