Predict the Therapeutic Effect of Bevacizumab Treatment Using a Channel Attention Convolution Neural Network

Hongtao Xu

doi:10.54097/hset.v14i.1695

Authors

Hongtao Xu

DOI:

https://doi.org/10.54097/hset.v14i.1695

Keywords:

Ovarian, efficient channel attention, histopathological image, bevacizumab, stain normalization.

Abstract

One of the malignancies with the highest mortality rates among women worldwide is ovarian cancer. Epithelial ovarian cancer (EOC) is the most common kind of ovarian cancer which takes ~90% of ovarian cancer patients. peritoneal serous surface papillary carcinoma (PSPC) is rare cancer whose incident rate is 7% in women. Bevacizumab has been used as a monotherapy along with chemotherapy to treat advanced EOC and PSPC. Bevacizumab has a significant effect on chemotherapy, however, due to the high cost and side effects of the bevacizumab, how to predict the therapeutic effect of Bevacizumab treatment is very important. In this paper, the author uses the proposed attention module, ECA, embedding to the ResNet, compose as ECA-Net, to predict the treatment effect of the bevacizumab in current tissue is effective or invalid through the histopathological image. As a result, the ECA-Net gained novel performance, scoring highly on several evaluation metrics. Specifically, the classification accuracy of the ECA-Net is 94.54% and the f1 score is 95.00%. Bevacizumab is pricey and has side effects, the classification model will forecast its therapeutic impact. In this situation, the experiment will assist the gynecologist in selecting the best course of therapy while also saving money.

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References

Wang, C. W., Chang, C. C., Khalil, M. A., Lin, Y. J., Liou, Y. A., Hsu, P. C., Lee, Y. C., Wang, C.-H., Chao, and T.K., “Histopathological whole slide image dataset for classification of treatment effectiveness to ovarian cancer,” Scientific Data 9(1) (2022).

Integrated genomic analyses of ovarian carcinoma, “Integrated genomic analyses of ovarian carcinoma.,” Nature 474(7353), 609–615 (2011).

Fu, J., Zheng, H., and Mei, T., “Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017).

Lee, K., Lockhart, J. H., Xie, M., Chaudhary, R., Slebos, R. J., Flores, E. R., Chung, C. H., and Tan, A. C., “Deep learning of histopathology images at the single cell level,” Frontiers in Artificial Intelligence 4 (2021).

Gowda, S. N., and Yuan, C., “Colornet: Investigating the importance of color spaces for image classification,” Computer Vision – ACCV 2018, 581–596 (2019).

Anghel, A., Stanisavljevic, M., Andani, S., Papandreou, N., Rüschoff, J. H., Wild, P., Gabrani, M., and Pozidis, H., “A high-performance system for robust stain normalization of whole-slide images in histopathology,” Frontiers in Medicine 6 (2019).

Wang, X., Girshick, R., Gupta, A., and He, K., "Non-local neural networks." Proceedings of the IEEE conference on computer vision and pattern recognition. (2018).

Yan, R., Ren, F., Wang, Z., Wang, L., Zhang, T., Liu, Y., and Zhang, F., “Breast cancer histopathological image classification using deep convolutional neural network”, Methods, (2020).

Mishra, A., “Metrics to evaluate your machine learning algorithm,” Medium, 28 May 2020, <https://towardsdatascience.com/metrics-to-evaluate-your-machine-learning-algorithm-f10ba6e38234> (5 July 2022).

He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., and Li, M., “Bag of tricks for image classification with Convolutional Neural Networks,” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019).