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Quantitative Imaging to Assess Tumor Response to Therapy: Common Themes of Measurement, Truth Data & Error Sources



Charles D. Fenimore, Charles R. Meyer, Samuel G. Armato, Geoffrey McLennan, Luc Bidaut, Daniel P. Barboriak, Marios A. Gavrielides, Nicholas Petrick, Edward F. Jackson, Michael McNitt-Gray, Paul E. Kinahan, Binsheng Zhao


Rationale and Objectives 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 therapy that may be effective 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 III trials to achieve statistically significant decisions in shorter durations with fewer subjects. Such studies would be much less costly in time and money spent to bring the drug to market. Materials and Methods This paper describes image modality-independent means to assess the relative performance of algorithms for measuring tumor change in response to therapy. It is in this setting that we describe specifically the example of measurement of tumor volume change from anatomic imaging. Additionally we provide an overview of other promising generic analytic methods that can be used to assess change in heterogeneous tumors. Results Short interval exams and phantom scans will provide known truth for comparative evaluation of the variance and bias of algorithms. Conclusion Out of a given set of measurement methods the algorithm that has the smallest measurement noise will likely perform best in detecting the smallest real tumor change.
Journal of Translational Oncology


short interval, dual baseline, coffee-break exams, early response to therapy, assess relative algorithm performance


Fenimore, C. , Meyer, C. , Armato, S. , McLennan, G. , Bidaut, L. , Barboriak, D. , Gavrielides, M. , Petrick, N. , Jackson, E. , McNitt-Gray, M. , Kinahan, P. and Zhao, B. (2009), Quantitative Imaging to Assess Tumor Response to Therapy: Common Themes of Measurement, Truth Data & Error Sources, Journal of Translational Oncology, [online], (Accessed May 21, 2024)


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Created December 1, 2009, Updated February 19, 2017