# MRI Radiomic Signatures of DCIS to Optimize Treatment

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2022 · $597,536

## Abstract

Abstract/Project Summary: The purpose of this study is to determine whether breast MRI radiomic features
can be utilized to optimize treatment of ductal carcinoma in situ (DCIS), the earliest form of breast cancer
diagnosed. Although DCIS survival rates approach 100%, there is concern that its management generally
results in overtreatment, exposing many of the 50,000 U.S. women diagnosed each year to unnecessary
anxiety and morbidity. The vast majority of DCIS is detected in asymptomatic women in whom suspicious
calcifications are identified on mammography and characterized using limited tissue histopathology.
Unfortunately, conventional imaging and pathology have not proven reliable for distinguishing low vs. high-risk
DCIS. Specifically, it is unclear at diagnosis which forms of DCIS will upstage to invasive disease or have an
ipsilateral breast recurrence (IBR) after treatment. This limited risk-stratification is due in part to inadequate
sampling of the entire DCIS lesion and an inability to account for peritumoral microenvironment features. This
results in unnecessary surgery, radiation therapy, and medical therapy for as many as half of women
diagnosed with DCIS. Breast MRI is commonly and easily performed, able to best depict DCIS span, and can
assess tumor and peritumoral heterogeneity rooted in biological features such as angiogenesis, making it an
appealing choice for a radiomics assay to improve DCIS risk assessments. The Quantitative Breast Imaging
Lab at the University of Washington has shown that quantitative MRI features are associated with DCIS grade,
a molecular marker of recurrence (Oncotype DX DCIS Score), and IBR. The Computational Biomarker Imaging
Group at the University of Pennsylvania has pioneered breast MRI radiomic phenotyping and shown radiomic
measures of breast cancers correlate with genomic features and recurrence. The Center for Statistical
Sciences at Brown University has expertise with radiomics, machine learning, and statistical analyses for
imaging trials from ECOG-ACRIN. In this collaborative application, we hypothesize that breast MRI radiomic
signatures of DCIS will result in distinct phenotypes that are prognostic and can be integrated with
clinical, molecular, and pathologic markers to optimize DCIS treatment. To test this hypothesis, we will
create a multi-institutional database of over 1400 MRIs, including exams from the ECOG-ACRIN E4112 trial,
with curated outcomes (e.g., upstage to invasion, DCIS Score, and IBR). Leveraging a novel approach to
harmonize multicenter data (nested-Combat radiomic feature standardization), we will discover and validate
MRI radiomic phenotypes and assess those phenotypes’ associations with invasive upstaging, Oncotype DX
DCIS Score, and 5- and 10-year IBR. Finally, we will determine whether integration of these phenotypes into
existing clinical prognostic indices (e.g., Van Nuys Prognostic Index) can provide more precise estimates of
IBR. If successful, this study will help...

## Key facts

- **NIH application ID:** 10537149
- **Project number:** 1R01CA268341-01A1
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Despina Kontos
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $597,536
- **Award type:** 1
- **Project period:** 2022-07-01 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10537149

## Citation

> US National Institutes of Health, RePORTER application 10537149, MRI Radiomic Signatures of DCIS to Optimize Treatment (1R01CA268341-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10537149. Licensed CC0.

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