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.
|Schoenfeld, Joshua D; Sibenaller, Zita A; Mapuskar, Kranti A et al. (2017) O2?- and H2O2-Mediated Disruption of Fe Metabolism Causes the Differential Susceptibility of NSCLC and GBM Cancer Cells to Pharmacological Ascorbate. Cancer Cell 31:487-500.e8|
|Yusung Kim; Patwardhan, Kaustubh Anil; Beichel, Reinhard R et al. (2017) Development of a radiobiological evaluation tool to assess the expected clinical impacts of contouring accuracy between manual and semi-automated segmentation algorithms. Conf Proc IEEE Eng Med Biol Soc 2017:3409-3412|
|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|
|McGuire, Sarah M; Bhatia, Sudershan K; Sun, Wenqing et al. (2016) Using [(18)F]Fluorothymidine Imaged With Positron Emission Tomography to Quantify and Reduce Hematologic Toxicity Due to Chemoradiation Therapy for Pelvic Cancer Patients. Int J Radiat Oncol Biol Phys 96:228-39|
|Pierce II, Larry A; Byrd, Darrin W; Elston, Brian F et al. (2016) An algorithm for automated ROI definition in water or epoxy-filled NEMA NU-2 image quality phantoms. J Appl Clin Med Phys 17:440–456|
|Farahani, Keyvan; Kalpathy-Cramer, Jayashree; Chenevert, Thomas L et al. (2016) Computational Challenges and Collaborative Projects in the NCI Quantitative Imaging Network. Tomography 2:242-249|
|Yankeelov, Thomas E; Mankoff, David A; Schwartz, Lawrence H et al. (2016) Quantitative Imaging in Cancer Clinical Trials. Clin Cancer Res 22:284-90|
|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|
|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 43:2948-2964|
|Kurland, Brenda F; Aggarwal, Sameer; Yankeelov, Thomas E et al. (2016) Accrual Patterns for Clinical Studies Involving Quantitative Imaging: Results of an NCI Quantitative Imaging Network (QIN) Survey. Tomography 2:276-282|
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