title: The use of probability maps to deal with the uncertainties in prostate cancer delineation.
abstract: OBJECTIVE
  The use of dynamic contrast-enhanced (DCE) imaging for delineation of prostate tumors requires that decisions are made on a voxel wise basis about the presence of tumor. While the sensitivity and specificity of this technique is high, we propose a probabilistic approach to deal with the intrinsic imaging uncertainty.
  
  
  METHODS
  Twenty-nine patients with biopsy-proven prostate cancer underwent a DCE-CT exam prior to radiotherapy. From a logistic regression on K(trans) values from healthy and diseased appearing prostate regions we obtained a probability function for the presence of tumor. K(trans) parameter maps were converted into probability maps and a stratification was applied at the 5% and 95% probability level, to identify low-, intermediate-, and high-risk areas for the presence of tumor.
  
  
  RESULTS
  In all patients, regions with high-, intermediate-, and low-risk were identified, with median volume percentages of 7.6%, 40.0%, and 52.1%, respectively. The contiguous areas that resulted from the voxel wise stratification can be interpreted as GTV, high-risk CTV, and CTV.
  
  
  CONCLUSIONS
  K(trans) parameter maps from a DCE-CT exam can be converted into probability maps for the presence of tumor. In this way, the intrinsic uncertainty that a voxel contains tumor can be incorporated into the treatment planning process.
authors.affiliation: University Medical Center Utrecht, Department of Radiotherapy, The Netherlands. J.Korporaal-2@umcutrecht.nl
authors.forename: Johannes G
authors.initials: JG
authors.lastname: Korporaal
authors.forename: Cornelis A T
authors.initials: CA
authors.lastname: van den Berg
authors.forename: Greetje
authors.initials: G
authors.lastname: Groenendaal
authors.forename: Maaike R
authors.initials: MR
authors.lastname: Moman
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; D000369:Aged, 80 and over; D000465:Algorithms; D001706:Biopsy; D003287:Contrast Media; D006801:Humans; D021621:Imaging, Three-Dimensional; D007472:Iohexol / Q000031:analogs & derivatives; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011336:Probability; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D011857:Radiographic Image Interpretation, Computer-Assisted; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed
chemical_list: D003287:Contrast Media; D007472:Iohexol; C038192:iopromide