title: Evaluation of an automated knowledge based treatment planning system for head and neck.
abstract: BACKGROUND
  This study evaluated an automated inverse treatment planning algorithm, Pinnacle Auto-Planning (AP), and compared automatically generated plans with historical plans in a large cohort of head and neck cancer patients.
  
  
  METHODS
  Fifty consecutive patients treated with volumetric modulated arc therapy (Eclipse, Varian Medical System, Palo Alto, CA) for head and neck were re-planned with AP version 9.10. Only one single cycle of plan optimization using one single template was allowed for AP. The dose to the planning target volumes (PTV's; 3-4 dose levels), the organs at risk (OAR's) and the effective working time for planning was evaluated. Additionally, two experienced radiation oncologists blind-reviewed and ranked 10 plans.
  
  
  RESULTS
  Dose coverage and dose homogeneity of the PTV were significantly improved with AP, however manually optimized plans showed significantly improved dose conformity. The mean dose to the parotid glands, oral mucosa, swallowing muscles, dorsal neck tissue and maximal dose to the spinal cord were significantly reduced with AP. In 64 % of the plans, the mean dose to any OAR (spinal cord excluded) was reduced by >20 % with AP in comparison to the manually optimized plans. In 12 % of the plans, the manually optimized plans showed reduced doses by >20 % in at least one OAR. The experienced radiation oncologists preferred the AP plan and the clinical plan in 80 and 20 % of the cases, respectively. The average effective working time was 3.8 min ± 1.1 min in comparison to 48.5 min ± 6.0 min using AP compared to the manually optimized plans, respectively.
  
  
  CONCLUSIONS
  The evaluated automated planning algorithm achieved highly consistent and significantly improved treatment plans with potentially clinically relevant OAR sparing by >20 % in 64 % of the cases. The effective working time was substantially reduced with Auto-Planning.
authors.affiliation: Department of Radiation Oncology, University Hospital Zurich, Klinik für Radio-Onkologie, Rämistrasse 100, CH-8091, Zürich, Switzerland. Jerome.krayenbuehl@usz.ch.
authors.forename: Jerome
authors.initials: J
authors.lastname: Krayenbuehl
authors.affiliation: Philips Radiation Oncology Systems, Fitchburg, WI, USA. ian.norton@philips.com.
authors.forename: Ian
authors.initials: I
authors.lastname: Norton
authors.affiliation: Department of Radiation Oncology, University Hospital Zurich, Klinik für Radio-Onkologie, Rämistrasse 100, CH-8091, Zürich, Switzerland. Gabriela.Studer@usz.ch.
authors.forename: Gabriela
authors.initials: G
authors.lastname: Studer
authors.affiliation: Department of Radiation Oncology, University Hospital Zurich, Klinik für Radio-Onkologie, Rämistrasse 100, CH-8091, Zürich, Switzerland. Matthias.Guckenberger@usz.ch.
authors.forename: Matthias
authors.initials: M
authors.lastname: Guckenberger
mesh_terms: D000465:Algorithms; D006258:Head and Neck Neoplasms / Q000532:radiotherapy*; D006801:Humans; D058958:Organs at Risk / Q000528:radiation effects; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods*; D012189:Retrospective Studies