title: Pathologic validation of a model based on diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging for tumor delineation in the prostate peripheral zone.
abstract: OBJECTIVE
  For focal boost strategies in the prostate, the robustness of magnetic resonance imaging-based tumor delineations needs to be improved. To this end we developed a statistical model that predicts tumor presence on a voxel level (2.5×2.5×2.5 mm3) inside the peripheral zone. Furthermore, we show how this model can be used to derive a valuable input for radiotherapy treatment planning.
  
  
  METHODS
  The model was created on 87 radiotherapy patients. For the validation of the voxelwise performance of the model, an independent group of 12 prostatectomy patients was used. After model validation, the model was stratified to create three different risk levels for tumor presence: gross tumor volume (GTV), high-risk clinical target volume (CTV), and low-risk CTV.
  
  
  RESULTS
  The model gave an area under the receiver operating characteristic curve of 0.70 for the prediction of tumor presence in the prostatectomy group. When the registration error between magnetic resonance images and pathologic delineation was taken into account, the area under the curve further improved to 0.89. We propose that model outcome values with a high positive predictive value can be used to define the GTV. Model outcome values with a high negative predictive value can be used to define low-risk CTV regions. The intermediate outcome values can be used to define a high-risk CTV.
  
  
  CONCLUSIONS
  We developed a logistic regression with a high diagnostic performance for voxelwise prediction of tumor presence. The model output can be used to define different risk levels for tumor presence, which in turn could serve as an input for dose planning. In this way the robustness of tumor delineations for focal boost therapy can be greatly improved.
authors.affiliation: Department of Radiotherapy, University Medical Center, Utrecht, The Netherlands. G.Groenendaal-2@umcutrecht.nl
authors.forename: Greetje
authors.initials: G
authors.lastname: Groenendaal
authors.forename: Alie
authors.initials: A
authors.lastname: Borren
authors.forename: Maaike R
authors.initials: MR
authors.lastname: Moman
authors.forename: Evelyn
authors.initials: E
authors.lastname: Monninkhof
authors.forename: Paul J
authors.initials: PJ
authors.lastname: van Diest
authors.forename: Marielle E P
authors.initials: ME
authors.lastname: Philippens
authors.forename: Marco
authors.initials: M
authors.lastname: van Vulpen
authors.forename: Uulke A
authors.initials: UA
authors.lastname: van der Heide
mesh_terms: D000368:Aged; D019540:Area Under Curve; D003287:Contrast Media; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods; D006801:Humans; D016015:Logistic Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011467:Prostate / Q000473:pathology* / Q000601:surgery; D017430:Prostate-Specific Antigen / Q000097:blood; D011468:Prostatectomy; D011471:Prostatic Neoplasms / Q000097:blood / Q000473:pathology* / Q000532:radiotherapy / Q000601:surgery; D012372:ROC Curve; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D047368:Tumor Burden
chemical_list: D003287:Contrast Media; D017430:Prostate-Specific Antigen