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.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01CA140206-03
Application #
8244350
Study Section
Special Emphasis Panel (ZCA1-SRLB-9 (J1))
Program Officer
Nordstrom, Robert J
Project Start
2010-04-01
Project End
2015-03-31
Budget Start
2012-04-01
Budget End
2013-03-31
Support Year
3
Fiscal Year
2012
Total Cost
$545,255
Indirect Cost
$176,734
Name
University of Iowa
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
062761671
City
Iowa City
State
IA
Country
United States
Zip Code
52242
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
Bayouth, John E; Casavant, Thomas L; Graham, Michael M et al. (2011) Image-based biomarkers in clinical practice. Semin Radiat Oncol 21:157-66