title: Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation.
abstract: OBJECTIVE
  To assess the effectiveness of two automated magnetic resonance imaging (MRI) segmentation methods in determining the gross tumor volume (GTV) of brain tumors for use in radiation therapy treatment planning.
  
  
  METHODS
  Two automated MRI tumor segmentation methods (supervised k-nearest neighbors [kNN] and automatic knowledge-guided [KG]) were evaluated for their potential as "cyber colleagues." This required an initial determination of the accuracy and variability of radiation oncologists engaged in the manual definition of the GTV in MRI registered with computed tomography images for 11 glioma patients. Three sets of contours were defined for each of these patients by three radiation oncologists. These outlines were compared directly to establish inter- and intraoperator variability among the radiation oncologists. A novel, probabilistic measurement of accuracy was introduced to compare the level of agreement among the automated MRI segmentations. The accuracy was determined by comparing the volumes obtained by the automated segmentation methods with the weighted average volumes prepared by the radiation oncologists.
  
  
  RESULTS
  Intra- and inter-operator variability in outlining was found to be an average of 20% +/- 15% and 28% +/- 12%, respectively. Lowest intraoperator variability was found for the physician who spent the most time producing the contours. The average accuracy of the kNN segmentation method was 56% +/- 6% for all 11 cases, whereas that of the KG method was 52% +/- 7% for 7 of the 11 cases when compared with the physician contours. For the areas of the contours where the oncologists were in substantial agreement (i.e., the center of the tumor volume), the accuracy of kNN and KG was 75% and 72%, respectively. The automated segmentation methods were found to be least accurate in outlining at the edges of the tumor volume.
  
  
  CONCLUSIONS
  The kNN method was able to segment all cases, whereas the KG method was limited to enhancing tumors and gliomas with clear enhancing edges and no cystic formation. Both methods undersegment the tumor volume when compared with the radiation oncologists and performed within the variability of the contouring performed by experienced radiation oncologists based on the same data.
authors.affiliation: Department of Radiology, Moffitt Cancer Center, Tampa, FL, USA. mazzarg@moffitt.usf.edu
authors.forename: Gloria P
authors.initials: GP
authors.lastname: Mazzara
authors.forename: Robert P
authors.initials: RP
authors.lastname: Velthuizen
authors.forename: James L
authors.initials: JL
authors.lastname: Pearlman
authors.forename: Harvey M
authors.initials: HM
authors.lastname: Greenberg
authors.forename: Henry
authors.initials: H
authors.lastname: Wagner
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001932:Brain Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology* / Q000532:radiotherapy*; D005189:False Positive Reactions; D005260:Female; D005910:Glioma / Q000000981:diagnostic imaging / Q000473:pathology* / Q000532:radiotherapy*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D018787:Radiation Oncology / Q000592:standards; D011880:Radiotherapy Planning, Computer-Assisted; D015203:Reproducibility of Results; D013997:Time Factors; D014057:Tomography, X-Ray Computed