SCGAN: Sparse CounterGAN for Counterfactual Explanations in Breast Cancer Prediction

Abstract

Imaging phenotypes extracted via radiomics of magnetic resonance imaging have shown great potential in predicting the treatment response in breast cancer patients after administering neoadjuvant systemic therapy (NST). Understanding the causal relationships between Imaging phenotypes, Clinical information, and Molecular (ICM) features, and the treatment response are critical in guiding treatment strategies and management plans. Counterfactual explanations provide an interpretable approach to generating causal inference; however, existing approaches are either computationally prohibitive for high dimensional problems, generate unrealistic counterfactuals, or confound the effects of causal features. This paper proposes a new method called Sparse CounteRGAN (SCGAN) for generating counterfactual instances to establish causal relationships between ICM features and the treatment response after NST. The generative approach learns the distribution of the original instances and, therefore, ensures that the new instances are realistic. Further, we propose a loss function that regularizes the counterfactuals to minimize the distance between original instances and counterfactuals (to promote sparsity) and the distances among generated counterfactuals to promote diversity. We evaluate the proposed method on two publicly available datasets, followed by the breast cancer dataset, and compare their performance with existing methods in the literature. Finally, we demonstrate the causal relationships from generated counterfactual instances. Results show that SCGAN generates plausible and realistic counterfactual instances with small changes in only a few features, making it a valuable tool for understanding the causal relationships between ICM features and treatment response.