title: Quantification of planning target volume margin when using a robotic radiosurgery system to treat lung tumors with spine tracking.
abstract: OBJECTIVE
  The use of fiducial markers or direct tumor visualization allows for tumor tracking and ultimately smaller planning target volume (PTV) margins in the treatment of lung tumors, yet many patients are either not amenable to fiducial marker placement or their tumors are unable to be visualized on orthogonal-axis x-ray images. Spine tracking is an alternative method for tumor localization but is limited by the assumption that the location of the lung tumor relative to the spine is constant. The purpose of this study was to quantify the additional PTV margin needed when spine tracking is used to ensure the internal target volume (ITV) receives the prescription dose during treatment.
  
  
  METHODS
  Daily cone beam computed tomography images, registered based on tumor position, from 63 patients with lung cancer treated with stereotactic body radiation therapy were collected and analyzed. Rigid registrations were reperformed so that the position of the spine on the cone beam computed tomography image was aligned to its position on the planning computed tomography. Shifts from the treatment position to the new position were recorded, and per-patient mean shifts and standard deviations were calculated, as well as group systematic and random standard deviations. These data were used with van Herk's margin recipe to determine the additional margin required to adequately treat the patient population if spine tracking were used instead of direct daily tumor imaging. A retrospective dosimetric analysis was also performed on 6 patients with lung cancer previously treated by CyberKnife using spine tracking to determine the potential decrease in target coverage attributable to insufficient margin on the ITV. This analysis was performed by shifting the PTV volume relative to the CyberKnife treatment geometry to simulate a setup error caused by tracking the spine as opposed to the tumor.
  
  
  RESULTS
  The additional margins calculated by van Herk's margin recipe to adequately cover the ITV with the 95% isodose surface for 90% of the entire patient population in the vertical, longitudinal, and lateral directions were 6.4, 6.0, and 4.5 mm, respectively. The retrospective analysis showed a decrease in PTV coverage from 95.6% to 93.1% and an increase in new conformity index by 2.7% when the average shift data were used to simulate setup error. When the maximum shift data were used to simulate the worst possible outcome, PTV coverage decreased to 73.4% and the new conformity index increased by 26.8%.
  
  
  CONCLUSIONS
  Standard margins of 5 mm on the ITV for patients with lung cancer being treated with stereotactic body radiation therapy are insufficient and may result in geographic misses of the tumor when spine tracking is used to locate the position of the tumor in the lung. Therefore, we recommend the addition of 5-mm margins in all directions for a total of 10 mm to take into account the change in position of the tumor relative to the spine from the time of simulation to treatment.
authors.affiliation: Department of Radiation Oncology, University of Louisville, Louisville, Kentucky.
authors.forename: Joshua
authors.initials: J
authors.lastname: James
authors.affiliation: Baptist Health Louisville, Louisville, Kentucky.
authors.forename: Christine
authors.initials: C
authors.lastname: Swanson
authors.affiliation: Center for Cancer Care, Huntsville, Alabama.
authors.forename: Bart
authors.initials: B
authors.lastname: Lynch
authors.affiliation: Department of Radiation Oncology, University of Louisville, Louisville, Kentucky.
authors.forename: Brian
authors.initials: B
authors.lastname: Wang
authors.affiliation: Department of Radiation Oncology, University of Louisville, Louisville, Kentucky. Electronic address: nedunl01@louisville.edu.
authors.forename: Neal E
authors.initials: NE
authors.lastname: Dunlap
mesh_terms: D054893:Cone-Beam Computed Tomography; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008175:Lung Neoplasms / Q000601:surgery*; D016634:Radiosurgery / Q000379:methods*; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D012189:Retrospective Studies; D065287:Robotic Surgical Procedures / Q000379:methods*; D013131:Spine / Q000000981:diagnostic imaging