Quantitative Imaging to Assess Tumor Response to Therapy: Common Themes of Measurement,Truth Data,and Error Sources |
| |
Authors: | Charles R Meyer Samuel G Armato III Charles P Fenimore Geoffrey McLennan Luc M Bidaut Daniel P Barboriak Marios A Gavrielides Edward F Jackson Michael F McNitt-Gray Paul E Kinahan Nicholas Petrick Binsheng Zhao |
| |
Institution: | 2. Department of Radiology, University of Chicago, Chicago IL, USA;3. National Institute of Standards and Technology Gaithersburg, MD, USA;4. Department of Internal Medicine, University of Iowa, Iowa City, IA, USA;5. Department of Imaging Physics, UT-MD Anderson Cancer Center, Houston, TX, USA;11. Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA;12. Department of Radiology, University of Washington, Seattle, WA, USA;8. Department of Radiology, Memorial Sloan-Lettering Cancer Center, New York, NY, USA |
| |
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. |
| |
Keywords: | |
本文献已被 ScienceDirect 等数据库收录! |
|