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

Abstract

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop a deep learning algorithm that uses temporal information to improve the performance of a previously published framework of cancer lesion detection for digital breast tomosynthesis (DBT). Materials and Methods This retrospective study analyzed the current and the 1-year prior Hologic DBT screening examinations from 8 different institutions between 2016 to 2020. The dataset contained 973 cancer and 7123 noncancer cases. The front-end of this algorithm was an existing deep learning framework that performed singleview lesion detection followed by ipsilateral view matching. For this study, PriorNet was implemented as a cascaded deep learning module that used the additional growth information to refine the final probability of malignancy. Data from seven of the eight sites were used for training and validation, while the eighth site was reserved for external testing. Model performance was evaluated using localization receiver operating characteristic (ROC) curves. Results On the validation set, PriorNet showed an area under the ROC curve (AUC) of 0.931 (95% CI 0.930- 0.931), which outperformed both baseline models using single-view detection (AUC, 0.892 (95% CI 0.891-0.892), P < .001) and ipsilateral matching (AUC, 0.915 (95% CI 0.914-0.915), P < .001). On the external test set, PriorNet achieved an AUC of 0.896 (95% CI 0.885-0.896), outperforming both baselines (AUCs, 0.846 (95% CI 0.846-0.847, P < .001) and 0.865 (95% CI 0.865-0.866) P < .001, respectively). In the high sensitivity range of 0.9 to 1.0, the partial AUC of PriorNet was significantly higher (P < .001) relative to both baselines. Conclusion PriorNet using temporal information further improved the breast cancer detection performance of an existing DBT cancer detection framework. ©RSNA, 2024.