Stable Breast Cancer Prognosis

Predicting breast cancer prognosis helps improve the treatment and management of the disease. In recent decades, many prediction models have been developed for breast cancer prognosis. A common assumption made by these models is that the test and training data follow the same distribution. However, due to the heterogeneity of breast cancer and differences in the environments where the data are collected, the distribution of the test data may shift from that of the training data. Existing methods may not provide stable predictions when data distribution shifts occur. In this paper, we present a novel method for stable breast cancer prognosis in situations with data distribution shifts. Our model, known as Deep Global Balancing Cox regression (DGBCox), is based on the causal inference theory. In DGBCox, initial high-dimensional gene expression data is transferred to latent representations by a deep autoencoder neural network. After balancing the latent representations using a proposed causality-based approach, causal latent features are selected for breast cancer prognosis. We apply DGBCox to 12 test datasets from different breast cancer studies. The results show that DGBCox outperforms benchmark methods in terms of both prediction accuracy and stability.