SCGAN: Sparse CounterGAN for Counterfactual Explanations in Breast Cancer Prediction

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 the treatment response and Imaging phenotypes, Clinical information, and Molecular (ICM) features 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 by changing multiple features simultaneously. This paper proposes a new method called Sparse CounteRGAN (SCGAN) for generating counterfactual instances to reveal 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. We propose dropout training of the discriminator to promote sparsity and introduce a diversity term in the loss function to maximize the distances among generated counterfactuals. 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. Results show that SCGAN generates sparse and diverse counterfactual instances that also achieve plausibility and feasibility, making it a valuable tool for understanding the causal relationships between ICM features and treatment response. <italic>Note to Practitioners</italic>&#x2014; Determining the suitability of NST for a breast cancer patient before surgery is complex and depends on factors such as patient demographics, tumor characteristics, clinical history, and molecular subtypes. Understanding the causal relationships between different features and pathologic responses to NST may lead to opportunities for targeted therapies and help oncologists make informed decisions about continuing or limiting systemic therapy after the initial consultation. The lack of causal explanations in traditional machine learning models for predicting NST has limited their applicability in clinical decision-making. SCGAN proposed in this paper overcomes the limitations of existing methods in generating causal inference via counterfactual explanations. This approach helps identify causal relationships between imaging phenotypes, clinical history, molecular features, and pathologic response to NST. The resulting information can aid in developing personalized treatment plans for patients and ultimately improve patient outcomes. The proposed approach may be extended to broader applications beyond healthcare, such as manufacturing, where it could improve quality control processes and enhance production efficiency.