Information-Theoretic Surprise-Driven Approach to Enhance Decision Making in Healthcare

项目总结 仅用于临床决策的传统指南往往不足以准确地对患者进行分层。 用于诊断测试。例如，国家综合癌症网络(NCCN)指南 对乳腺癌进行胚系基因检测的患者分层未能识别出近50%的 可能有BRCA突变。随着成像技术和黑盒机器学习的进步 算法，放射组学已经成为一种在广泛的健康状况下进行预测的有前途的工具 例如乳腺癌和卵巢癌、阿尔茨海默氏症和冠心病。基于这些研究， 这项研究的基本假设是，从放射组学获得的成像表型一起 使用传统指南可以筛查患者的潜在疾病(在本研究中，生殖系BRCA 突变)具有比单独使用传统指南更高的阳性预测价值。这里，我们指的是 NCCN指南作为传统指南。 然而，人们对这些有害健康状况和疾病之间的因果关系知之甚少。 定量成像表型。再加上缺乏量化和报告成像的标准 在多个机构的表型中，目前将它们整合到临床决策中是不可行的。 为此，这项研究将侧重于以下两个具体目标，以应对这些挑战和 随后对假设进行了验证。具体目标1：通过幅度同步来协调磁共振成像 降低扫描仪到扫描仪的可变性。具体目标2：因果推论和信息论 发现BRCA突变和成像表型之间的因果关系，并随后整合 让他们参与到临床决策中。 虽然拟议的研究侧重于基于磁学的生殖系BRCA检测的患者分层 磁共振成像表型、方法和算法可推广到其他健康状况和 成像方式。这项研究的结果将导致临床决策的新范式 医生将能够将成像表型与潜在的健康状况联系起来--类似于如何 综合代谢小组中的异常水平可作为潜在健康问题的指标--并做好准备 进行适当的干预。

PROJECT SUMMARY Traditional guidelines used in clinical decision-making alone are often insufficient in accurately stratifying patients for diagnostic testing. For instance, the National Comprehensive Cancer Network (NCCN) guidelines for stratifying patients for germline genetic testing of breast cancer fails to identify nearly 50% of the patients who might have a BRCA mutation. With advancements in imaging techniques and black-box machine learning algorithms, radiomics has emerged as a promising tool for making predictions in a wide range of health conditions such as breast and ovarian cancer, Alzheimer’s disease, and coronary heart disease. Based on these studies, the underlying hypothesis of this research is that imaging phenotypes obtained from radiomics together with traditional guidelines can screen patients for underlying diseases (in this research, germline BRCA mutation) with a higher positive predictive value than the traditional guidelines alone. Here, we refer to the NCCN guidelines as the traditional guidelines. However, little is known about the causal relationship between these deleterious health conditions and quantitative imaging phenotypes. Together with the lack of standards for quantifying and reporting imaging phenotypes across multiple institutions, it is currently not feasible to integrate them into clinical decision-making. To this end, this research will focus on the following two specific aims to address these challenges and subsequently validate the hypothesis. Specific Aim 1: MRI harmonization via amplitude synchronization to mitigate the scanner-to-scanner variability. Specific Aim 2: Causal inference and information theory to discover the causal relationships between BRCA mutation and imaging phenotypes and subsequently integrate them into clinical decision making. While the proposed research focuses on stratifying patients for germline BRCA testing based on magnetic resonance imaging phenotypes, the methodology and algorithms generalize to other health conditions and imaging modalities. The outcome of this research will lead to a new paradigm of clinical decision making where medical practitioners would be able to link imaging phenotypes with underlying health conditions—akin to how abnormal levels on comprehensive metabolic panels act as indicators of potential health problems—and prepare for appropriate interventions.