The University of Iowa (UI) has contributed significantly to the quantitative imaging network (QIN) goals and mission during its first grant cycle. The overall innovation of the UI QIN site is accelerated deliverable tool generation accomplished through a successfully implemented cloud-like distributed workflow infrastructure. Our diversely talented and experienced interdisciplinary team includes oncologists, nuclear medicine physicians, radiologists, physicists, electrical and computer engineers, bioinformaticists, and statisticians. We propose the following new specific aims that build creatively from previous work in a highly innovative fashion and help accelerate QIN progress during this funding period:
Specific Aim 1 : Develop a novel, robust imaging genomics-based decision support platform using a combination of our successful Phase-I developed and validated highly automated quantitative image analysis methods applied to linked and publically-available well curated image (TCIA) and molecular (TCGA) data warehouses along with an established outcomes database for H&N cancers. This will facilitate new methods necessary for future risk adaptive trials that will certainly include both genomic and quantitative image data.
Specific Aim 2 : Build and innovate based on Phase-I developed and validated image analysis tools: a) Apply highly and fully automated quantitative image analysis methods to a cooperative group data set of H&N cancers, b) Develop unique new tools through creative new image analysis methods for application to FLT/PET in H&N cancer, FLT/PET in pelvis and bone marrow, as well as DOTATOC PET/CT for liver metastases in neuroendocrine cancers. These novel approaches will be made publicly available and will contribute to future clinical trials, decision support, quantitative imaging response assessment and therapy targeting in a variety of cancer sites.
Specific Aim 3 : Create a novel link between our established work in PET quantification and calibration phantoms with our image analysis and decision support tools to create a clinically practical open source automated phantom analysis tool that can be applied to national efforts aimed to improve quantitative imaging quality assurance for clinical trials across multiple modalities including PET, CT, and MRI. This will provide a critical tool for improving the ease, accuracy and harmonization for clinical trials data acquisition.
Specific Aim 4 : Adapt, enhance and extend quantitative image-based response assessment in clinical trial decision-support through relevant active clinical trials. Several clinical trials are highlighted exploring:1) FLT/PET as a predictor of bone marrow activity and toxicity in pelvic malignancies treated with chemoradiotherapy, 2) DOTATOC as an indicator of disease burden and treatment response in neuroendocrine tumors and 3) quantitative MR imaging [T2, T1, T1, quantitative susceptibility mapping (QSM) and MRSI] as effective predictors of response in malignant glial tumors treated with intravenous high dose vitamin C. These trials will facilitate quantitative image analysis tool development, decision support tools and risk adaptive approaches in future clinical trials.

Public Health Relevance

This work can positively impact cancer care by determining the effectiveness of cancer treatment more accurately and earlier using detailed analysis of imaging. This may be used alone or combined with genetic information from tumors to help decision making for cancer treatments. Results will be shared so that work among groups will help apply these new imaging and decision making approaches more effectively.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
2U01CA140206-06
Application #
8964178
Study Section
Special Emphasis Panel (ZCA1-TCRB-Y (M2))
Program Officer
Nordstrom, Robert J
Project Start
2009-07-01
Project End
2020-06-30
Budget Start
2015-09-25
Budget End
2016-08-31
Support Year
6
Fiscal Year
2015
Total Cost
$617,490
Indirect Cost
$206,640
Name
University of Iowa
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
062761671
City
Iowa City
State
IA
Country
United States
Zip Code
52246
Byrd, Darrin; Christopfel, Rebecca; Buatti, John et al. (2018) Multicenter survey of PET/CT protocol parameters that affect standardized uptake values. J Med Imaging (Bellingham) 5:011012
Ulrich, Ethan J; Sunderland, John J; Smith, Brian J et al. (2018) Automated model-based quantitative analysis of phantoms with spherical inserts in FDG PET scans. Med Phys 45:258-276
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
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
Smith, Brian J; Beichel, Reinhard R (2017) A Bayesian framework for performance assessment and comparison of imaging biomarker quantification methods. Stat Methods Med Res :962280217741334
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
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

Showing the most recent 10 out of 28 publications