# Digital breast tomosynthesis imaging biomarkers for breast cancer risk estimation

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2021 · $502,919

## Abstract

Project Summary
Studies have repeatedly shown that breast density, which limits mammographic sensitivity, is also a strong risk
factor for breast cancer. As a result, breast density information is increasingly utilized in guiding personalizing
breast cancer screening and prevention. Conventional 2D mammography, however, is inherently limited due to
the effect of tissue superimposition in estimating breast density. In addition, the commonly used measures of
mammographic density cannot fully capture the heterogeneity of the breast parenchymal pattern, shown to be
an important additional indicator of breast cancer risk. Breast Tomosynthesis is an emerging 3D x-ray modality
which offers superior, tomographic breast tissue visualization compared to 2D mammography. Multiple studies
have shown that screening with tomosynthesis reduces recalls while increasing cancer detection compared to
screening with mammography alone, which is currently fueling a broad implementation of tomosynthesis for
general population breast cancer screening. In addition to improved screening performance, our hypothesis is
that 3D measures of breast density and parenchymal pattern complexity from tomosynthesis can outperform
density measures from conventional 2D mammography, to improve breast cancer risk estimation. During the
previous phase of this award, we developed innovative methods for breast density and parenchymal texture
analysis in digital mammography and evaluated their association to breast cancer risk. Our studies have
shown compelling evidence that these measures provide powerful new imaging markers that can augment the
standard mammographic measures. This renewal application focuses on extending our work to the emerging
technology of tomosynthesis, by developing novel measures of breast tissue composition from tomosynthesis
and by determining their association to breast cancer risk. Towards this end, we will perform the largest
association study reported to-date for tomosynthesis and breast cancer risk, including a well-characterized and
diverse sample of 675 cases and 2700 controls nested within two large academic breast cancer screening
practices at the University of Pennsylvania (UPenn) and the Mayo Clinic. In AIM1 we will extend and optimize
our mammographic parenchymal analysis software for breast tomosynthesis using sophisticated computational
methods pioneered from our group for breast tomosynthesis; in AIM2 we will determine associations between
breast cancer and the novel tomosynthesis density and texture measures, using retrospective data analysis as
our training set; and in AIM3 we will perform independent validation using prospectively collected, ethnically
diverse, samples from both institutions. Within this setting, we will also evaluate the performance of measures
derived from synthetic digital mammograms, increasingly replacing conventional digital mammography images
in tomosynthesis acquisition. This study will be the first to evaluate tomosynthes...

## Key facts

- **NIH application ID:** 10131764
- **Project number:** 5R01CA161749-09
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Despina Kontos
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $502,919
- **Award type:** 5
- **Project period:** 2012-05-04 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10131764

## Citation

> US National Institutes of Health, RePORTER application 10131764, Digital breast tomosynthesis imaging biomarkers for breast cancer risk estimation (5R01CA161749-09). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10131764. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*