title: Reliability of contour-based volume calculation for radiosurgery.
abstract: OBJECTIVE
  Determining accurate target volume is critical for both prescribing and evaluating stereotactic radiosurgery (SRS) treatments. The aim of this study was to determine the reliability of contour-based volume calculations made by current major SRS platforms.
  
  
  METHODS
  Spheres ranging in diameter from 6.4 to 38.2 mm were scanned and then delineated on imaging studies. Contour data sets were subsequently exported to 6 SRS treatment-planning platforms for volume calculations and comparisons. This procedure was repeated for the case of a patient with 12 metastatic lesions distributed throughout the brain. Both the phantom and patient datasets were exported to a stand-alone workstation for an independent volume-calculation analysis using a series of 10 algorithms that included approaches such as slice stacking, surface meshing, point-cloud filling, and so forth.
  
  
  RESULTS
  Contour data-rendered volumes exhibited large variations across the current SRS platforms investigated for both the phantom (-3.6% to 22%) and patient case (1.0%-10.2%). The majority of the clinical SRS systems and algorithms overestimated the volumes of the spheres, compared with their known physical volumes. An independent algorithm analysis found a similar trend in variability, and large variations were typically associated with small objects whose volumes were < 0.4 cm(3) and with those objects located near the end-slice of the scan limits.
  
  
  CONCLUSIONS
  Significant variations in volume calculation were observed based on data obtained from the SRS systems that were investigated. This observation highlights the need for strict quality assurance and benchmarking efforts when commissioning SRS systems for clinical use and, moreover, when conducting multiinstitutional cross-SRS platform clinical studies.
authors.affiliation: Department of Radiation Oncology, University of California, San Francisco, CA 94143, USA. lijunma@radonc.ucsf.edu
authors.forename: Lijun
authors.initials: L
authors.lastname: Ma
authors.forename: Arjun
authors.initials: A
authors.lastname: Sahgal
authors.forename: Ke
authors.initials: K
authors.lastname: Nie
authors.forename: Andrew
authors.initials: A
authors.lastname: Hwang
authors.forename: Aliaksandr
authors.initials: A
authors.lastname: Karotki
authors.forename: Brian
authors.initials: B
authors.lastname: Wang
authors.forename: Dennis C
authors.initials: DC
authors.lastname: Shrieve
authors.forename: Penny K
authors.initials: PK
authors.lastname: Sneed
authors.forename: Michael
authors.initials: M
authors.lastname: McDermott
authors.forename: David A
authors.initials: DA
authors.lastname: Larson
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology / Q000601:surgery*; D001932:Brain Neoplasms / Q000473:pathology / Q000601:surgery*; D006801:Humans; D019047:Phantoms, Imaging; D016634:Radiosurgery / Q000379:methods*; D015203:Reproducibility of Results; D047368:Tumor Burden