title: Multiple anatomy optimization of accumulated dose.
abstract: OBJECTIVE
  To investigate the potential advantages of multiple anatomy optimization (MAO) for lung cancer radiation therapy compared to the internal target volume (ITV) approach.
  
  
  METHODS
  MAO aims to optimize a single fluence to be delivered under free-breathing conditions such that the accumulated dose meets the plan objectives, where accumulated dose is defined as the sum of deformably mapped doses computed on each phase of a single four dimensional computed tomography (4DCT) dataset. Phantom and patient simulation studies were carried out to investigate potential advantages of MAO compared to ITV planning. Through simulated delivery of the ITV- and MAO-plans, target dose variations were also investigated.
  
  
  RESULTS
  By optimizing the accumulated dose, MAO shows the potential to ensure dose to the moving target meets plan objectives while simultaneously reducing dose to organs at risk (OARs) compared with ITV planning. While consistently superior to the ITV approach, MAO resulted in equivalent OAR dosimetry at planning objective dose levels to within 2% volume in 14/30 plans and to within 3% volume in 19/30 plans for each lung V20, esophagus V25, and heart V30. Despite large variations in per-fraction respiratory phase weights in simulated deliveries at high dose rates (e.g., treating 4/10 phases during single fraction beams) the cumulative clinical target volume (CTV) dose after 30 fractions and per-fraction dose were constant independent of planning technique. In one case considered, however, per-phase CTV dose varied from 74% to 117% of prescription implying the level of ITV-dose heterogeneity may not be appropriate with conventional, free-breathing delivery.
  
  
  CONCLUSIONS
  MAO incorporates 4DCT information in an optimized dose distribution and can achieve a superior plan in terms of accumulated dose to the moving target and OAR sparing compared to ITV-plans. An appropriate level of dose heterogeneity in MAO plans must be further investigated.
authors.affiliation: Department of Radiation Oncology, University of Virginia, Charlottesville, Virginia 22908 and Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia 23298.
authors.forename: W Tyler
authors.initials: WT
authors.lastname: Watkins
authors.affiliation: Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland 21231 and Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia 23298.
authors.forename: Joseph A
authors.initials: JA
authors.lastname: Moore
authors.affiliation: Henry Ford Health System, Detroit, Michigan 48202 and Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia 23298.
authors.forename: James
authors.initials: J
authors.lastname: Gordon
authors.affiliation: Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia 23298.
authors.forename: Geoffrey D
authors.initials: GD
authors.lastname: Hugo
authors.affiliation: Department of Radiation Oncology, University of Virginia, Charlottesville, Virginia 22908 and Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia 23298.
authors.forename: Jeffrey V
authors.initials: JV
authors.lastname: Siebers
mesh_terms: D000465:Algorithms; D002289:Carcinoma, Non-Small-Cell Lung / Q000000981:diagnostic imaging / Q000532:radiotherapy; D003198:Computer Simulation; D056973:Four-Dimensional Computed Tomography / Q000379:methods*; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy*; D058958:Organs at Risk; D019047:Phantoms, Imaging; D011336:Probability; D011874:Radiometry / Q000295:instrumentation / Q000379:methods*; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D012119:Respiration