title: Suitability of semi-automated tumor response assessment of liver metastases using a dedicated software package.
abstract: OBJECTIVE
  To evaluate the suitability of semi-automated compared to manual tumor response assessment (TRA) of liver metastases.
  
  
  METHODS
  In total, 32 patients with colorectal cancer and liver metastases were followed by an average of 2.8 contrast-enhanced CT scans. Two observers (O 1, O 2) measured the longest diameter (LD) of 269 liver metastases manually and semi-automatically using software installed as thin-client on a PACS workstation (LMS-Liver, MEDIAN Technologies). LD and TRA ("progressive", "stable", "partial remission") were performed according to RECIST (Response Evaluation Criteria in Solid Tumors) and analyzed for between-method, interobserver and intraobserver variability. The time needed for evaluation was compared for both methods.
  
  
  RESULTS
  All measurements correlated excellently (r>or=0.96). Intraobserver (semi-automated), interobserver (manual) and between-method differences (by O 1) in LD of 1.4+/-2.6 mm, 1.9+/-1.9 mm and 2.1+/-2.0 mm, respectively, were not significant. Interobserver (semi-automated) and between-method (by O 2) differences in LD of 3.0+/-3.0 mm and 2.6+/-2.0 mm, respectively, reflected a significant variability (p<0.01). The interobserver agreement in manual and semi-automated TRA was 91.4 %. The intraobserver agreement in semi-automated TRA was 84.5%. Between both methods a TRA agreement of 86.2% was obtained. Semi-automated evaluation (2.7 min) took slightly more time than manual evaluation (2.3 min).
  
  
  CONCLUSIONS
  Semi-automated and manual evaluation of liver metastases yield comparable results in response assessments and require comparable effort.
authors.affiliation: Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen. janine.kalkmann@uk-essen.de
authors.forename: J
authors.initials: J
authors.lastname: Kalkmann
authors.forename: S C
authors.initials: SC
authors.lastname: Ladd
authors.forename: A
authors.initials: A
authors.lastname: de Greiff
authors.forename: M
authors.initials: M
authors.lastname: Forsting
authors.forename: J
authors.initials: J
authors.lastname: Stattaus
mesh_terms: D000328:Adult; D000368:Aged; D000970:Antineoplastic Agents / Q000627:therapeutic use*; D001185:Artificial Intelligence; D015179:Colorectal Neoplasms / Q000000981:diagnostic imaging* / Q000188:drug therapy* / Q000473:pathology; D003287:Contrast Media / Q000008:administration & dosage; D018450:Disease Progression; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007479:Iopamidol / Q000031:analogs & derivatives; D008099:Liver / Q000000981:diagnostic imaging / Q000473:pathology; D008113:Liver Neoplasms / Q000000981:diagnostic imaging / Q000188:drug therapy* / Q000473:pathology / Q000556:secondary*; D008297:Male; D008875:Middle Aged; D009367:Neoplasm Staging; D015588:Observer Variation; D011873:Radiology Information Systems; D012680:Sensitivity and Specificity; D012984:Software; D014057:Tomography, X-Ray Computed; D016896:Treatment Outcome
chemical_list: D000970:Antineoplastic Agents; D003287:Contrast Media; C057937:iomeprol; D007479:Iopamidol