The goal of this proposal is to provide technically-advanced support for the specific aims of NCI- funded projects for medical imaging affiliated with the University of Washington, the NCI Quantitative Imaging Network (QIN), and the NCTN ECOG-ACRIN cooperative group. My medical imaging career began at UW in 1985, where I was fortunate to work with Drs. Michael Graham and Alex Spence, who laid the foundation for quantitative analysis and clinical oncology, respectively. My early work in PET imaging revolved around managing animal imaging projects, a full biochemistry lab and a nascent human PET glioma imaging program. As PET imaging developed under the PET Program Project (P01-CA042045, KA Krohn, PI), we transitioned to human imaging and clinical trials research, probing different biochemical pathways using numerous PET radiotracers. I developed an avidity for compartmental modeling analysis of kinetic image data, initially with guidance from Drs. James Bassingwaithe, Michael Graham and Finbarr O'Sullivan, and later in collaboration with Drs. Tony Shields, David Mankoff, Janet Eary, Hannah Linden and Paul Kinahan, all of them funded by NCI grants. I am currently Director of the Imaging Core lab and a member of Paul Kinahan's U01 grant. I have responsibility for participating in the image analysis working groups of the QIN, extended planning of PET imaging projects, imaging protocols and designing data analysis methods for new experiments. I am a senior contributor to our overall program as well as a provider of novel approaches to data management, quantitative image analysis and data simulations for numerous cancer projects in Radiology and Oncology. Due to the cost of imaging, the radiation dose and other issues, my personal ambition is to extract as much quantitative information as possible from well-designed and executed cancer imaging studies using PET radiotracers and advanced MR methods. My career goal is to provide independent, but collaborative, support for the specific aims of NCI grants for medical imaging projects at with UW, the QIN and ECOG-ACRIN. Additionally, I have national recognition as manager of the ACRIN-UW PET Core Lab, member of the ECOG-ACRIN Head and Neck, Experimental Imaging Sciences and Brain Tumor Committees, and a key member of the NIH-Cancer Imaging Program's QIN Working Groups. The innovative protocols I developed for dynamic imaging are a substantial improvement for extracting quantitative information from dynamic PET, and are now a national clinical trials standard. I routinely present my advanced PET imaging results at national and international scientific meetings (EORTC, SNMMI, WMIC, ACRIN and QIN/NCI) and publish/review manuscripts annually in medical journals. In summary, I have developed a nationally recognized advanced lab for PET image analysis, funded entirely through NCI grants for 30 years. The overall benefit NCI receives for sharing my labor across NCI funded projects will have an impact on current and new investigators, providing them with quantitative methods of analysis for PET/CT and MRI image data using robust, repeatable and validated methods.
This project will provide salary support for the Director of the PET image Core Laboratory at the University of Washington. The Core Director provides many services that benefit public health and are affiliated with NCI PET imaging projects (UW U01, NCI QIN, ECOG-ACRIN Clinical Trials, ECOG-ACRIN U01), which includes providing the acquisition, reconstruction and imaging protocols, laboratory analysis for tracer metabolites, managing patient imaging studies, analyzing the collected imaging and patient meta-data, generating model simulations and publishing the results. The Core Director works closely with project members including statisticians (Drs. Finbarr O'Sullivan and Brenda Kurland) and often publishes non-clinical articles relating the technical aspects of PET imaging for a variety of cancers that benefits the scientific community.
|O'Sullivan, Finbarr; O'Sullivan, Janet N; Huang, Jian et al. (2018) Assessment of a statistical AIF extraction method for dynamic PET studies with 15O water and 18F fluorodeoxyglucose in locally advanced breast cancer patients. J Med Imaging (Bellingham) 5:011010|
|Nehmeh, Sadek A; Schwartz, Jazmin; Grkovski, Milan et al. (2018) Inter-operator variability in compartmental kinetic analysis of 18F-fluoromisonidazole dynamic PET. Clin Imaging 49:121-127|
|Ratai, Eva-Maria; Zhang, Zheng; Fink, James et al. (2018) ACRIN 6684: Multicenter, phase II assessment of tumor hypoxia in newly diagnosed glioblastoma using magnetic resonance spectroscopy. PLoS One 13:e0198548|
|Schmainda, K M; Prah, M A; Rand, S D et al. (2018) Multisite Concordance of DSC-MRI Analysis for Brain Tumors: Results of a National Cancer Institute Quantitative Imaging Network Collaborative Project. AJNR Am J Neuroradiol 39:1008-1016|
|Peterson, Lanell M; O'Sullivan, Janet; Wu, Qian Vicky et al. (2018) Prospective Study of Serial 18F-FDG PET and 18F-Fluoride PET to Predict Time to Skeletal-Related Events, Time to Progression, and Survival in Patients with Bone-Dominant Metastatic Breast Cancer. J Nucl Med 59:1823-1830|
|Kurland, Brenda F; Peterson, Lanell M; Shields, Andrew T et al. (2018) Test-retest reproducibility of FDG-PET/CT uptake in cancer patients within a qualified and calibrated local network. J Nucl Med :|
|Malyarenko, Dariya; Fedorov, Andriy; Bell, Laura et al. (2018) Toward uniform implementation of parametric map Digital Imaging and Communication in Medicine standard in multisite quantitative diffusion imaging studies. J Med Imaging (Bellingham) 5:011006|
|Newitt, David C; Malyarenko, Dariya; Chenevert, Thomas L et al. (2018) Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network. J Med Imaging (Bellingham) 5:011003|
|Beichel, Reinhard R; Smith, Brian J; Bauer, Christian et al. (2017) Multi-site quality and variability analysis of 3D FDG PET segmentations based on phantom and clinical image data. Med Phys 44:479-496|
|Wangerin, Kristen A; Muzi, Mark; Peterson, Lanell M et al. (2017) A virtual clinical trial comparing static versus dynamic PET imaging in measuring response to breast cancer therapy. Phys Med Biol 62:3639-3655|