We propose to develop, optimize and validate novel DW-MRI acquisition and modeling methods, which address non-Gaussian water diffusion and perfusion effects through diffusion kurtosis imaging and non- Gaussian intravoxel incoherent motion imaging and provide more specific measures of tissue structure and biology. Additionally, we will develop and implement advanced image processing tools to maximize the biologic information from the tumor/tissue provided by the imaging data. The essence of our timely proposal lies in it being the first multi-center, imaging trial to identify quantitative imaging biomarkers as early response to therapy indicators, which interrogate tumor biology in accordance with the central mission of the NCI Quantitative Imaging Network. It will address an urgent, unmet need in clinical trials for recurrent/metastatic (R/M) head and neck cancers. This UO1 proposal is in response to PAR-14-116 and the specific aims outlined in the proposal are as follows:
Aim 1 : To develop and standardize a multi b-value reduced field of view (rFOV) DW-MRI acquisition method and non-mono exponential modeling DW-MRI for oncology applications;
Aim 2 : To develop and implement optimal model methodology with advanced image segmentation and image feature analysis in patients with R/M malignancies in the HN region for oncology applications;
and Aim 3 : To establish the next generation DW-MRI biomarkers as early response to therapy indicators in experimental therapies using R/M HN squamous cell carcinoma (SCC) as a proof of principle model. We hypothesize that imaging metrics derived from newer methods can be used as quantitative imaging biomarkers for assessing early therapeutic efficacy in R/M HNSCC. The principles of identifying robust, reliable and quantitative imaging biomarkers derived from DW-MRI and image feature analysis remain similar and such imaging protocols, after appropriate adaptation, can have a wider clinical application, including their use in treating other solid tumors.

Public Health Relevance

We propose a multi-center imaging trial to develop, implement and validate newer diffusion weighted-MRI (DW-MRI) acquisition and modeling methods, which address non-Gaussian water diffusion and perfusion effects through diffusion kurtosis imaging and non-Gaussian intravoxel incoherent motion imaging and provide more specific measures of tissue structure and biology. The repeatability and reproducibility of these techniques will be tested in phantoms and patient volunteers across two clinical sites, Memorial Sloan- Kettering Cancer Center and Columbia University Medical Center. We will identify the new imaging biomarkers as early response to therapy indicators for recurrent/metastatic (R/M) head and neck cancer patients. These imaging biomarkers will interrogate tumor biology with next generation diffusion methods in accordance with the central mission of the NCI Quantitative Imaging Network. The multi-center imaging trial will address an urgent, unmet need in clinical oncology trials for R/M head and neck cancers.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01CA211205-02
Application #
9482702
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Tata, Darayash B
Project Start
2017-07-01
Project End
2022-06-30
Budget Start
2018-07-01
Budget End
2019-06-30
Support Year
2
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Sloan-Kettering Institute for Cancer Research
Department
Type
DUNS #
064931884
City
New York
State
NY
Country
United States
Zip Code
10065
Shukla-Dave, Amita; Obuchowski, Nancy A; Chenevert, Thomas L et al. (2018) Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials. J Magn Reson Imaging :
Malyarenko, Dariya; Fedorov, Andriy; Bell, Laura et al. (2018) Toward uniform implementation of parametric map Digital Imaging and Communication in Medicine standard in multisite quantitative diffusion imaging studies. J Med Imaging (Bellingham) 5:011006
Newitt, David C; Malyarenko, Dariya; Chenevert, Thomas L et al. (2018) Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network. J Med Imaging (Bellingham) 5:011003