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

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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.

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References

  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, 2000

    Article  PubMed  CAS  Google Scholar 

  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, 2009

    Article  PubMed  CAS  Google Scholar 

  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, 2006

    Article  PubMed  Google Scholar 

  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, 2008

    Article  PubMed  Google Scholar 

  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, 2010

    Article  PubMed  Google Scholar 

  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 16

    Article  PubMed  Google Scholar 

  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, 2004

    PubMed  CAS  Google Scholar 

  8. Turkbey B, Kobayash H, Ogawa M, Bernardo M, Choyke PL: Imaging of tumor angiogenesis: functional or targeted? AJR 193:304–313, 2009

    Article  PubMed  Google Scholar 

  9. Van Beers BE, Vilgrain V: Biomarkers in abdominal imaging. Abdominal Imaging 34(6):663–667, 2009

    Article  PubMed  Google Scholar 

  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, 2005

    Article  PubMed  CAS  Google Scholar 

  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, 2011

    Article  Google Scholar 

  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, 2007

    Article  PubMed  Google Scholar 

  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, 2002

    Article  PubMed  Google Scholar 

  14. Jaffe CC: Measures of response: RECIST, WHO, and new alternatives. J Clin Oncol 24(20):3245–3251, 2006

    Article  PubMed  Google Scholar 

  15. Likert R: A technique for the measurement of attitudes. Archives of Psychology 140:1–55, 1932

    Google Scholar 

  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, 2001

    Article  PubMed  CAS  Google Scholar 

  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, 1998

    PubMed  CAS  Google Scholar 

  18. Krupinski EA, Nodine CF, Kundel HL: A perceptually based method for enhancing pulmonary nodule recognition. Invest Radiol 28(4):289–294, 1993

    Article  PubMed  CAS  Google Scholar 

  19. Kundel HL, Polansky M: Measurement of observer agreement. Radiology 228(2):303–308, 2003. Epub 2003 Jun 20

    Article  PubMed  Google Scholar 

  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. Review

    Article  PubMed  Google Scholar 

  21. Krupinski EA, Kundel HL: Update on long-term goals for medical image perception research. Acad Radiol 5(9):629–633, 1998

    Article  PubMed  CAS  Google Scholar 

  22. Kundel HL: Medical image perception. Acad Radiol 2(Suppl 2):S108–S110, 1995

    PubMed  Google Scholar 

  23. Krupinski EA, Nodine CF, Kundel HL: Perceptual enhancement of tumor targets in chest Xray images. Percept Psychophys 53(5):519–526, 1993

    Article  PubMed  CAS  Google Scholar 

  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, 2009

    Article  PubMed  Google Scholar 

  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, 2010

    Article  PubMed  Google Scholar 

  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, 2008

    Article  Google Scholar 

  27. Langlotz CP: Automatic structuring of radiology reports: harbinger of a second information revolution in radiology. Radiology 222(1):5–7, 2002

    Article  Google Scholar 

  28. Langlotz C: RadLex: a new method for indexing online educational materials. Radiographics 26:1595–1597, 2006

    Article  PubMed  Google Scholar 

  29. Jemal A, Siegel R, Xu J, Ward E: Cancer Statistics 2010 American Cancer Society. CA Cancer J Clin 60:277–300, 2010

    Article  PubMed  Google Scholar 

  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 5

    Article  PubMed  CAS  Google Scholar 

  31. Smith JJ, Sorensen AG, Thrall JH: Biomarkers in imaging: realizing radiology’s future. Radiology 227(3):633–638, 2003

    Article  PubMed  Google Scholar 

  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, 2005

    Article  PubMed  CAS  Google Scholar 

  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, 2003

    Article  PubMed  Google Scholar 

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Correspondence to Ronald H. Gottlieb.

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Gottlieb, R.H., Krupinski, E., Chalasani, P. et al. Quantified Visual Scoring of Metastatic Melanoma Patient Treatment Response Using Computed Tomography: Improving on the Current Standard. J Digit Imaging 25, 258–265 (2012). https://doi.org/10.1007/s10278-011-9407-9

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  • DOI: https://doi.org/10.1007/s10278-011-9407-9

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