Neoadjuvant chemotherapy (NAC) is a systemic and systematic chemotherapy regimen for breast cancer patients before surgery. However, NAC is not effective for everyone, and the process is excruciating. Therefore, accurate early prediction of the efficacy of NAC is essential for the clinical diagnosis and treatment of patients. In this study, a novel convolutional neural network model with bimodal layer-wise feature fusion module (BLFFM) and temporal hybrid attention module (THAM) is proposed, which uses multistage bimodal ultrasound images as input for early prediction of the efficacy of neoadjuvant chemotherapy in locally advanced breast cancer (LABC) patients. The BLFFM can effectively mine the highly complex correlation and complementary feature information between gray-scale ultrasound (GUS) and color Doppler blood flow imaging (CDFI). The THAM is able to focus on key features of lesion progression before and after one cycle of NAC. The GUS and CDFI videos of 101 patients collected from cooperative medical institutions were preprocessed to obtain 3000 sets of multistage bimodal ultrasound image combinations for experiments. The experimental results show that the proposed model is effective and outperforms the compared models. The code will be published on the https://github.com/jinzhuwei/BLTA-CNN .
Keywords: Breast cancer; Deep learning; Multistage bimodal ultrasound images; Neoadjuvant chemotherapy.
© 2025. The Author(s).