Abstract
Breast cancer is a serious threat to women's physical and mental health. In recent years, its incidence has been on the rise and it has become the top female malignant tumor in China. At present, adjuvant chemotherapy for breast cancer has become the standard mode of breast cancer treatment, but the response results usually need to be completed after the implementation of adjuvant chemotherapy, and the optimization of the treatment plan and the implementation of breast-conserving therapy need to be based on accurate estimation of the pathological response. Therefore, to predict the efficacy of adjuvant chemotherapy for breast cancer patients is to find a predictive method that is conducive to individualized choice of chemotherapy regimens. This article introduces the research of DCE-MRI images based on deep transfer learning in breast cancer adjuvant curative effect prediction. Deep transfer learning algorithms are used to process images, and then, the features of breast cancer after adjuvant chemotherapy are collected through image feature collection. Predictions are made, and the research results show that the accuracy of the prediction reaches 70%.
Highlights
In recent years, technology has brought people the changes in living environment and lifestyle, and some health diseases
Convolutional Neural Networks can automatically learn features of different depths in images, so we explored the use of CNN combined with patients’ dynamic contrast-enhanced MRI (DCE-MRI) images to predict the efficacy of breast cancer adjuvant chemotherapy
If an accurate evaluation of the efficacy of neoadjuvant chemotherapy is made before surgery, it is helpful to timely discover intractable tumors that do not respond to treatment or only have a small response and adjust the chemotherapy regimen for breast cancer patients in real time to reduce the risk of failure
Summary
Technology has brought people the changes in living environment and lifestyle, and some health diseases. Journal of Healthcare Engineering dynamic contrast-enhanced MRI contains rich disease information It has become the mainstream in the prediction and evaluation of the curative effect of neoadjuvant chemotherapy for breast cancer. E correlation between the information contained in DCE-MRI images and the curative effect of NAC has become a research hotspot in the biomedical field [7, 8] It has mostly been done around semiquantitative analysis in the past and cannot indirectly accurately show the exchange and penetration process of the contrast agent in the tumor. Convolutional Neural Networks can automatically learn features of different depths in images, so we explored the use of CNN combined with patients’ DCE-MRI images to predict the efficacy of breast cancer adjuvant chemotherapy. E main content of this article is the study of DCE-MRI image based on deep transfer learning in breast cancer adjuvant curative effect prediction. His research was only one aspect of research, and there are limitations
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