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Last Updated: 10/26/18
Quantitative Imaging Network (QIN)

University of California at San Francisco

Quantitative Imaging for Assessing Breast Cancer Response to Treatment

Nola Hylton-Watson, Ph.D.
nola.hylton@ucsf.edu
Grant Number: U01 CA225427-01

Diffusion-weighted MRI (DWI) can measure treatment-induced alterations in tumor microstructure and cellularity and can provide distinct and complementary information to Dynamic Contrast Enhanced — MRI (DCE-MRI) for the non-invasive assessment of breast cancer response to treatment. However, limitations in breast DWI quality and accuracy prevent a full demonstration of its potential. The goal of this project is to implement effective, imaging-based strategies combining DCE-MRI and DWI to assess response for breast cancer patients receiving pre-operative (neoadjuvant) chemotherapy. This project builds on the prior NCI Quantitative Imaging Network (QIN) U01 grant award CA151235 entitled “Quantitative Imaging for Assessing Breast Cancer Response to Treatment” and addresses the needs for improved accuracy, standardization and consistency of breast MRI to perform quantitative assessment of treatment response across multiple clinical centers. The new QIN project will continue to advance quantitative MRI methods in the context of the I-SPY 2 TRIAL, an adaptive Phase II trial of targeted agents for breast cancer. The UCSF team aims to use diagnostic models applied to the expanding I-SPY 2 cohorts to maximize the biomarker performance of imaging measurements and to construct decision tools to enable rational strategies for treatment modification. In prior work the team had developed and implemented image quality control and assessment processes for breast diffusion-weighted MRI (DWI) that were utilized in the American College of Radiology Imaging Network (ACRIN) trial 6698, an imaging sub-study of I-SPY 2 testing DWI for prediction of response. Initial results showed excellent repeatability of apparent diffusion coefficient (ADC) measurements using a standardized 4 b-value protocol and change in ADC with treatment was found to be predictive of pathologic complete response (pCR). In parallel efforts, the team has worked with QIN collaborators at University of Michigan and industrial partners to develop gradient non-linearity correction and B0 inhomogeneity correction methods for ADC quantification. The team has also collaborated with the National Institute of Standards and Technology (NIST) to develop a universal breast MRI phantom for standardization of breast MRI in clinical trials. In this new U01 project the UCSF team will evaluate these methods on the multiple vendor platforms in I-SPY 2 with particular focus on maximizing the combined performance of breast DCE-MRI and DWI. It is anticipate that collective improvements in image acquisition, standardization, use of quality benchmarks and pixel based metrics will lead to overall improvements in ADC measurement. The improved metrics will be tested in predictive models for pathologic response and survival in I-SPY 2.