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Purpose Accurately predicting the clinical breast cancer subtypes could be extremely helpful for radiologists, pathologists, surgeons, and clinicians and inform future treatment prediction algorithms. Therefore, we evaluate and compare the accuracy of radiomic features extracted from contrast enhanced mammography (CEM) and magnetic resonance imaging (MRI) scans to make predictions to subtypes of breast cancer. Methods This HIPAA-compliant prospective single institution study was approved by the local institutional review board with written informed consent. Women with breast tumors 2 cm or larger underwent dynamic contrast-enhanced MRI and/or CEM for surgical staging. Semi-manual regions of interest were drawn by radiologist using Cancer Imaging Phenomics Toolkit (CaPTk). Radiomic features were obtained using PyRadiomics and MR- and CEM- based classification models were built on a low-dimensional representation of the features obtained via kernel principal component analysis. We subscribed to an ensemble tree-based learning approach called extremely randomized trees (ERT) to predict cancer subtypes captured via immunohistochemistry markers. Results For MRI analysis, 124 women with newly diagnosed breast cancer were included in the analysis comprising 49 HR+HER2-, 37 HR+HER2+, 11 HR-HER2+, and 27 HR-HER2- cases. For CEM analysis, models were built using data from 170 female patients including 74 HR+HER2-, 41 HR+HER2+, 14 HR-HER2+, and 43 HR-HER2-. CEM based model resulted in accuracies of 55%, 72%, 88%, and 71% respectively for HR+HER2-, HR+HER2+, HR-HER2+, and HR-HER2- whereas MRI based model alone led to accuracies of 54%, 62%, 89%, and 76% respectively for HR+HER2-, HR+HER2+, HR-HER2+, and HR-HER2-. Conclusions Radiomic features extracted from CEM and MR were strong predictors of breast cancer subtypes with CEM-based radiomic features performing slightly better, though not statistically significantly better (p = 0.82), than its MRI counterpart.

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.