PregGAN: A prognosis prediction model for breast cancer based on conditional generative adversarial networks. (September 2022)

• Record Type: Journal Article
• Authors: Zhang, Fan; Zhang, Yingqi; Zhu, Xiaoke; Chen, Xiaopan; Du, Haishun; Zhang, Xinhong
• Is Part Of: Computer methods and programs in biomedicine. Volume 224(2022)
• Publication Date: 2022-09
• Subjects: Prognosis prediction -- Breast cancer -- Generative adversarial network -- PregGAN; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107026
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Developing the capability of GAN for the prognosis prediction. Adding the clinical data as conditions to the training process. Using Wasserstein distance and gradient penalty to make the training process more stable. Abstract: Background and Objective: Generative adversarial network (GAN) is able to learn from a set of training data and generate new data with the same characteristics as the training data. Based on the characteristics of GAN, this paper developed its capability as a tool of disease prognosis prediction, and proposed a prognostic model PregGAN based on conditional generative adversarial network (CGAN). Methods: The idea of PregGAN is to generate the prognosis prediction results based on the clinical data of patients. PregGAN added the clinical data as conditions to the training process. Conditions were used as the input to the generator along with noises. The generator synthesized new samples using the noises vectors and the conditions. In order to solve the mode collapse problem during PregGAN training, Wasserstein distance and gradient penalty strategy were used to make the training process more stable. Results: In the prognosis prediction experiments using the METABRIC breast cancer dataset, PregGAN achieved good results, with the average accurate (ACC) of 90.6% and the average AUC (area under curve) of 0.946. Conclusions: Experimental results show that PregGAN is a reliable prognosis predictive model for breast cancer. Due to the strong ability of probability distribution learning, PregGAN can also be used for the prognosis prediction of other diseases.