动态增强影像映射图的深度学习方法预测乳腺癌新辅助化疗疗效

doi:10.3969/j.issn.0258-8021.2023.06.008

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Abstract Neoadjuvant chemotherapy is helpful to improve the later survival rate of breast cancer patients, but the efficacy evaluation has a certain lag. Accurate evaluation of the efficacy of neoadjuvant chemotherapy can give medical doctors more effective clinical suggestions and implement more optimized treatment plans. In order to make better use of the spatial information of the image and the time series information of the enhanced image, a dynamic enhanced image mapping pattern map was proposed to predict the efficacy of neoadjuvant chemotherapy for breast cancer. The images of 208 patients with breast cancer before neoadjuvant chemotherapy were retrospectively collected. According to the Miller & Payne grading system, the data were labeled as response group and non-response group, and randomly divided into training set (126 cases) and test set (82 cases). After image preprocessing and segmentation of the region of interest, the maximum diameter of the tumor and its adjacent 7 slices were selected to construct the mapping mode map. The original slice image, multi-sequence images under different mapping modes and multi-sequence images under fusion two mapping modes were constructed by combining the enhanced time series. The deep learning network was used to predict the mapping pattern graph, the ROC curve of the prediction results was drawn, and the evaluation indicators such as AUC, sensitivity, specificity were calculated to evaluate the performance of the model type. Among them, the prediction model of multi-sequence images fused with two mapping modes achieved the best result, with an AUC of 0.832. Experimental results showed that compared with the original slice images, the method combining longitudinal time series images and spatial features between slices effectively improved the classification effect of neoadjuvant chemotherapy response prediction.

Key words： breast cancer efficacy of neoadjuvant chemotherapy deep learning mapping mode

Received: 21 September 2022

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R318

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

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	Liu Xin
	Fan Ming
	Li Lihua

Cite this article:

Liu Xin,Fan Ming,Li Lihua. Deep Learning Model Using DCE-MRI Mapping for Prediction of Response to NeoadjuvantChemotherapy in Breast Cancer[J]. Chinese Journal of Biomedical Engineering, 2023, 42(6): 710-719.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2023.06.008 OR http://cjbme.csbme.org/EN/Y2023/V42/I6/710

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