An uncertainty-based interpretable deep learning framework for breast cancer outcomes prediction

Abstract

Accurate prediction of breast cancer outcomes is important for selecting appropriate treatment, which can prolong the survival period of the patients and improve the life quality. Recently, different deep learning-based methods are carefully designed for cancer outcomes prediction. However, the applications of these methods are still challenging due to the model interpretability. In this study, we proposed a novel multi-task deep neural network UISNet to interpret the feature importance of the prediction model by an uncertainty-based integrated gradients algorithm. Additionally, UISNet improves the prediction accuracy by introducing the prior biological pathway knowledge and utilizing the patients heterogeneity information. By applications to seven breast cancer public datasets, the method was shown to outperform state-of-the-art methods by achieving a 5.79% higher C-index value on average. For the identified genes based on the interpretable model, 11 out of the top 20 genes have been proved to be associated with breast cancer by literature review. The comprehensive tests indicated that our proposed method is accurate and robust to predict breast cancer outcomes, and is an effective way to identify the prognosis-related genes. The method codes are available at: https://github.com/chh171/UISNet.