Breast cancer is extremely common, striking 1 in 8 American women, and is the second leading cause of cancer death among women in the U.S. Earlier detection through screening is a fundamental way to improve survival. Women with dense breasts on screening mammography have increased risk of developing breast cancer, and increased breast density reduces the sensitivity of mammography for cancer detection. As a result, a growing number of states have passed legislation mandating that providers inform women if their breast density is increased, warranting supplemental screening. Dynamic contrast enhanced MRI (DCE-MRI) is the most sensitive technique for detecting breast cancer, particularly in women with dense breasts. However, overlap in the appearance of benign and malignant breast lesions on DCE-MRI can produce many false positives, and there is a clear need to improve the specificity of breast MRI in order to limit the number of unnecessary biopsies that will result from expanded use of this highly sensitive screening tool. Furthermore, the high costs and safety concerns of gadolinium-based contrast agents limit the accessibility of breast MRI screening for many women, so identifying a non-contrast alternative for detecting breast cancer would have strong clinical impact. A promising adjunct MRI technique is diffusion-weighted imaging (DWI), which indirectly assesses tissue microstructure and can provide complementary information to DCE-MRI for lesion characterization. We have previously demonstrated significant differences in apparent diffusion coefficient (ADC) values between benign and malignant lesions and that ADC measures can increase the positive predictive value of conventional breast MRI. We recently confirmed these findings in a multicenter trial (ACRIN 6702). We have also observed that many mammographically and clinically-occult cancers are visible on DWI without using a contrast agent, suggesting DWI could provide a faster, less expensive, and safer screening option than DCE-MRI, which is the topic being explored in the parent grant (NIH R01-CA207290).
Aims of the proposed study will be to standardize and validate technical approaches to overcome hurdles in translating ADC as diagnostic biomarker into clinical practice. Image corrections to address known inaccuracies related to gradient nonlinearities, misregistration and geometric distortion will be implemented, and methodology for measuring lesion ADC will be optimized to maximize diagnostic performance and reliability. The assay will be refined using the existing ACRIN 6702 multicenter dataset to ensure wide generalizability and tested prospectively at several sites. If successful, a standardized ADC assay to more accurately assess the probability of malignancy at the time of detection would reduce the number of unnecessary (benign) biopsies performed as a result of breast MRI screening, with or without contrast.

Public Health Relevance

to Public Health: The apparent diffusion coefficient (ADC) is an imaging marker that has shown potential value to improve ability to distinguish cancer from benign lesions on breast MRI and significantly reduce the number of unnecessary biopsies performed. However, standardization of the process to determine ADC of breast lesions is critically needed to increase reliability and facilitate translation of this valuable biomarker to clinical practice.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
3R01CA207290-04S1
Application #
10047516
Study Section
Cancer Biomarkers Study Section (CBSS)
Program Officer
Zhang, Yantian
Project Start
2017-06-01
Project End
2022-05-31
Budget Start
2020-06-01
Budget End
2021-05-31
Support Year
4
Fiscal Year
2020
Total Cost
Indirect Cost
Name
University of Washington
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195
Amornsiripanitch, Nita; Rahbar, Habib; Kitsch, Averi E et al. (2018) Visibility of mammographically occult breast cancer on diffusion-weighted MRI versus ultrasound. Clin Imaging 49:37-43