# Dynamic imaging and tissue biomarker models to delineate indolent from aggressive breast calcifications

> **NIH NIH R01** · DUKE UNIVERSITY · 2022 · $403,142

## Abstract

ABSTRACT. Breast cancer screening programs suffer from false positive mammograms, unnecessary biopsies,
overdiagnosis, and overtreatment. A major contributor to the poor performance of screening mammography is
the diagnostic and prognostic uncertainty of mammographically detected calcifications. Breast calcifications
represent a biological continuum from benign disease to ductal carcinoma in situ (DCIS) to aggressive cancer.
Radiologists struggle to correlate their imaging appearance with the underlying pathology and roughly two-thirds
of biopsied calcifications return with a benign pathology. Although calcifications evolve dynamically over time,
the current management strategy relies heavily on the static appearance of calcifications from the most recent
mammogram. Most women in screening programs have multiple mammograms, yet this temporal information is
consistently underutilized in clinical decision making. There is thus an urgent need to quantify the dynamics of
calcifications from serial mammograms, and to characterize the relationship between calcification trajectories
and disease biology. In the absence of such innovation, increasingly sensitive screening modalities are expected
to further increase the burden of unnecessary diagnostic work-up and breast cancer overdiagnosis. The central
hypothesis of this proposal is that dynamic imageable and tissue biomarkers contain actionable diagnostic and
prognostic information about mammographic calcifications. The use of established diagnostic imaging
(mammography) in conjunction with investigational imageable biomarkers will enable testing of this hypothesis.
Key to this proposal will be the creation of a large database of retrospectively and prospectively collected cohorts
of patients with serial mammograms, tissue samples and clinical outcomes. This proposal will consist of three
specific aims: (1) Develop a static model of breast calcifications to improve the clinical performance of
mammography screening; 2) Develop a dynamic model of breast calcifications to predict histopathology and
DCIS prognosis; and 3) Combine the dynamic calcification model with tissue-based biomarkers of the underlying
evolutionary dynamics to delineate DCIS prognosis. The proposed research is highly innovative because it adds
the temporal dimension to computer-assisted classification of mammographic calcifications, yields a joint
characterization of calcification growth trajectories and lesion biology, and develops dynamic risk models to
predict invasive progression in women undergoing active monitoring for DCIS. This proposal will be co-led by
Dr. Grimm (breast radiologist) and Dr. Ryser (mathematical modeler) supported by a highly collaborative
multidisciplinary team with expertise in cancer biology, computer vision, and surgical oncology. The overall
objective of this proposal is to develop a dynamic imageable biomarker that delineates lethal cancer from non-
lethal disease by leveraging the temporal dimension of s...

## Key facts

- **NIH application ID:** 10448752
- **Project number:** 1R01CA271237-01
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Lars J Grimm
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $403,142
- **Award type:** 1
- **Project period:** 2022-09-15 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10448752

## Citation

> US National Institutes of Health, RePORTER application 10448752, Dynamic imaging and tissue biomarker models to delineate indolent from aggressive breast calcifications (1R01CA271237-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10448752. Licensed CC0.

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