The full impacts of digital mammography and computer-aided diagnostic (CAD/QIA) systems on the performance of diagnostic mammography are yet to be realized. Lesion composition as described by its 3 compositional thicknesses of protein, lipid, and water (3CB) was recently discovered to be a strong descriptor of abnormal breast lesions. The long-term goal of this project is to reduce unnecessary breast biopsies by creating diagnostic imaging models using the strongest CAD/QIA algorithms incorporating advances such as 3CB. Our objective is to quantify lipid-protein-water signatures around CAD/QIA markers to better predict malignant findings. Our central hypothesis is that lesion composition can be combined with existing CAD/QIA methods to improve the specificity of cancer diagnosis and reduce the number of unnecessary biopsies.
Our specific aims are as follows: to 1) investigate the sensitivity and specificity of localized 3CB to distinguish breast cancer from benign lesions on prospectively acquired diagnostic mammograms of women recommended to undergo biopsy, 2) compare the sensitivity and specificity of 3CB to an established CAD/QIA method and conventional morphological BI-RADS descriptors, 3) develop a predictive diagnostic model to quantify the probability mammographic findings require biopsy versus don't require biopsy based on clinical risk factors, CAD/QIA measures and 3CB measures, and secondary) investigate advantages of 3-dimensional 3CB signatures using dual-energy digital breast tomosynthesis. The working hypotheses for each are as follows:
aim 1 - that unique 3CB signatures of lipid, protein, and water exist for breast cancer versus benign lesions and that this information can be used to better identify lesions that require breast biopsy aim 2 - that automated diagnostic CAD/QIA will yield quantitative lesion features that either correlate with or complement (independent) to the compositional signatures, aim 3 - that localized 3CB measures and established CAD/QIA measures are independent methods that assess different predictors of breast cancer and benign lesions and that the combination of measures from the two methods in a single model will increase the sensitivity and specificity from either one alone, secondary aim - that 3D mammography will provide more accurate lesions compositions than 2D imaging. The research's innovation is the combination of the two independent imaging risk markers: 3CB and a powerful CAD/QIA model, and compares it to the clinical standards used by radiologist. The expected outcomes include 1) a novel 3CB and CAD/QIA combined and accessible technology that will yield improved discernibility between cancerous and benign mammographic findings, 2) extensive, and biologically relevant knowledge on how lesion composition correlates with CAD/QIA findings, and 3) an demonstration of an optimized image-based predictive model for malignancy. We expected an important positive impact because more accurately identification of women with and without breast cancer will reduce the harm of unnecessary biopsies.

Public Health Relevance

The proposed studies are relevant to public health because they have the potential to increase the effectiveness of diagnostic mammography. Thus, this advance is expected to have a high impact to the health and well-being of women because mammography is currently the primary diagnostic tool for early breast cancer detection, and over two-thirds of women diagnosed through mammography do not have cancer, resulting in many unnecessary painful invasive procedures. This is relevant to the part of NIH's mission which focuses on the prevention of disease by supporting research in the diagnosis of human diseases.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
1R01CA166945-01A1
Application #
8439678
Study Section
Epidemiology of Cancer Study Section (EPIC)
Program Officer
Henderson, Lori A
Project Start
2013-03-06
Project End
2018-02-28
Budget Start
2013-03-06
Budget End
2014-02-28
Support Year
1
Fiscal Year
2013
Total Cost
$635,168
Indirect Cost
$172,702
Name
University of California San Francisco
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
094878337
City
San Francisco
State
CA
Country
United States
Zip Code
94143
Malkov, Serghei; Shepherd, John (2014) Combining 3D optical imaging and dual energy absorptiometry to measure three compositional components. Proc SPIE Int Soc Opt Eng 8937:893714
Drukker, Karen; Duewer, Fred; Giger, Maryellen L et al. (2014) Mammographic quantitative image analysis and biologic image composition for breast lesion characterization and classification. Med Phys 41:031915
Malkov, Serghei; Kerlikowske, Karla; Shepherd, John (2014) Automated Volumetric Breast Density derived by Shape and Appearance Modeling. Proc SPIE Int Soc Opt Eng 9034:90342T