Zhou Siqiong, Pfeiffer Nicholaus, Islam Upala J, Banerjee Imon, Patel Bhavika K, Iquebal Ashif S
School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA.
Department of Radiology, Mayo Clinic, Phoenix, AZ 85054, USA.
IEEE Int Conf Automation Sci Eng (CASE). 2022 Aug;2022:955-960. doi: 10.1109/case49997.2022.9926519. Epub 2022 Oct 28.
Imaging phenotypes extracted via radiomics of magnetic resonance imaging has shown great potential at predicting the treatment response in breast cancer patients after administering neoadjuvant systemic therapy (NST). Existing machine learning models are, however, limited in providing an expert-level interpretation of these models, particularly interpretability towards generating causal inference. Causal relationships between imaging phenotypes, clinical information, molecular features, and the treatment response may be useful in guiding the treatment strategies, management plans, and gaining acceptance in medical communities. In this work, we leverage the concept of counterfactual explanations to extract causal relationships between various imaging phenotypes, clinical information, molecular features, and the treatment response after NST. We implement the methodology on a publicly available breast cancer dataset and demonstrate the causal relationships generated from counterfactual explanations. We also compare and contrast our results with traditional explanations, such as LIME and Shapley.
通过磁共振成像的放射组学提取的影像表型在预测新辅助全身治疗（NST）后乳腺癌患者的治疗反应方面显示出巨大潜力。然而，现有的机器学习模型在对这些模型进行专家级解释方面存在局限性，尤其是在生成因果推断的可解释性方面。影像表型、临床信息、分子特征与治疗反应之间的因果关系可能有助于指导治疗策略、管理计划，并在医学领域获得认可。在这项工作中，我们利用反事实解释的概念来提取NST后各种影像表型、临床信息、分子特征与治疗反应之间的因果关系。我们在一个公开可用的乳腺癌数据集上实施该方法，并展示从反事实解释中生成的因果关系。我们还将我们的结果与传统解释（如LIME和Shapley）进行比较和对比。
