title: Iterative active deformational methodology for tumor delineation: Evaluation across radiation treatment stage and volume.
abstract: OBJECTIVE
  To introduce, implement, and assess an iterative modification to the active deformational image segmentation method as applied to cervical cancer tumors.
  
  
  METHODS
  A comparison by Jaccard similarity (JS) between this active deformational method and manual segmentation was performed on tumors of various sizes across preradiation, 3 weeks postradiation, and 6 weeks postradiation using a General Linear Mixed Model across 121 studies from 52 patients with Stage IIB-IV cervical cancers.
  
  
  RESULTS
  The deformable segmentation method produced promising levels of agreement including JS factors of 0.71+/-0.11 in the preradiation studies. The analysis illustrated a rate of improvement in JS with increasing tumor volume that differed between the preradiation and 6 weeks postradiation stage (P=0.0474). In the large preradiated tumors each additional cm3 of volume was associated with an increase or improvement in JS of 0.0008 (95% confidence interval [CI]: 0.0003, 0.0014). In the smaller postradiation tumors, each additional cm3 of volume was associated with a more robust improvement in JS of 0.0046 (95% CI: 0.0009, 0.0082).
  
  
  CONCLUSIONS
  Agreement was strongly affected by tumor volume, and its performance was most impacted across volume in the later stages of radiation therapy. The deformation-based segmentation method appears to demonstrate utility for delineating cervical cancer tumors, particularly in the earliest stages of radiation treatment, where agreement is greatest.
authors.affiliation: Department of Radiological Sciences, Oklahoma University Health Science Center, Oklahoma City, Oklahoma 73104, USA. dee-wu@ouhsc.edu
authors.forename: D H
authors.initials: DH
authors.lastname: Wu
authors.forename: A D
authors.initials: AD
authors.lastname: Shaffer
authors.forename: D M
authors.initials: DM
authors.lastname: Thompson
authors.forename: Z
authors.initials: Z
authors.lastname: Yang
authors.forename: V A
authors.initials: VA
authors.lastname: Magnotta
authors.forename: R
authors.initials: R
authors.lastname: Alam
authors.forename: J
authors.initials: J
authors.lastname: Suri
authors.forename: W T C
authors.initials: WT
authors.lastname: Yuh
authors.forename: N A
authors.initials: NA
authors.lastname: Mayr
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D009367:Neoplasm Staging; D010363:Pattern Recognition, Automated / Q000379:methods*; D011881:Radiotherapy, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016896:Treatment Outcome; D002583:Uterine Cervical Neoplasms / Q000175:diagnosis* / Q000532:radiotherapy*