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Improving Computer-aided Detection for Digital Breast Tomosynthesis by Incorporating Temporal Change.

Yinhao Ren¹, Zisheng Liang¹, Jun Ge¹

¹From the Departments of Biomedical Engineering (Y.R.), Bioinformatics (X.X.), Radiology (D.L.N., J.Y.L., L.J.G.), and Electrical and Computer Engineering and Biomedical Engineering (J.Y.L.), Duke University, 2424 Erwin Rd, Studio #302, Durham, NC 27705; and iCAD Inc, Nashua, NH (Y.R., Z.L., J. Ge, J. Go).

Radiology. Artificial Intelligence

|August 14, 2024

Summary

A new deep learning algorithm, PriorNet, enhances breast cancer detection in digital breast tomosynthesis by incorporating temporal information. This advancement significantly improves the accuracy of identifying cancerous lesions compared to existing methods.

Keywords:

Breast Cancer Computer-aided Detection Deep Learning Digital Breast Tomosynthesis

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Area of Science:

Radiology and Medical Imaging
Artificial Intelligence in Healthcare
Oncology

Background:

Digital breast tomosynthesis (DBT) is a key imaging modality for breast cancer screening.
Existing deep learning frameworks for DBT lesion detection can be further improved.
Temporal information, such as lesion changes over time, is valuable for accurate diagnosis.

Purpose of the Study:

To develop and evaluate PriorNet, a deep learning algorithm utilizing temporal information to enhance DBT cancer detection.
To refine an existing framework by incorporating growth information for improved malignancy probability assessment.

Main Methods:

Retrospective analysis of DBT screening examinations from multiple institutions (2016-2020).
Development of PriorNet as a cascaded deep learning module building upon a single-view detection and ipsilateral view matching framework.
Training and validation on data from seven sites, with external testing on an eighth site; performance evaluated using localization ROC curves.

Main Results:

PriorNet achieved a higher AUC (0.931) on the validation set compared to baseline models (0.892 and 0.915).
On the external test set, PriorNet demonstrated superior performance with an AUC of 0.896 versus baselines (0.846 and 0.865).
PriorNet showed significantly higher partial AUC in the high sensitivity range (0.9-1.0), indicating improved detection of true positives.

Conclusions:

PriorNet effectively leverages temporal information to significantly improve the performance of DBT cancer detection frameworks.
The algorithm demonstrates enhanced accuracy in distinguishing malignant lesions by considering changes over time.
This deep learning approach offers a promising advancement for computer-aided detection in breast cancer screening.