Quantitative Imaging to Assess Tumor Response to Therapy: Common Themes of Measurement, Truth Data, and Error Sources
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 des...
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| Published in | Translational oncology Vol. 2; no. 4; pp. 198 - 210 |
|---|---|
| Main Authors | , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.12.2009
Neoplasia Press Inc |
| Online Access | Get full text |
| ISSN | 1936-5233 1936-5233 |
| DOI | 10.1593/tlo.09208 |
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| Abstract | 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, image modality-independent means 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 of measurement methods, the algorithm that has the smallest measurement noise and least bias on average will perform best in early detection of true tumor change. |
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| AbstractList | 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, image modality-independent means 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 of measurement methods, the algorithm that has the smallest measurement noise and least bias on average will perform best in early detection of true tumor change. 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. This consensus-based article describes multiple, image modality-independent means 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. Very short interval clinical imaging examinations and phantom scans provide known truth for comparative evaluation of algorithms. For a given category of measurement methods, the algorithm that has the smallest measurement noise and least bias on average will perform best in early detection of true tumor change. 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, image modality-independent means 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 of measurement methods, the algorithm that has the smallest measurement noise and least bias on average will perform best in early detection of true tumor change. 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.RATIONALEEarly 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.This consensus-based article describes multiple, image modality-independent means 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.MATERIALS AND METHODSThis consensus-based article describes multiple, image modality-independent means 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.Very short interval clinical imaging examinations and phantom scans provide known truth for comparative evaluation of algorithms.RESULTSVery short interval clinical imaging examinations and phantom scans provide known truth for comparative evaluation of algorithms.For a given category of measurement methods, the algorithm that has the smallest measurement noise and least bias on average will perform best in early detection of true tumor change.CONCLUSIONSFor a given category of measurement methods, the algorithm that has the smallest measurement noise and least bias on average will perform best in early detection of true tumor change. |
| Author | McNitt-Gray, Michael F. Zhao, Binsheng Bidautn, Luc M. Meyer, Charles R. Barboriak, Daniel P. Gavrielides, Marios A. Jackson, Edward F. Kinahan, Paul E. Armato, Samuel G. Fenimore, Charles P. McLennan, Geoffrey Petrick, Nicholas |
| AuthorAffiliation | Department of Imaging Physics, UT-MD Anderson Cancer Center, Houston, TX, USA Department of Radiology, University of Chicago, Chicago IL, USA Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA National Institute of Standards and Technology, Gaithersburg, MD, USA Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA Department of Radiology, Duke University Medical Center, Durham, NC, USA Department of Radiology, University of Washington, Seattle, WA, USA Department of Internal Medicine, University of Iowa, Iowa City, IA, USA Department of Radiology, University of Michigan, Ann Arbor, MI, USA |
| AuthorAffiliation_xml | – name: Department of Radiology, University of Michigan, Ann Arbor, MI, USA – name: Department of Radiology, University of Washington, Seattle, WA, USA – name: Department of Radiology, Duke University Medical Center, Durham, NC, USA – name: National Institute of Standards and Technology, Gaithersburg, MD, USA – name: Department of Internal Medicine, University of Iowa, Iowa City, IA, USA – name: Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA – name: Department of Radiology, University of Chicago, Chicago IL, USA – name: Department of Imaging Physics, UT-MD Anderson Cancer Center, Houston, TX, USA – name: Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA – name: Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA |
| Author_xml | – sequence: 1 givenname: Charles R. surname: Meyer fullname: Meyer, Charles R. email: cmeyer@umich.edu organization: Department of Radiology, University of Michigan, Ann Arbor, MI, USA – sequence: 2 givenname: Samuel G. surname: Armato fullname: Armato, Samuel G. organization: Department of Radiology, University of Chicago, Chicago IL, USA – sequence: 3 givenname: Charles P. surname: Fenimore fullname: Fenimore, Charles P. organization: National Institute of Standards and Technology Gaithersburg, MD, USA – sequence: 4 givenname: Geoffrey surname: McLennan fullname: McLennan, Geoffrey organization: Department of Internal Medicine, University of Iowa, Iowa City, IA, USA – sequence: 5 givenname: Luc M. surname: Bidautn fullname: Bidautn, Luc M. organization: Department of Imaging Physics, UT-MD Anderson Cancer Center, Houston, TX, USA – sequence: 6 givenname: Daniel P. surname: Barboriak fullname: Barboriak, Daniel P. organization: Department of Radiology, Duke University Medical Center, Durham, NC, USA – sequence: 7 givenname: Marios A. surname: Gavrielides fullname: Gavrielides, Marios A. organization: Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA – sequence: 8 givenname: Edward F. surname: Jackson fullname: Jackson, Edward F. organization: Department of Imaging Physics, UT-MD Anderson Cancer Center, Houston, TX, USA – sequence: 9 givenname: Michael F. surname: McNitt-Gray fullname: McNitt-Gray, Michael F. organization: Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA – sequence: 10 givenname: Paul E. surname: Kinahan fullname: Kinahan, Paul E. organization: Department of Radiology, University of Washington, Seattle, WA, USA – sequence: 11 givenname: Nicholas surname: Petrick fullname: Petrick, Nicholas organization: Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA – sequence: 12 givenname: Binsheng surname: Zhao fullname: Zhao, Binsheng organization: Department of Radiology, Memorial Sloan-Lettering Cancer Center, New York, NY, USA |
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| Snippet | RATIONALE: Early detection of tumor response to therapy is a key goal. Finding measurement algorithms capable of early detection of tumor response could... Early detection of tumor response to therapy is a key goal. Finding measurement algorithms capable of early detection of tumor response could individualize... RATIONALE: Early detection of tumor response to therapy is a key goal. Finding measurement algorithms capable of early detection of tumor response could... |
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| Title | Quantitative Imaging to Assess Tumor Response to Therapy: Common Themes of Measurement, Truth Data, and Error Sources |
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