Journal of Digital Imaging

, Volume 25, Issue 2, pp 258–265 | Cite as

Quantified Visual Scoring of Metastatic Melanoma Patient Treatment Response Using Computed Tomography: Improving on the Current Standard

  • Ronald H. Gottlieb
  • Elizabeth Krupinski
  • Pavani Chalasani
  • Lee Cranmer
Article

Abstract

To assess whether quantitative visual scoring (QVS) is a better early predictor of progression-free survival (PFS) in patients on chemotherapy for metastatic melanoma using CT than the currently used Response Evaluation Criteria in Solid Tumors (RECIST) standard. Retrospective evaluation of 65 consecutive patients with metastatic melanoma on treatment who had a baseline and follow-up CT after two cycles of therapy. QVS was used to code imaging findings on the radiology reports considering size change, brain metastases, new lesions, mixed lesion response, and the number of organ systems involved. RECIST 1.1 criteria placed patients in the progressive disease, stable disease, or partial response groups. Multiple regression analysis was used to correlate the various independent variables with PFS. The Cox hazard proportions ratio, median survival, and Kaplan–Meier curves of the different prognostic groups were calculated. QVS of size change was found more sensitive in detecting patients deteriorating (57.1% versus 37.5%) or improving (23.8% versus 10.7%), more correlated with the median PFS for the deteriorating (1.8 versus 1.7 months), stable (5.6 versus 4.0 month), and improving (8.3 versus 5.5 months) categories and more predictive of PFS (Cox hazard proportion ratio of 3.070 versus 1.860) than RECIST 1.1 categorization. Multiple regression analysis demonstrated QVS of lesion size correlated most closely with PFS among the variables assessed (r = 0.519, p < 0.0001). QVS in this study was superior to standard RECIST categorization in terms of discriminating treated metastatic melanoma patients likely to have longer PFS.

Keywords

Structured reporting Visual perception Cancer detection Image analysis Image perception Computed tomography Body imaging Clinical oncology Health services research 

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Copyright information

© Society for Imaging Informatics in Medicine 2011

Authors and Affiliations

  • Ronald H. Gottlieb
    • 1
  • Elizabeth Krupinski
    • 1
  • Pavani Chalasani
    • 2
  • Lee Cranmer
    • 2
  1. 1.Department of RadiologyUniversity of ArizonaTucsonUSA
  2. 2.Department of MedicineUniversity of ArizonaTucsonUSA

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