# SCH: Topological Methods for Breast Tissue Quantification

> **NIH NIH R01** · STATE UNIVERSITY NEW YORK STONY BROOK · 2024 · $300,000

## Abstract

To better understand breast cancer and its response to treatment, the key is to understand breast tissue
architecture. Modifications to tissue architecture are a direct consequence of the rearrangement of fine-
grained structures such as the fibroglandular tissue and vessels, brought about by events including, but not
limited to angiogenesis, and treatments such as radiation therapy. Changes in breast structural topology has
the potential to influence cancer risk, prognosis, and treatment response; however, this has not been
extensively studied nor quantified. Existing work is limited only to analysis of features such as radiomics.
 This project, undertaken by a multi-disciplinary team comprising topologists, computer scientists, imaging
informatics experts, and clinicians, aims to develop advanced methods for topological modeling and
reasoning with the primary hypothesis that these algorithms can ascertain weakening of tissue architecture
on imaging. This can help in identifying high-risk cases that are prone to cancer manifestation and cancer
recurrence. Specifically, the project proposes to develop TopoQuant, a suite of Topology Data Analysis
(TDA)-driven techniques for extracting and interpreting fine-grained topological information from breast
parenchyma, based on both 2D and 3D breast imaging. TopoQuant will generate high-quality annotations of
breast tissue, produce advanced topological descriptors to characterize breast tissue complex, learn
topology-informed prediction models using these topological features, as well as provide clinically intuitive
visualization of relevant topological features.
 The proposed work will advance both TDA and cancer research by create new TDA methodologies to
extract and analyze rich structural information from breast imaging. This will be achieved through: (1)
Developing novel topological algorithms to effectively capture the structural diversity in breast parenchyma;
(2) Addressing the challenge of limited data availability by devising 2D to 3D mapping methodologies that
yield highly actionable topological information even when the available data is sparse or restricted; (3)
Developing algorithms to co-harness the powerful learning ability of ‘black-box’ deep networks with
biologically-grounded ‘glass-box’ topological descriptors; and (4) Constructing interpretable TDA frameworks
specifically tailored for assessing cancer risk and radiation treatment response in breast tissue. Our
algorithms will reveal topological insights from breast tissue structures, enabling clinicians and researchers
to better comprehend, plan, and evaluate the effectiveness of treatments in breast cancer patients.
RELEVANCE (See instructions):
The research will enhance our understanding of breast cancer, potentially improving early detection and
treatment, thereby benefiting women’s health and quality of life. The novel methods for analyzing radiology
scans will have significant implications across multiple disciplines, including n...

## Key facts

- **NIH application ID:** 11062792
- **Project number:** 1R01CA297843-01
- **Recipient organization:** STATE UNIVERSITY NEW YORK STONY BROOK
- **Principal Investigator:** Chao Chen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $300,000
- **Award type:** 1
- **Project period:** 2024-08-01 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11062792

## Citation

> US National Institutes of Health, RePORTER application 11062792, SCH: Topological Methods for Breast Tissue Quantification (1R01CA297843-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/11062792. Licensed CC0.

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