At the University of Iowa we have assembled a remarkably strong team of oncologists, clinical imaging specialists, computer scientists, imaging and therapy physicists, bioinformatics specialists, and statisticians, as well as collaboration with NIST and a public/private partnership with Siemens. This group is an ideal team to address the goals of this UOI Program Announcement and contribute robustly to the envisioned Quantitative Imaging Network (QIN). We plan to achieve our goals through:
Specific Aim 1 : Create an accessible environment for quantitative image analysis tool development and testing using an existing large H&N cancer dataset and caBIG. 1a: We plan to design and deploy a broadly connected informatics system architecture to support a locally networked relational database for development, implementation and validation of shared tools to the QIN through caBIG and the National Cancer Imaging Archive (NCIA). 1b: We will Interface our large PET/CT imaging and clinical outcomes database of pre and post therapy H&N cancer patients as a universally accessible test database that will be shared with the QIN.
Specific Aim 2 : Develop novel semi-automated tools for quantitative image-based response assessment. 2a: We will design, implement, and validate open source 2D and 3D semi- automated quantitative image analysis tools for tumor definition and quantitative metrics applicable to response assessment. We will also develop and apply advanced decision-support software as well as measurement variability tools so that image analysis tools can be compared and optimally selected for clinical cancer trials. 2b. We will test these image analysis tools on the imaging and clinical data to assess applicability and accessibility by QIN.
Specific Aim 3 : Establish robust quality procedures and standards for quantitative imaging, working with NIST and the QIN to enhance the quality and reliability of quantitative imaging for clinical decision-making. 3a: Define and validate calibration procedures, tools and standards for PET imaging using a Ge-68 NIST-traceable phantom. 3b: Define and validate robust quality assurance tools and standards for 4D PET/CT 3c: Define similar tools and standards for quantitative imaging response assessment using ACR and GE phantoms for MRI, including MRSI.
Specific Aim 4 : Adapt and Enhance Quantitative Image-based Response Assessment for Clinical Trials Decision-Support. 3a: In an existing, NCI-funded study of FDG and F-18 fiuorothymidine in head and neck cancer patients. 3b In an existing, NCI-funded study of FDG and C-11 acetate imaging in lung cancer and 3c: In a planned clinical trial of MRSI and F-18 fluorodopa in high-grade gliomas.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZCA1-SRLB-9 (J1))
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Nordstrom, Robert J
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University of Iowa
Schools of Medicine
Iowa City
United States
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Yankeelov, Thomas E; Mankoff, David A; Schwartz, Lawrence H et al. (2016) Quantitative Imaging in Cancer Clinical Trials. Clin Cancer Res 22:284-90
Beichel, Reinhard R; Van Tol, Markus; Ulrich, Ethan J et al. (2016) Semiautomated segmentation of head and neck cancers in 18F-FDG PET scans: A just-enough-interaction approach. Med Phys 43:2948
Fedorov, Andriy; Clunie, David; Ulrich, Ethan et al. (2016) DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research. PeerJ 4:e2057
Fuerst, Bernhard; Mansi, Tommaso; Carnis, Francois et al. (2015) Patient-specific biomechanical model for the prediction of lung motion from 4-D CT images. IEEE Trans Med Imaging 34:599-607
Anderson, Carryn M; Chang, Tangel; Graham, Michael M et al. (2015) Change of maximum standardized uptake value slope in dynamic triphasic [18F]-fluorodeoxyglucose positron emission tomography/computed tomography distinguishes malignancy from postradiation inflammation in head-and-neck squamous cell carcinoma: a prospecti Int J Radiat Oncol Biol Phys 91:472-9
Anderson, Carryn M; Sun, Wenqing; Buatti, John M et al. (2014) Interobserver and intermodality variability in GTV delineation on simulation CT, FDG-PET, and MR Images of Head and Neck Cancer. Jacobs J Radiat Oncol 1:006
Muruganandham, Manickam; Clerkin, Patrick P; Smith, Brian J et al. (2014) 3-Dimensional magnetic resonance spectroscopic imaging at 3 Tesla for early response assessment of glioblastoma patients during external beam radiation therapy. Int J Radiat Oncol Biol Phys 90:181-9
Menda, Yusuf; Buatti, John M (2013) PET imaging during radiotherapy of head and neck cancer. J Nucl Med 54:497-8
Song, Qi; Bai, Junjie; Han, Dongfeng et al. (2013) Optimal co-segmentation of tumor in PET-CT images with context information. IEEE Trans Med Imaging 32:1685-97
Liu, Fei-Fei; workshop participants; Okunieff, Paul et al. (2013) Lessons learned from radiation oncology clinical trials. Clin Cancer Res 19:6089-100

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