title: Treatment Response Assessment for Bladder Cancer on CT Based on Computerized Volume Analysis, World Health Organization Criteria, and RECIST.
abstract: OBJECTIVE
  The purpose of this study was to evaluate the accuracy of our autoinitialized cascaded level set 3D segmentation system as compared with the World Health Organization (WHO) criteria and the Response Evaluation Criteria In Solid Tumors (RECIST) for estimation of treatment response of bladder cancer in CT urography.
  
  
  METHODS
  CT urograms before and after neoadjuvant chemo-therapy treatment were collected from 18 patients with muscle-invasive localized or locally advanced bladder cancers. The disease stage as determined on pathologic samples at cystectomy after chemotherapy was considered as reference standard of treatment response. Two radiologists measured the longest diameter and its perpendicular on the pre- and posttreatment scans. Full 3D contours for all tumors were manually outlined by one radiologist. The autoinitialized cascaded level set method was used to automatically extract 3D tumor boundary. The prediction accuracy of pT0 disease (complete response) at cystectomy was estimated by the manual, autoinitialized cascaded level set, WHO, and RECIST methods on the basis of the AUC.
  
  
  RESULTS
  The AUC for prediction of pT0 disease at cystectomy was 0.78 ± 0.11 for autoinitialized cascaded level set compared with 0.82 ± 0.10 for manual segmentation. The difference did not reach statistical significance (p = 0.67). The AUCs using RECIST criteria were 0.62 ± 0.16 and 0.71 ± 0.12 for the two radiologists, both lower than those of the two 3D methods. The AUCs using WHO criteria were 0.56 ± 0.15 and 0.60 ± 0.13 and thus were lower than all other methods.
  
  
  CONCLUSIONS
  The pre- and posttreatment 3D volume change estimates obtained by the radiologist's manual outlines and the autoinitialized cascaded level set segmentation were more accurate for irregularly shaped tumors than were those based on RECIST and WHO criteria.
authors.affiliation: 1 Department of Radiology, The University of Michigan, MIB C476, 1500 East Medical Center Dr, Ann Arbor, MI 48109-5842.
authors.forename: Lubomir
authors.initials: L
authors.lastname: Hadjiiski
authors.affiliation: 2 Department of Urology, Comprehensive Cancer Center, The University of Michigan, Ann Arbor, MI.
authors.forename: Alon Z
authors.initials: AZ
authors.lastname: Weizer
authors.affiliation: 3 Department of Internal Medicine, Division of Hematology/Oncology, Comprehensive Cancer Center, The University of Michigan, Ann Arbor, MI.
authors.forename: Ajjai
authors.initials: A
authors.lastname: Alva
authors.affiliation: 1 Department of Radiology, The University of Michigan, MIB C476, 1500 East Medical Center Dr, Ann Arbor, MI 48109-5842.
authors.forename: Elaine M
authors.initials: EM
authors.lastname: Caoili
authors.affiliation: 1 Department of Radiology, The University of Michigan, MIB C476, 1500 East Medical Center Dr, Ann Arbor, MI 48109-5842.
authors.forename: Richard H
authors.initials: RH
authors.lastname: Cohan
authors.affiliation: 1 Department of Radiology, The University of Michigan, MIB C476, 1500 East Medical Center Dr, Ann Arbor, MI 48109-5842.
authors.forename: Kenny
authors.initials: K
authors.lastname: Cha
authors.affiliation: 1 Department of Radiology, The University of Michigan, MIB C476, 1500 East Medical Center Dr, Ann Arbor, MI 48109-5842.
authors.forename: Heang-Ping
authors.initials: HP
authors.lastname: Chan
mesh_terms: D000328:Adult; D000368:Aged; D015653:Cystectomy; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional; D008297:Male; D008875:Middle Aged; D020360:Neoadjuvant Therapy; D009361:Neoplasm Invasiveness; D009367:Neoplasm Staging; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D014057:Tomography, X-Ray Computed / Q000379:methods*; D016896:Treatment Outcome; D001749:Urinary Bladder Neoplasms / Q000000981:diagnostic imaging* / Q000188:drug therapy / Q000473:pathology / Q000601:surgery; D014567:Urography / Q000379:methods*; D014944:World Health Organization
keywords: CT scans; bladder cancer; computer 3D segmentation; level sets; response to treatment