# Machine learning for risk-adjusted breast MRI screening

> **NIH NIH R01** · CITY COLLEGE OF NEW YORK · 2022 · $633,261

## Abstract

SUMMARY
Magnetic Resonance Imaging (MRI) is the most sensitive imaging modality for breast cancer diagnosis to date.
Women with a strong family history or related genetic mutations have an elevated risk of breast cancer and are
recommended to participate in yearly MRI screenings. However, the rate of detection in this high-risk cohort is
small, prompting a desire to reduce unnecessary MRI exams. The basic hypothesis of this project is that within
the screening cohort the individual risk of a future cancer can be estimated based on the appearance of breast
MRI and mammograms today. In preliminary work we have already identified low-risk women that could have
omitted a screening session without missing a new cancer. The discovery of this lower-risk subgroup was
made possible by modern deep-learning tools developed in preliminary work. Memorial Sloan Kettering Cancer
Center (MSK) has accrued a database of approximately 70,000 breast MRI exams over 18 years along with
the patients’ clinical outcomes. This unprecedented resource enables the training of modern machine learning
“from the ground-up” to extract and classify volumetric MRI features. The specific aims of this project are as
follows. Aim 1 (Data curation): Systematic analysis of the large dataset accrued at MSK requires careful
curation including image content, image quality, pathology results, clinical follow-up, as well as demographic
and genomic information. The outcome of this Aim is a curated dataset that can broadly benefit future technical
efforts in breast diagnosis. Aim 2 (Deep learning): To make risk stratification quantitative we propose to
analyze the MRI scans using modern deep networks that have been trained to identify the location and extent
of a cancer. We will then transfer the MRI features of these trained networks as well as networks trained on
mammograms to the task of diagnosis and risk assessment. The intended outcome of this Aim are predictive
models with human-level performance at diagnosis and segmentation. Aim 3 (Risk adjusted screening): To
reduce the burden of screening while maintaining sensitivity we will estimate the risk of finding a malignant
tumor in the future, based on the present MRI exam and most recent mammogram as well as patient
information. The machine-estimated risk will be used in a retrospective analysis to determine the primary
outcome, namely, the number of exams that could have been omitted by scheduling a longer screening interval
without compromising sensitivity. This will be repeated on newly accrued data at MSK, Duke and Johns
Hopkins University (JHU) as secondary sites. Once validated, the risk-prediction model will be publicly
released to encourage data sharing and clinical adoption. The preliminary work performed over the last two
years has brought together a unique interdisciplinary team including clinical investigators on breast MRI at
MSK, and machine-learning and medical imaging experts at CCNY, Duke and JHU. The platform technology...

## Key facts

- **NIH application ID:** 10316235
- **Project number:** 5R01CA247910-02
- **Recipient organization:** CITY COLLEGE OF NEW YORK
- **Principal Investigator:** LUCAS C PARRA
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $633,261
- **Award type:** 5
- **Project period:** 2020-12-09 → 2025-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10316235

## Citation

> US National Institutes of Health, RePORTER application 10316235, Machine learning for risk-adjusted breast MRI screening (5R01CA247910-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10316235. Licensed CC0.

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