title: Coverage-based treatment planning to accommodate deformable organ variations in prostate cancer treatment.
abstract: OBJECTIVE
  To compare two coverage-based planning (CP) techniques with standard fixed margin-based planning (FM), considering the dosimetric impact of interfraction deformable organ motion exclusively for high-risk prostate treatments.
  
  
  METHODS
  Nineteen prostate cancer patients with 8-13 prostate CT images of each patient were used to model patient-specific interfraction deformable organ changes. The model was based on the principal component analysis (PCA) method and was used to predict the patient geometries for virtual treatment course simulation. For each patient, an IMRT plan using zero margin on target structures, prostate (CTVprostate) and seminal vesicles (CTVSV), were created, then evaluated by simulating 1000 30-fraction virtual treatment courses. Each fraction was prostate centroid aligned. Patients whose D98 failed to achieve 95% coverage probability objective D98,95 ≥ 78 Gy (CTVprostate) or D98,95 ≥ 66 Gy (CTVSV) were replanned using planning techniques: (1) FM (PTVprostate = CTVprostate + 5 mm, PTVSV = CTVSV + 8 mm), (2) CPOM which optimized uniform PTV margins for CTVprostate and CTVSV to meet the coverage probability objective, and (3) CPCOP which directly optimized coverage probability objectives for all structures of interest. These plans were intercompared by computing probabilistic metrics, including 5% and 95% percentile DVHs (pDVH) and TCP/NTCP distributions.
  
  
  RESULTS
  All patients were replanned using FM and two CP techniques. The selected margins used in FM failed to ensure target coverage for 8/19 patients. Twelve CPOM plans and seven CPCOP plans were favored over the other plans by achieving desirable D98,95 while sparing more normal tissues.
  
  
  CONCLUSIONS
  Coverage-based treatment planning techniques can produce better plans than FM, while relative advantages of CPOM and CPCOP are patient-specific.
authors.affiliation: Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia 23298 and Department of Radiation Oncology, University of Maryland, Baltimore, Maryland 21201.
authors.forename: Huijun
authors.initials: H
authors.lastname: Xu
authors.affiliation: Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia 23298.
authors.forename: Douglas J
authors.initials: DJ
authors.lastname: Vile
authors.affiliation: Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia 23298.
authors.forename: Manju
authors.initials: M
authors.lastname: Sharma
authors.affiliation: Department of Radiation Oncology, Henry Ford Health System, Detroit, Michigan 48202.
authors.forename: J James
authors.initials: JJ
authors.lastname: Gordon
authors.affiliation: Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia 23298 and Department of Radiation Oncology, University of Virginia, Charlottesville, Virginia 22908.
authors.forename: Jeffrey V
authors.initials: JV
authors.lastname: Siebers
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D008297:Male; D009038:Motion; D025341:Principal Component Analysis; D011336:Probability; D011467:Prostate / Q000000981:diagnostic imaging / Q000528:radiation effects; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy*; D011874:Radiometry; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods; D012306:Risk; D012669:Seminal Vesicles / Q000000981:diagnostic imaging / Q000528:radiation effects; D014057:Tomography, X-Ray Computed