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Improved Breast Cancer Detection with Artificial Intelligence in a Real-World Digital Breast Tomosynthesis Screening

Joshua A Nepute¹, Meridith Peratikos², Alicia Y Toledano²

¹Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indiana University Health - West Central Region, Avon, IN.

Clinical Breast Cancer

|June 5, 2025

Summary

Artificial intelligence (AI) significantly improved breast cancer screening performance in digital breast tomosynthesis (DBT). The AI detection system led to higher cancer detection rates and reduced unnecessary recalls, enhancing screening accuracy.

Keywords:

Breast cancer screening Breast imaging clinical operations Breast imaging quality and outcomes Mammography

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Area of Science:

Radiology and Medical Imaging
Artificial Intelligence in Healthcare
Oncology

Background:

Digital breast tomosynthesis (DBT) is a key tool in breast cancer screening.
Evaluating the impact of emerging technologies like AI on clinical workflow is crucial.
Previous studies have explored AI's potential, but real-world performance data is essential.

Purpose of the Study:

To compare the performance of radiologists in breast cancer screening using DBT before and after the integration of an AI detection system.
To assess the impact of AI on key performance metrics such as cancer detection rate (CDR) and abnormal interpretation rate (AIR).

Main Methods:

A retrospective study involving 4 radiologists across 3 sites.
Comparison of DBT screening mammogram interpretations from a pre-AI period (n=10,322) and a post-AI period (n=6,407).
Analysis of cancer detection rate (CDR), abnormal interpretation rate (AIR), and positive predictive values (PPV1, PPV3).

Main Results:

Cancer detection rate (CDR) increased from 3.7 to 6.1 per 1000 exams with AI (P = .008).
Abnormal interpretation rate (AIR) decreased from 8.2% to 6.5% with AI (P < .001).
Positive predictive values (PPV1 and PPV3) significantly increased with AI implementation.

Conclusions:

Implementation of an AI detection system in DBT screening enhances radiologist performance.
AI integration leads to improved cancer detection and a reduction in false positives.
The study demonstrates the real-world benefits of AI in improving breast cancer screening outcomes.