Xie Jiang, Wei Jinzhu, Shi Huachan, Lin Zhe, Lu Jinsong, Zhang Xueqing, Wan Caifeng
School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China.
School of Medicine, Shanghai University, Shanghai, 200444, China.
BMC Med Imaging. 2025 Jan 23;25(1):26. doi: 10.1186/s12880-024-01543-7.
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 .
新辅助化疗（NAC）是一种针对乳腺癌患者术前的全身系统性化疗方案。然而，NAC并非对所有人都有效，且该过程十分痛苦。因此，准确早期预测NAC的疗效对于患者的临床诊断和治疗至关重要。在本研究中，提出了一种具有双峰逐层特征融合模块（BLFFM）和时间混合注意力模块（THAM）的新型卷积神经网络模型，该模型使用多阶段双峰超声图像作为输入，用于早期预测局部晚期乳腺癌（LABC）患者新辅助化疗的疗效。BLFFM能够有效地挖掘灰度超声（GUS）和彩色多普勒血流成像（CDFI）之间高度复杂的相关性和互补特征信息。THAM能够关注NAC一个周期前后病变进展的关键特征。对从合作医疗机构收集的101例患者的GUS和CDFI视频进行预处理，以获得3000组多阶段双峰超声图像组合用于实验。实验结果表明，所提出的模型是有效的，并且优于比较模型。代码将发布在https://github.com/jinzhuwei/BLTA-CNN 上。
