# Efficient and cost-effective breast cancer risk stratification using whole slide histopathology images

> **NIH NIH R21** · WAKE FOREST UNIVERSITY HEALTH SCIENCES · 2024 · $203,434

## Abstract

Efficient and cost-effective breast cancer risk stratification using whole-slide histopathology
images
Breast cancer prognosis depends highly on receptor status, as optimal treatment depends on
the presence or absence of overexpression of estrogen, progesterone, or HER-2/neu receptors.
To prevent over-treating patients with chemotherapy, it is crucial to quantify the risk of
recurrence for estrogen receptor (ER) positive (ER+), HER2 negative (HER2-) breast cancer. A
common assessment method to meet this need is the Oncotype DX (ODX) Recurrence Score.
Unfortunately, ODX and similar gene assays are expensive, time-consuming, and tissue
destructive. As an alternative, we propose estimating the ODX recurrence score using routine,
ubiquitous, and inexpensive hematoxylin and eosin (H&E) staining of biopsies. There are other
efforts to predict ODX recurrence risk from H&E. These automated methods detect histological
primitives (e.g., nuclei) often in specific, also automatically detected, anatomical regions (e.g.,
ducts, tubules, lumen, epithelium, and stroma). Classification is performed into two or three risk
categories, often collapsing two categories into one. The performance of these models is
promising but still modest. One way to improve the performance of the models is to train on
larger datasets; however, annotating larger datasets is challenging. Here, we propose an
automated method to predict ODX recurrence risk without annotations. If successful, this
method would have a wide range of applications, including but not limited to the availability of an
inexpensive, web-based tool to predict ODX in developing countries or rural areas with internet
access where standard Oncotype Dx assay would be cost-prohibitive or take too long to obtain.
Furthermore, our method would find use in clinical research where valuable tumor tissue could
be saved by obtaining correlative research data based on standard H&E-stained slides.

## Key facts

- **NIH application ID:** 10823271
- **Project number:** 5R21CA273665-02
- **Recipient organization:** WAKE FOREST UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Metin Nafi Gurcan
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $203,434
- **Award type:** 5
- **Project period:** 2023-04-06 → 2027-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10823271

## Citation

> US National Institutes of Health, RePORTER application 10823271, Efficient and cost-effective breast cancer risk stratification using whole slide histopathology images (5R21CA273665-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10823271. Licensed CC0.

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