# Quantitative assessment of autologous fat grafting in breast cancer treatment using 3D imaging

> **NIH NIH R21** · UNIVERSITY OF CHICAGO · 2024 · $217,493

## Abstract

Project Abstract
Autologous fat grafting (AFG) is a procedure whereby adipose tissue is processed and transferred from
one part of the body to another to add volume and address contour irregularity in soft tissue reconstruction.
Anecdotally, we see improvement in tissue fibrosis and pain with standard grafting after radiation treatment
indicating more than just a volume benefit of AFG. We have recently discovered that the transferred fat or
adipose brings in healthy progenitor cells, growth factors and immune cells helpful in tissue regeneration and
repair. Thus, purified graft is effectively a biological scaffold which can be modified to direct tissue healing or
targeted therapies. One of the hurdles to clinical translation is the variability in graft retention and our ability
to follow the graft over time. The long-term goal of our research is to investigate the capacity of engineered
adipose grafts to improve clinical outcomes by mitigating inflammation and promoting graft retention. We
propose a feasibility study investigating a novel imaging platform to quantitatively assess the overall volume
and three-dimensional (3D) shape of the breast after fat grafting in cancer treatment.

## Key facts

- **NIH application ID:** 10791131
- **Project number:** 1R21EB035242-01
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Summer E Hanson
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $217,493
- **Award type:** 1
- **Project period:** 2024-04-01 → 2027-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10791131

## Citation

> US National Institutes of Health, RePORTER application 10791131, Quantitative assessment of autologous fat grafting in breast cancer treatment using 3D imaging (1R21EB035242-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10791131. Licensed CC0.

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