Quantitative imaging to assess tumor response to therapy: common themes of measurement, truth data, and error sources

Meyer, Charles R., Armato, Samuel G. III, Fenimore, Charles P. , McLennan, Geoffrey, Bidaut, Luc M., Barboriak, Daniel P., Gavrielides, Marios A., Jackson, Edward F., McNitt-Gray, Michael F., Kinahan, Paul E., Petrick, Nicholas and Zhao, Binsheng (2009) Quantitative imaging to assess tumor response to therapy: common themes of measurement, truth data, and error sources. Translational Oncology, 2 (4). pp. 198-210. ISSN 1944-7124

Full content URL: http://dx.doi.org/10.1593/tlo.09208

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Item Type:Article
Item Status:Live Archive


RATIONALE: Early detection of tumor response to therapy is a key goal. Finding measurement algorithms capable of early detection of tumor response could individualize therapy treatment as well as reduce the cost of bringing new drugs to market. On an individual basis, the urgency arises from the desire to prevent continued treatment of the patient with a high-cost and/or high-risk regimen with no demonstrated individual benefit and rapidly switch the patient to an alternative efficacious therapy for that patient. In the context of bringing new drugs to market, such algorithms could demonstrate efficacy in much smaller populations, which would allow phase 3 trials to achieve statistically significant decisions with fewer subjects in shorter trials. MATERIALS AND METHODS: This consensus-based article describes multiple, imagemodality- independentmeans to assess the relative performance of algorithms for measuring tumor change in response to therapy. In this setting, we describe specifically the example of measurement of tumor volume change from anatomic imaging as well as provide an overview of other promising generic analytic methods that can be used to assess change in heterogeneous tumors. To support assessment of the relative performance of algorithms for measuring small tumor change, data sources of truth are required. RESULTS: Very short interval clinical imaging examinations and phantom scans provide known truth for comparative evaluation of algorithms. CONCLUSIONS: For a given category ofmeasurementmethods, the algorithmthat has the smallest measurement noise and least bias on average will perform best in early detection of true tumor change. Copyright © 2009 Neoplasia Press, Inc. All rights reserved.

Keywords:algorithm, analytic method, anisotropy, article, cancer therapy, clinical assessment, computer assisted tomography, consensus, contrast enhancement, diagnostic accuracy, diagnostic error, diagnostic imaging, diffusion weighted imaging, digital imaging and communications in medicine, drug uptake, early diagnosis, human, image analysis, image quality, measurement error, methodology, molecular imaging, nuclear magnetic resonance imaging, outcome assessment, perfusion weighted imaging, positron emission tomography, quantitative analysis, single photon emission computer tomography, treatment response, tumor volume, variance
Subjects:F Physical Sciences > F350 Medical Physics
Divisions:College of Science > School of Computer Science
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ID Code:24139
Deposited On:25 Oct 2016 14:07

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