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Artificial Intelligence for Image-Based Breast Cancer Risk Prediction Using Attention.

Stepan Romanov¹, Sacha Howell^2,3,4, Elaine Harkness¹

¹Division of Informatics, Imaging and Data Science, University of Manchester, Manchester M13 9PT, UK.

Tomography (Ann Arbor, Mich.)

|December 22, 2023

Summary

This summary is machine-generated.

This study introduces an attention-based deep learning model for breast cancer risk prediction using mammograms. The model accurately identifies women at high risk for developing breast cancer within months, improving early detection and personalized prevention strategies.

Keywords:

attention deep learning mammography multiple instance learning risk prediction

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Artificial Intelligence Approaches to Assessing Primary Cilia

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Published on: May 1, 2021

Area of Science:

Radiology and Medical Imaging
Artificial Intelligence in Healthcare
Oncology

Background:

Accurate breast cancer risk prediction is crucial for personalized prevention and early detection.
While genetic data and breast density improve risk models, mammogram image features are underutilized.
Deep learning can extract complex information from mammograms, but data scarcity and computational demands pose challenges.

Purpose of the Study:

To develop an attention-based Multiple Instance Learning (MIL) model for accurate, short-term breast cancer risk prediction using full-resolution mammograms.
To incorporate image-based features from mammograms into risk assessment models.
To address data scarcity by mixing current screen-detected cancers with prior mammograms during model development.

Main Methods:

An attention-based Multiple Instance Learning (MIL) model was developed to analyze mammograms.
The model was trained using a dataset that included prior mammograms from women who later developed screen-detected or interval cancers.
Performance was evaluated using the Area Under the Curve (AUC) metric.

Main Results:

The proposed MAI-risk model achieved an AUC of 0.747 [0.711, 0.783] for predicting short-term breast cancer risk.
This performance surpassed established models like IBIS (AUC 0.594 [0.557, 0.633]) and VAS (AUC 0.649 [0.614, 0.683]).
The model effectively identified women who developed cancer between 5 and 55 months after their screening mammogram.

Conclusions:

Deep learning models, specifically attention-based MIL, can accurately predict short-term breast cancer risk from mammograms.
Incorporating detailed image features from mammograms significantly improves risk prediction accuracy.
This approach holds promise for enhancing personalized breast cancer screening and prevention strategies.