title: Consistency and efficiency of CT analysis of metastatic disease: semiautomated lesion management application within a PACS.
abstract: OBJECTIVE
  The purpose of this study was to evaluate the success, consistency, and efficiency of a semiautomated lesion management application within a PACS in the analysis of metastatic lesions in serial CT examinations of cancer patients.
  
  
  METHODS
  Two observers using baseline and follow-up CT data independently reviewed 93 target lesions (17 lung, five liver, 71 lymph node) in 50 patients with either metastatic bladder or prostate cancer. The observers measured the longest axis (or short axis for lymph nodes) of each lesion and made Response Evaluation Criteria in Solid Tumors (RECIST) determinations using manual and lesion management application methods. The times required for examination review, RECIST calculations, and data input were recorded. The Wilcoxon signed rank test was used to assess time differences, and Bland-Altman analysis was used to assess interobserver agreement within the manual and lesion management application methods. Percentage success rates were also reported.
  
  
  RESULTS
  With the lesion management application, most lung and liver lesions were semiautomatically segmented. Comparison of the lesion management application and manual methods for all lesions showed a median time saving of 45% for observer 1 (p<0.05) and 28% for observer 2 (p=0.05) on follow-up scans versus 28% for observer 1 (p<0.05) and 9% for observer 2 (p=0.087) on baseline scans. Variability of measurements showed mean percentage change differences of only 8.9% for the lesion management application versus 26.4% for manual measurements.
  
  
  CONCLUSIONS
  With the lesion management application method, most lung and liver lesions were successfully segmented semiautomatically; the results were more consistent between observers; and assessment of tumor size was faster than with the manual method.
authors: Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr, Bldg 10, Rm 1C340 Bethesda, MD 20892, USA. les.folio@nih.gov
authors: Les R
authors: LR
authors: Folio
authors: Aline
authors: A
authors: Sandouk
authors: Jiaxin
authors: J
authors: Huang
authors: Jeffrey M
authors: JM
authors: Solomon
authors: Andrea B
authors: AB
authors: Apolo
mesh_terms: D000368:Aged; D004526:Efficiency; D005260:Female; D006801:Humans; D008297:Male; D009362:Neoplasm Metastasis / Q000000981:diagnostic imaging*; D009369:Neoplasms / Q000000981:diagnostic imaging* / Q000628:therapy; D010363:Pattern Recognition, Automated; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000592:standards*; D011873:Radiology Information Systems; D012189:Retrospective Studies; D014057:Tomography, X-Ray Computed
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