title: Plan robustness of simultaneous integrated boost radiotherapy of prostate and lymph nodes for different image-guidance and delivery techniques.
abstract: OBJECTIVE
  Uncorrelated motion of targets and large deformations of organs at risk represent challenges for image-guidance in simultaneous integrated boost (SIB) radiotherapy (RT) of pelvic tumour sites. This study aims to evaluate the robustness towards geometrical uncertainties in prostate cancer using two image-guided RT (IGRT) set-up strategies for two SIB delivery methods. Secondly, we evaluate the ability of geometrical parameters to predict when the applied margins are insufficient, resulting in target underdosage (TUD).
  
  
  METHODS
  The study included nine patients with eight to nine repeat computed tomography (CT)-scans evenly distributed throughout their treatment course. The prostate target (CTV-p) and the lymph node target including seminal vesicles (CTV-ln/sv) were delineated in all scans. SIB treatment plans for intensity-modulated RT and volumetric modulated arc therapy were generated on the planning CT and transferred to the repeat CTs for dose re-calculation using registration based on either anatomy or intra-prostatic fiducial markers. Receiving operator characteristic analysis was used to deduce the ability of the parameters to predict TUD.
  
  
  RESULTS
  The dosimetric differences between the two positioning strategies were small for all parameters evaluated and significant only for the dose to rectum. Anatomy based registration resulted in inferior target coverage with a larger number of TUDs, mostly seen in the seminal vesicles. For both targets the highest sensitivity and specificity of predicting TUD was seen for the relative volume and the lowest was found for the displacement vector.
  
  
  CONCLUSIONS
  Positioning based on fiducials gave the best trade-off between coverage of the targets although resulting in the highest dose to rectum. Target underdosage occurred mostly in the seminal vesicles. For both targets, the best parameter to predict TUD was the relative volume.
authors.affiliation: Clinical Institute, Aarhus University, Aarhus, Denmark. sarathoe@rm.dk
authors.forename: Sara
authors.initials: S
authors.lastname: Thörnqvist
authors.forename: Lise
authors.initials: L
authors.lastname: Bentzen
authors.forename: Jørgen B B
authors.initials: JB
authors.lastname: Petersen
authors.forename: Liv B
authors.initials: LB
authors.lastname: Hysing
authors.forename: Ludvig P
authors.initials: LP
authors.lastname: Muren
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008198:Lymph Nodes / Q000000981:diagnostic imaging* / Q000528:radiation effects*; D008297:Male; D010386:Pelvic Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy*; D012372:ROC Curve; D011880:Radiotherapy Planning, Computer-Assisted; D050397:Radiotherapy, Intensity-Modulated; D012004:Rectal Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy; D014057:Tomography, X-Ray Computed; D001749:Urinary Bladder Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy