Deep learning model for breast cancer diagnosis based on bilateral asymmetrical detection (BilAD) in digital breast tomosynthesis images

• Published in: Radiological physics and technology 2023-03, Vol.16 (1), p.20-27
• Main Authors: Shimokawa, Daiki, Takahashi, Kengo, Kurosawa, Daiya, Takaya, Eichi, Oba, Ken, Yagishita, Kazuyo, Fukuda, Toshinori, Tsunoda, Hiroko, Ueda, Takuya
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Asymmetry; Breast - diagnostic imaging; Breast cancer; Breast Neoplasms - diagnostic imaging; Deep Learning; Diagnostic systems; Digital imaging; Embedding; Female; Humans; Image classification; Imaging; Machine learning; Mammography - methods; Medical and Radiation Physics; Medical imaging; Medicine; Medicine & Public Health; Nuclear Medicine; Radiology; Radiotherapy; Research Article; Retrospective Studies; Tensors
• ISSN: 1865-0333; 1865-0341
• DOI: 10.1007/s12194-022-00686-y

The purpose of this study was to develop a deep learning model to diagnose breast cancer by embedding a diagnostic algorithm that examines the asymmetry of bilateral breast tissue. This retrospective study was approved by the institutional review board. A total of 115 patients who underwent breast surgery and had pathologically confirmed breast cancer were enrolled in this study. Two image pairs [230 pairs of bilateral breast digital breast tomosynthesis (DBT) images with 115 malignant tumors and contralateral tissue (M/N), and 115 bilateral normal areas (N/N)] were generated from each patient enrolled in this study. The proposed deep learning model is called bilateral asymmetrical detection (BilAD), which is a modified convolutional neural network (CNN) model of Xception with two-dimensional tensors for bilateral breast images. BilAD was trained to classify the differences between pairs of M/N and N/N datasets. The results of the BilAD model were compared to those of the unilateral control CNN model (uCNN). The results of BilAD and the uCNN were as follows: accuracy, 0.84 and 0.75; sensitivity, 0.73 and 0.58; and specificity, 0.93 and 0.92, respectively. The mean area under the receiver operating characteristic curve of BilAD was significantly higher than that of the uCNN ( p  = 0.02): 0.90 and 0.84, respectively. The proposed deep learning model trained by embedding a diagnostic algorithm to examine the asymmetry of bilateral breast tissue improves the diagnostic accuracy for breast cancer.