Skip to Content
COVID-19 Resources
Cancer Imaging Program (CIP)
Contact CIP
Show menu
Search this site
Last Updated: 09/21/17
Quantitative Imaging Network (QIN)

University of Washington

Advanced PET/CT Imaging for Improving Clinical Trials

Paul Kinahan, Ph.D., Hannah Linden, M.D.
kinahan@u.washington.edu; hmlinden@uw.edu
Grant Number: U01 CA148131

The overall goal in this research project is to improve the efficiency of clinical trials of new cancer therapies by enhancing the effectiveness of quantitative PET/CT imaging of tumor response. The group seeks to deploy and validate, through multi-center clinical trials, the quantitative PET imaging tools and methods that they have developed within the Quantitative Imaging Network (QIN). While quantitative PET imaging is a uniquely powerful tool to assess response to potential cancer therapies, it is also subject to several sources of bias and variability that degrade study power. In addition, multi-center studies are needed to increase patient accrual rates, even in early-phase studies. These multi-center studies can confound quantitative accuracy, further reducing study power, leading to missed opportunities in evaluating new therapies. To accelerate the evaluation of cancer therapies using quantitative PET imaging the team has three distinct and linked aims:

  1. Develop and implement a unified database and imaging platform that enables feasible deployment of their phantoms and software tools.

  2. Extend biologically principled imaging tools developed for FDG to FLT (proliferation) and FES (receptor status) in multicenter studies.

  3. Prospectively test the integration of the above tools and methods in a newly approved ECOG-ACRIN clinical trial that uses FES PET imaging to evaluate new breast cancer therapies.

The team believes these approaches can be readily extended to other cancers using quantitative PET Imaging. Prospective testing of quantitative PET imaging tools in multi-center clinical trials is an essential step. The advancement will help accelerate the effective evaluation of improved cancer therapies by quantitative imaging, which will benefit investigators, clinicians, and cancer patients.

Selected Peer-Reviewed Manuscripts

  1. Kurland BF, Muzi M, Peterson LM, Doot RK, Wangerin KA, Mankoff DA, Linden HM, Kinahan PE. Multicenter Clinical Trials Using 18F-FDG PET to Measure Early Response to Oncologic Therapy: Effects of Injection-to-Acquisition Time Variability on Required Sample Size. J Nucl Med 57:226-230, 2016. PMID: 26493206.PMCID: PMC4749350.

  2. Byrd DW, Doot RK, Allberg KC, MacDonald LR, McDougald WA, Elston BF, Linden HM, Kinahan PE. Evaluation of Cross-Calibrated (68)Ge/(68)Ga Phantoms for Assessing PET/CT Measurement Bias in Oncology Imaging for Single- and Multicenter Trials. Tomography 2(4):353-360, 2016. PMID: 28066807. PMCID: PMC5214172.

  3. Beichel RR, Smith BJ, Bauer C, Ulrich EJ, Ahmadvand P, Budzevich MM, Gillies RJ, Goldgof D, Grkovski M, Hamarneh G, Huang Q, Kinahan PE, Laymon CM, Mountz JM, Muzi JP, Muzi M, Nehmeh S, Oborski MJ, Tan Y, Zhao B, Sunderland JJ, Buatti JM. Multi-site quality and variability analysis of 3D FDG PET segmentations based on phantom and clinical image data. Med Phys 44(2):479-496, 2017. PMID: 28205306.

  4. Wangerin KA, Muzi M, Peterson LM, Linden HM, Novakova A, Mankoff DA, Kinahan PE. A virtual clinical trial comparing static versus dynamic PET imaging in measuring response to breast cancer therapy. Phys Med Biol 62(9):3639-3655, 2017. PMID: 28191877

  5. Huang W, Chen Y, Fedorov A, Li X, Jajamovich GH, Malyarenko DI, Aryal MP, LaViolette PS, Oborski MJ, O'Sullivan F, Jafari-Khouzani K, Afzal A, Tudorica A, Moloney B, Gupta SN, Besa C, Kalpathy-Cramer J, Mountz JM, Layman CM, Muzi M, Kinahan PE, Schmainda K, Cao Y, Chenevert T, Taoluli B, Yankeelov TE, Fennessy FM, Li X. The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge. Tomography 2:56-66, 2016. PMID: 27200418. PMCID: PMC4869732