# Quantitative imaging tools to derive DW-MRI oncological biomarkers

> **NIH NIH U01** · SLOAN-KETTERING INST CAN RESEARCH · 2020 · $681,815

## Abstract

Abstract:
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.

## Key facts

- **NIH application ID:** 9957032
- **Project number:** 5U01CA211205-04
- **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH
- **Principal Investigator:** AMITA DAVE
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $681,815
- **Award type:** 5
- **Project period:** 2017-07-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9957032

## Citation

> US National Institutes of Health, RePORTER application 9957032, Quantitative imaging tools to derive DW-MRI oncological biomarkers (5U01CA211205-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9957032. Licensed CC0.

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