Abstract PO1-07-03: An Accurate Breast Cancer Diagnosis Method Using a Multi-Feature Fusion Neural Network on Contrast-Enhanced Spectral Mammography

Abstract Objectives: Our study aims to develop an accurate and comprehensive deep neural network capable of classifying CESM images to aid in the early detection and diagnosis of breast cancer in clinical settings. Methods: We enrolled diagnostic CESM examinations conducted between January 1, 2019 and January 17, 2021. We developed and tested the performance of a multi-feature fusion network for breast lesion classification by combining low-energy (LE) and dual-energy subtracted (DES) images, dual-view, and bilateral information using a large and diverse CESM dataset. We have evaluated the ability of the proposed network to generalize on external datasets and the results were reported using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Results: In the study period 1973CESMs were included. Mean age was 53 years ± 12 (standard deviation). In the internal test dataset, the model with CESM (LE +DES) inputs combined not only bilateral information (left and right breasts) but also dualview information (CC and MLO), achieved best diagnosis performance with AUC of 0.96 [95% confidence interval (CI): 0.95–0.97], accuracy of 0.9 [95% confidence interval (CI): 0.88–0.92], sensitivity of 0.84 [95% confidence interval (CI): 0.80–0.88] and specificity of 0.93 [95% confidence interval (CI): 0.91–0.95], while the model with LE inputs only got an AUC of 0.94 [95% (CI): 0.93–0.95], accuracy of 0.88 [95% confidence interval (CI): 0.86–0.90], sensitivity of 0.79 [95% confidence interval (CI): 0.74–0.83] and specificity of 0.93 [95% confidence interval (CI): 0.91–0.95] (external dataset: an AUC of 0.90 [95% (CI): 0.85–0.94], accuracy of 0.84 [95% confidence interval (CI): 0.78–0.89], sensitivity of 0.77 [95% confidence interval (CI): 0.61–0.88] and specificity of 0.87 [95% confidence interval (CI):0.80–0.92]). Conclusion: CESM is a promising technique in breast cancer diagnosis due to its high feasibility and potential. The results demonstrate that left-right breast fusion and dual-view fusion are helpful for accurate diagnosis on CESM images. Specifically, dual-energy subtracted (DES) images generated by CESM improve diagnostic accuracy when compared to low-energy (LE) images alone. Keywords: Contrast-enhanced spectral mammography; Deep neural network; Breast cancer diagnosis; Multi-feature fusion Citation Format: Yipeng Song, Wei Jiang. An Accurate Breast Cancer Diagnosis Method Using a Multi-Feature Fusion Neural Network on Contrast-Enhanced Spectral Mammography [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-07-03.