# Deep interpretation of mammographic images in breast cancer screening

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2021 · $357,994

## Abstract

Project Summary/Abstract
Screening mammography has been shown effective in early detection of breast cancer and in reducing
mortality. However, controversies and challenges still remain, with primary concerns on personal breast cancer
risk prediction from mammographic parenchymal markers, high recall and benign biopsy rates, and improving
radiologists’ clinical reading practices. Computerized methods have been developed in these regards, with the
goal of providing computer assistance to radiologists in making clinical decisions. While successful, the
accuracy of these methods is subject to appropriate data representation (i.e., image features) that requires
strong feature engineering. A newly emerged artificial intelligence technique, called deep learning, represents
a breakthrough in machine learning paradigms, and has revolutionized computer image analysis and many
other applications in the past few years. Breast cancer screening yields a huge amount of mammogram data
that requires in-depth interpretation to improve current clinical workup. The goal of this study is to develop and
optimize a convolutional neural network (CNN)-based computational approach to improve mammographic
imaging trait identification, analysis, and interpretation and to use this approach to address accurate breast
cancer risk prediction and reduce false recall rates. This study will be the first to examine the effects of the
revolutionary deep learning technique on performing in-depth interpretation of big screening mammogram data,
aimed at improving clinical practice. The new risk biomarkers will contribute to providing more accurate risk
prediction than currently available. The recall-decision model will help reduce false recalls (associated with
potential benign biopsy results), and better understand radiologists’ reading behaviors. Overall, the CNN-based
approach will optimize the clinical utility of screening mammography and has a high likelihood to translate to
the clinic for breast cancer screening.

## Key facts

- **NIH application ID:** 10165659
- **Project number:** 5R01CA218405-04
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Shandong Wu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $357,994
- **Award type:** 5
- **Project period:** 2018-06-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10165659

## Citation

> US National Institutes of Health, RePORTER application 10165659, Deep interpretation of mammographic images in breast cancer screening (5R01CA218405-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10165659. Licensed CC0.

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