Abstract：Due to its long treatment cycle, neoadjuvant chemotherapy has important clinical reference value for early and accurate prediction of the final curative effect of chemotherapy. Due to the existence of factors such as tumor heterogeneity and image partial volume effect, the traditional radiomics method makes it difficult to further improve the prediction accuracy. Features were used to predict the efficacy of neoadjuvant chemotherapy. In the experiment, images of 191 breast cancer patients collected were preprocessed to obtain images of regions of interest in tumors and glands, and radiomics features were extracted, and the longitudinal time feature change rate was calculated. The random forest model was used to predict the curative effect and combined with the AUC index to evaluate and analyze the classification performance of the model. The results showed that the best AUC of 0.791 was achieved in the task of predicting raw images before decomposition. In the image depth decomposition experiment, the distribution of longitudinal pattern changes in tumor images was more significantly different among treatment groups (P<0.01), and the best AUC of 0.888 was achieved in the prediction task of image features of different dynamic patterns. In summary, by combining the multi-regional images and longitudinal time features, compared with the images before decomposition, the images of different modes after deep decomposition improved the curative effect prediction ability based on the feature level, which was expected to provide important reference for early diagnosis of patients and program adjustment in accordance with.
苏天放, 范明, 厉力华. 基于纵向时间影像动态增强模式分析的乳腺癌新辅助化疗疗效预测[J]. 中国生物医学工程学报, 2023, 42(2): 139-147.
Su Tianfang, Fan Ming, Li Lihua. Prediction of Breast Cancer Neoadjuvant Chemotherapy Based on Dynamic Enhanced Mode Analysisof Longitudinal Time Images. Chinese Journal of Biomedical Engineering, 2023, 42(2): 139-147.
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