To aid clinical decision making and improve oncological patient management, current multi-center/multi- platform clinical trials evaluate quantitative diffusion imaging for tissue response and quantitative lesion characterization for translational oncology applications. While providing data analysis and quality control for ongoing ECOG-ACRIN clinical oncology trials, the academic partners of the proposed AIP have identified the major source of spatial variability in quantitative diffusion measurements across scanner systems related to platform-specific nonuniformity bias in diffusion weighting. This systematic bias caused by platform-dependent gradient designs confounds quantitative diffusion metrics for characterization of tissue pathology leading to inconclusive findings and increasing the requisite subject numbers and trial costs. Through active involvement with national and international quantitative imaging initiatives (NCI-QIN, RSNA-QIBA, ISMRM, QuIC- ConCePT) as well as collaboration between several academic centers and three major vendors the consensus has been established that robust and timely solution of the technical hurdle for quantitative DWI trials requires merging expertise among commercial scientists/engineers and academic researchers to implement practical correction of spatial bias across diverse clinical MRI platforms. The goal of the proposed AIP is to design, evaluate and implement practical DW bias correction tools for quantitative diffusion imaging applications in clinical cancer trails acrss three major MRI vendors. These tools will eliminate a dominant source of scanner- specific bias manifesting as cross-platform, cross-exam variability and thereby advance longitudinal and multi- institutional translational cancer research that utilizes quantitative diffusion imaging to improve management of oncology patients. The goal will be achieved through Aim1: designing and testing practical bias correction procedures to improve quantitative diffusion imaging across representative clinical platforms and through Aim2: evaluation and implementation of practical correction procedures to enhance precision of tissue diffusivity metrics generated in clinical oncology trials. Academic members of the proposed partnership are leading experts in clinical MRI and diffusion imaging and have active collaboration with three dominant clinical MRI manufactures. The PI institution has developed a widely-utilized phantom for diffusion imaging quality control in numerous translational oncology clinical trials. Partnership members have pioneered investigations of practical diffusion bias characterization and correction procedures. Accomplishment of the project aims will eliminate significant instrumental bias that confounds current multi-center/multi-platform clinical trials that employ quantitative diffusion imaging.

Public Health Relevance

Current multi-center clinical trials evaluate quantitative diffusion imaging for systematic monitoring and early prediction of therapy response, as well as noninvasive characterization of cancer. Significant platform-dependent spatial bias in diffusion weighting both corrupts tissue diffusivity metrics generated by a single MRI platform and increases variability across platforms. Increased technical variance greatly amplifies requisite subject numbers and proportional trial cost, as well as is liable to false findings from underpowered studies. The goal of this project is to merge academic research with industrial expertise to design, evaluate and implement practical bias correction across dominant clinical MRI platforms that reduces systematic errors in oncologic diffusion imaging trials. Ultimately, this will advance diagnostic, prognostic and treatment monitoring based on quantitative diffusion imaging technology toward more effective clinical management of cancer patients.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA190299-05
Application #
9744601
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Zhang, Huiming
Project Start
2015-08-10
Project End
2020-07-31
Budget Start
2019-08-01
Budget End
2020-07-31
Support Year
5
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of Michigan Ann Arbor
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
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