# Understanding the biological basis for the association between parenchymal texture features and breast cancer risk

> **NIH NIH R01** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2021 · $604,724

## Abstract

Breast composition is a potential breast biomarker, but its utility has been limited by measurement methods.
Visually-assessed qualitative scales capture within-breast heterogeneity but are subjective and lack
reproducibility. In contrast, quantitative automated assessments of global breast density are reproducible, but
contain no information about within-breast variation. Limitations of both of these approaches can be overcome
with the measurement of parenchymal texture features. Texture features are quantitative measures that estimate
complex characteristics of pixel density in the breast image, ranging from descriptive statistics to higher order
statistics that describe spatial relationships and structural patterns. Prior studies have shown that texture features
independently predict breast cancer risk. However, little is known about the biological mechanisms driving that
risk relationship. The objective of this study is to identify the biological processes associated with parenchymal
texture features. The rationale is that direct evidence that texture features reflect specific biological
properties will provide the basis for development of texture features as a dynamic marker of breast cancer
risk and prognosis. This study will pursue three aims. Using a case-control analysis, Aim 1 will identify the
texture features that are independently associated with newly-diagnosed breast cancer among women
attending breast cancer screening. Aim 2 will evaluate how the texture features that were associated with
breast cancer in this population vary with estrogen levels, through (i) cross-sectional analysis of texture features
and 15 urinary estrogens and estrogen metabolites, and (ii) analyses of longitudinal change in texture
features among breast cancer patients treated with anti-estrogenic therapy. Aim 3 will evaluate
associations between texture features and breast histologic characteristics (tissue composition, benign
breast disease/LCIS, measures of lobular involution) among women with a benign biopsy. Analyses will
draw on existing mammograms, biopsy specimens, and electronic health records from women participating
in mammography at the University of North Carolina; urine will be collected prospectively. Texture features
will be measured using a novel lattice-based grid method developed and validated by members of the study
team that allows information from the whole breast to inform the texture measurements. These analyses
will establish: the magnitude of the relationship between lattice-based texture features and breast cancer
in a general screening population (Aim 1); the extent to which texture features may act as biosensors of
breast estrogen/anti-estrogen activity (Aim 2); and whether texture features can serve as a radiologic
surrogate of histologic characteristics that have known associations with breast cancer risk (Aim 3). These
results will clarify the potential role of parenchymal texture features as predictors of breast cancer risk a...

## Key facts

- **NIH application ID:** 10241446
- **Project number:** 5R01CA237129-03
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Sarah Jane Nyante
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $604,724
- **Award type:** 5
- **Project period:** 2019-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10241446

## Citation

> US National Institutes of Health, RePORTER application 10241446, Understanding the biological basis for the association between parenchymal texture features and breast cancer risk (5R01CA237129-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10241446. Licensed CC0.

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