基于纵向时间深度网络融合的乳腺癌新辅助化疗疗效预测

doi:10.3969/j.issn.0258-8021.2022.02.007

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Abstract Neoadjuvant chemotherapy can improve the cure rate of breast cancer, but it is not effective for all patients. Accurate prediction of chemotherapy efficacy can provide reference for physicians to formulate treatment protocols. This study used deep learning to integrate the image characteristics of longitudinal time dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict the efficacy of neoadjuvant chemotherapy. We analyzed 164 DCE-MRI images of patients who underwent neoadjuvant chemotherapy for breast cancer, and selected the maximum tumor diameter and two upper and lower slices from each patient's image data set to expand the data to 442 cases that were randomly divided into 312 cases in the training set and 130 cases in the test set. DCE-MRI images had 6 sequences in total. Segmented the breast area of each sequence and removed the skin and chest cavity. Using deep learning model,the efficacy of neoadjuvant chemotherapy was predicted based on the images before chemotherapy, after 2 courses of chemotherapy and both of them, respectively. We drew the ROC curve of the prediction results and calculated the area under the curve (AUC) to evaluate the classification performance of the model. The best AUC of deep learning model for predicting the efficacy of the images before chemotherapy and the images after two courses of chemotherapy was 0.775 and 0.808 respectively, and the best AUC for predicting the efficacy of the fusion of images before chemotherapy and images after 2 courses of chemotherapy was 0.863, which was better than using the images before chemotherapy. The experimental results showed that compared with the existing approach of using the images before chemotherapy, using the fusion of longitudinal time images could improve the prediction performance of neoadjuvant chemotherapy.

Key words： breast cancer deep learning efficacy of neoadjuvant chemotherapy dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) longitudinal analysis

Received: 15 September 2021

PACS:

R318

Corresponding Authors: ^*E-mail: lilh@hdu.edu.cn

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	Articles by authors
	Xue Tailong
	Fan Ming
	Chen Shujun
	Li Lihua

Cite this article:

Xue Tailong,Fan Ming,Chen Shujun, et al. Prediction of Breast Cancer Neoadjuvant Chemotherapy Based on Longitudinal Time DepthNetwork Fusion[J]. Chinese Journal of Biomedical Engineering, 2022, 41(2): 186-194.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2022.02.007 OR http://cjbme.csbme.org/EN/Y2022/V41/I2/186

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