R01CA264987

Radiomic and Genomic Predictors of Breast Cancer Risk - Abstract

Over 40,000 U.S. women will die of breast cancer each year. Screening mammography saves lives but also results in potential harms. Personalized screening regimens tailored to a woman's individual risk can both improve early detection of lethal cancers through more intensive regimens for high-risk women, and reduce over-screening and over-treatment of low-risk women. However, the current clinical breast cancer risk prediction models are insufficiently accurate for discriminating high-risk and low-risk women.

New radiomic deep learning algorithms, which automatically mine troves of breast tissue features from a woman's screening mammogram to predict her future cancer risk, have enormous potential to transform breast cancer screening, but have not been independently validated. New polygenic risk scores (PRS) for breast cancer also show promise for improving risk prediction, although still costly to implement on a population scale.

We propose to examine whether adding radiomic and genomic risk scores can significantly improve current clinical risk prediction models in a large, diverse population-based cohort of 178K women enrolled in Kaiser Permanente's Research Program on Genes, Environment and Health (RPGEH) who were screened with 2D full-field digital mammography (FFDM). We also propose to extend the best performing radiomic deep learning algorithms to diverse screening mammography systems utilized in two large health care settings in California and New York, including a cohort of 50K women screened with 3D digital breast tomosynthesis (DBT) in the Mount Sinai Health System (MSHS).

The specific aims are to:
(1) Evaluate the performance of radiomic deep learning breast cancer risk prediction models, estimate their associations with 5-year and 10-year breast cancer risk, and determine the extent to which the associations are independent of known clinical risk factors;
(2) Determine whether radiomic and genomic risk scores independently predict breast cancer risk, and explore potential differences by race/ethnicity and other clinical risk factors; and
(3) Transfer the best radiomic deep learning algorithm(s) from 2D FFDM to 3D tomosynthesis.

The proposed research will fill essential knowledge gaps needed to realize the potential of radiomics and genomics by validating new radiomic algorithms, quantifying the improvements in model performance above traditional risk factor models and new polygenic risk scores, exploring differences by race/ethnicity, and extending the best radiomic tools to diverse mammography systems utilized in two large multi-ethnic health care settings.

The Univeristy Of Texas M.D. Anderson Cancer Center

TO IDENTIFY CANCER RISKS AND RISK REDUCTION STRATEGIES, TO IDENTIFY FACTORS THAT CAUSE CANCER IN HUMANS, AND TO DISCOVER AND DEVELOP MECHANISMS FOR CANCER PREVENTION AND PREVENTIVE INTERVENTIONS IN HUMANS. RESEARCH PROGRAMS INCLUDE: (1) CHEMICAL, PHYSICAL AND MOLECULAR CARCINOGENESIS, (2) SCREENING, EARLY DETECTION AND RISK ASSESSMENT, INCLUDING BIOMARKER DISCOVERY, DEVELOPMENT AND VALIDATION, (3) EPIDEMIOLOGY, (4) NUTRITION AND BIOACTIVE FOOD COMPONENTS, (5) IMMUNOLOGY AND VACCINES, (6) FIELD STUDIES AND STATISTICS, (7) CANCER CHEMOPREVENTION AND INTERCEPTION, (8) PRE-CLINICAL AND CLINICAL AGENT DEVELOPMENT, (9) ORGAN SITE STUDIES AND CLINICAL TRIALS, (10) HEALTH-RELATED QUALITY OF LIFE AND PATIENT-CENTERED OUTCOMES, AND (11) SUPPORTIVE CARE AND MANAGEMENT OF SYMPTOMS AND TOXICITIES. SMALL BUSINESS INNOVATION RESEARCH (SBIR) PROGRAM: TO EXPAND AND IMPROVE THE SBIR PROGRAM, TO STIMULATE TECHNICAL INNOVATION, TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT FUNDING, TO INCREASE SMALL BUSINESS PARTICIPATION IN FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION IN INNOVATION AND ENTREPRENEURSHIP BY WOMEN AND SOCIALLY/ECONOMICALLY DISADVANTAGED PERSONS. SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAM: TO STIMULATE AND FOSTER SCIENTIFIC AND TECHNOLOGICAL INNOVATION THROUGH COOPERATIVE RESEARCH AND DEVELOPMENT CARRIED OUT BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO FOSTER TECHNOLOGY TRANSFER THROUGH COOPERATIVE RESEARCH AND DEVELOPMENT BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT FUNDING, AND FOSTER PARTICIPATION IN INNOVATION AND ENTREPRENEURSHIP BY WOMEN AND SOCIALLY/ECONOMICALLY DISADVANTAGED PERSONS.

93.393 - Cancer Cause and Prevention Research

National Cancer Institute (NCI) [HHS - NIH]

Houston, Texas 770304009 United States

Single Zip Code

NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed) (PA-20-185)

Amendment Since initial award the End Date has been extended from 08/31/26 to 11/30/26 and the total obligations have increased 375% from $712,593 to $3,381,964.