# Breast Cancer Detection Consortium

> **NIH NIH U01** · DUKE UNIVERSITY · 2020 · $438,654

## Abstract

Abstract
 Mammography is an early detection modality for breast cancer that is implemented
widely in the United States, has established benchmarks of performance, and in most studies
throughout the world has been demonstrated to reduce mortality due to the disease. This
relatively inexpensive x-ray imaging of the breast also provides a location that can be directly
sampled through needle biopsy which leads generally to an unambiguous pathologic diagnosis
of invasive cancer, carcinoma in situ, or benign findings. No system is perfect and
mammographic screening, particularly in the US, prompts over 1.6 million biopsies per year
detecting approximately 230,000 invasive and 60,000 non-invasive cancers for a positive
predictive value of less than 20%. There may be substantial room to improve on this and
reduce the number of biopsies but this improvement must not sacrifice detection rates so the
negative predictive value (NPV, identification of true negatives) must remain very high. In this
Biomarker Development Laboratory application, we propose to test whether a combination of
mammographic feature analysis and candidate biomarkers that we have identified can achieve
an NPV that would be acceptable to patients and providers to prevent unnecessary breast
biopsies. One of the biomarkers is a type of circulating giant cell termed “Cancer Associated
Macrophage Like” (CAML) that can only be detected using freshly drawn whole blood, we
propose to conduct a prospective trial at Duke University in women undergoing breast cancer
diagnosis. Our realistic goal is to accrue ~1000 women over the course of 4 years for which full
field digital mammography has been performed. The images will undergo feature extraction for
decision modeling. Blood will be analyzed for the presence and type of CAML cells,
immunosignaturing using the high density peptide arrays developed by Stephen Johnston at
Arizona State, and measurements of two specific analytes that have the highest sensitivity and
specificity for basal type cancers, CA125 and TP53 autoantibodies. As feature analysis from
imaging alone can achieve, at least for masses on mammography, an AUC of ~0.9, the study is
designed to determine whether the biomarkers have sufficient complementary information to the
imaging and each other to increase the AUC to 0.95 allowing us to identify a threshold where
there is a 98% NPV. We will make use of the most careful and consistent standard operating
procedures, the best candidate biomarkers, and the most well developed imaging algorithms to
make this a definitive study.

## Key facts

- **NIH application ID:** 9998907
- **Project number:** 5U01CA214183-05
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Jeffrey R. Marks
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $438,654
- **Award type:** 5
- **Project period:** 2016-09-19 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9998907

## Citation

> US National Institutes of Health, RePORTER application 9998907, Breast Cancer Detection Consortium (5U01CA214183-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9998907. Licensed CC0.

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