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


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.


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


  1. 1.
    Therasse P, Arbuck SG, Eisenhauer EA, et al: New guidelines to evaluate the response to treatment in solid tumors. J Natl Cancer Inst 92(3):205–216, 2000PubMedCrossRefGoogle Scholar
  2. 2.
    Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M, Rubinstein L, Shankar L, Dodd L, Kaplan R, Lacombe D, Verweij J: New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45(2):228–247, 2009PubMedCrossRefGoogle Scholar
  3. 3.
    Zhao B, Schwartz LH, Moskowitz CS, Ginsberg MS, et al: Lung cancer: computerized quantification of tumor response-initial results. Radiology 241(3):892–898, 2006PubMedCrossRefGoogle Scholar
  4. 4.
    Korn EL, Liu PY, Lee SJ, Chapman JA, et al: Meta-analysis of phase II cooperative group trials in metastatic stage IV melanoma to determine progression-free and overall survival benchmarks for future phase II trials. J Clin Oncol 26(4):527–534, 2008PubMedCrossRefGoogle Scholar
  5. 5.
    Liu F, Zhao B, Krug L, Ishill N, et al: Assessment of therapy responses and prediction of survival in malignant pleural mesothelioma through computer-aided volumetric measurement on computed tomography scans. J Thorac Oncol 5(6):879–884, 2010PubMedCrossRefGoogle Scholar
  6. 6.
    Balch CM, Gershenwald JE, Soong SJ, Thompson JF, et al: Final version of 2009 AJCC melanoma staging and classification. J Clin Oncol 27(36):6199–6206, 2009. Epub 2009 Nov 16PubMedCrossRefGoogle Scholar
  7. 7.
    Husband JE, Schwartz LH, Spencer J, Olivier L, et al: Evaluation of the response to treatment of solid tumours—a consensus statement of the International Cancer Imaging Society. Br J Cancer 90(12):2256–2260, 2004PubMedGoogle Scholar
  8. 8.
    Turkbey B, Kobayash H, Ogawa M, Bernardo M, Choyke PL: Imaging of tumor angiogenesis: functional or targeted? AJR 193:304–313, 2009PubMedCrossRefGoogle Scholar
  9. 9.
    Van Beers BE, Vilgrain V: Biomarkers in abdominal imaging. Abdominal Imaging 34(6):663–667, 2009PubMedCrossRefGoogle Scholar
  10. 10.
    Jordan BF, Runquist M, Raghunand N, Baker A, et al: Dynamic Contrast-enhanced and diffusion mri show rapid and dramatic changes in tumor microenvironment in response to inhibition of HIF-1α using PX-47. Neoplasia 7(5):475–485, 2005PubMedCrossRefGoogle Scholar
  11. 11.
    Nishino M, Guo M, Jackman DM, DiPiro PJ, et al: CT tumor volume measurement in advanced non-small-cell lung cancer: performance characteristics of an emerging clinical tool. Acad Radiol 18(1):57–62, 2011CrossRefGoogle Scholar
  12. 12.
    Choi H, Charnsangavej C, Faria SC, Macapinlac HA, Burgess MA, Patel SR, Chen LL, Podoloff DA, Benjamin RS: Correlation of computed tomography and positron emission tomography in patients with metastatic gastrointestinal stromal tumor treated at a single institution with imatinib mesylate: proposal of new computed tomography response criteria. J Clin Oncol 25:1753–1759, 2007PubMedCrossRefGoogle Scholar
  13. 13.
    Prasad SR, Jhaveri KS, Saini S, Hahn PF, Halpern EF, Sumner JE: CT tumor measurement for therapeutic response assessment: comparison of unidimensional, bidimensional, and volumetric techniques initial observations. Radiology 225(2):416–419, 2002PubMedCrossRefGoogle Scholar
  14. 14.
    Jaffe CC: Measures of response: RECIST, WHO, and new alternatives. J Clin Oncol 24(20):3245–3251, 2006PubMedCrossRefGoogle Scholar
  15. 15.
    Likert R: A technique for the measurement of attitudes. Archives of Psychology 140:1–55, 1932Google Scholar
  16. 16.
    Rogers MP, Orav J, Black PM: The use of a simple Likert Scale to measure quality of life in brain tumor patients. J Neurooncol 55(2):121–131, 2001PubMedCrossRefGoogle Scholar
  17. 17.
    Krupinski EA, Kundel HL, Judy PF, Nodine CF: The medical image perception society. Key issues for image perception research. Radiology 209(3):611–612, 1998PubMedGoogle Scholar
  18. 18.
    Krupinski EA, Nodine CF, Kundel HL: A perceptually based method for enhancing pulmonary nodule recognition. Invest Radiol 28(4):289–294, 1993PubMedCrossRefGoogle Scholar
  19. 19.
    Kundel HL, Polansky M: Measurement of observer agreement. Radiology 228(2):303–308, 2003. Epub 2003 Jun 20PubMedCrossRefGoogle Scholar
  20. 20.
    Beam CA, Krupinski EA, Kundel HL, Sickles EA, Wagner RF: The place of medical image perception in 21st-century health care. J Am Coll Radiol 3(6):409–412, 2006. ReviewPubMedCrossRefGoogle Scholar
  21. 21.
    Krupinski EA, Kundel HL: Update on long-term goals for medical image perception research. Acad Radiol 5(9):629–633, 1998PubMedCrossRefGoogle Scholar
  22. 22.
    Kundel HL: Medical image perception. Acad Radiol 2(Suppl 2):S108–S110, 1995PubMedGoogle Scholar
  23. 23.
    Krupinski EA, Nodine CF, Kundel HL: Perceptual enhancement of tumor targets in chest Xray images. Percept Psychophys 53(5):519–526, 1993PubMedCrossRefGoogle Scholar
  24. 24.
    Gottlieb RH, Kumar P, Loud P, Klippenstein D, et al: Semiquantitative visual approach to scoring lung cancer treatment response using computed tomography: a pilot study. J Comput Assist Tomogr 33(5):743–747, 2009PubMedCrossRefGoogle Scholar
  25. 25.
    Gottlieb RH, Raczyk C, Hanna T, Fora A, et al: Quantitative methodology using CT for predicting survival in patients with metastatic colorectal carcinoma: a pilot study. Clin Imaging 34(3):196–202, 2010PubMedCrossRefGoogle Scholar
  26. 26.
    Gottlieb RH, Litwin A, Gupta B, Taylor, et al: Qualitative radiology assessment of tumor response: does it measure up? J Clin Imaging 32(2):136–140, 2008CrossRefGoogle Scholar
  27. 27.
    Langlotz CP: Automatic structuring of radiology reports: harbinger of a second information revolution in radiology. Radiology 222(1):5–7, 2002CrossRefGoogle Scholar
  28. 28.
    Langlotz C: RadLex: a new method for indexing online educational materials. Radiographics 26:1595–1597, 2006PubMedCrossRefGoogle Scholar
  29. 29.
    Jemal A, Siegel R, Xu J, Ward E: Cancer Statistics 2010 American Cancer Society. CA Cancer J Clin 60:277–300, 2010PubMedCrossRefGoogle Scholar
  30. 30.
    Hodi FS, O’Day SJ, McDermott DF, Weber RW, Sosman JA, et al: Improved survival with ipilimumab in patients with metastatic melanoma. N Engl J Med 363(8):711–723, 2010. Epub 2010 Jun 5PubMedCrossRefGoogle Scholar
  31. 31.
    Smith JJ, Sorensen AG, Thrall JH: Biomarkers in imaging: realizing radiology’s future. Radiology 227(3):633–638, 2003PubMedCrossRefGoogle Scholar
  32. 32.
    Pien HH, Fischman AJ, Thrall JH, Sorensen AG: Using imaging biomarkers to accelerate drug development and clinical trials. Drug Discov Today 10(4):259–266, 2005PubMedCrossRefGoogle Scholar
  33. 33.
    Johnson JR, Williams G, Pazdur R: End points and United States Food and Drug Administration approval of oncology drugs. J Clin Oncol 21(7):1404–1411, 2003PubMedCrossRefGoogle Scholar

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

Personalised recommendations