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

信息论惊喜驱动方法增强医疗保健决策

• 批准号: 10575550
• 负责人: Ashif Iquebal
• 金额: $ 55.45万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10575550
• 关键词: Address; Adjuvant Therapy; Algorithms; Alzheimer' s Disease; Award; BRCA mutations; Belief; Breast Cancer Patient; Characteristics; Clinic; Clinical; Complex; Computer software; Coronary heart disease; Data; Decision Making; Diagnosis; Diagnostic tests; Disease; Disparity; Early Diagnosis; Family history of; Goals; Guidelines; Health; Healthcare; Image; Imaging Techniques; Information Theory; Institution; Intervention; Letters; Link; Low Income Population; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of ovary; Medical; Metabolic; Methodology; Morphology; Mutation; National Comprehensive Cancer Network; Newly Diagnosed; Operative Surgical Procedures; Outcomes Research; Pathogenicity; Patients; Phenotype; Populations at Risk; Predictive Value; Pythons; Quality of life; Reporting; Research; Running; Shapes; Site; Standardization; Testing; Validation; Variant; Woman; Work; accurate diagnosis; brca gene; care burden; clinical decision-making; falls; generative adversarial network; genetic makeup; genetic testing; imaging modality; imaging software; improved; individualized medicine; machine learning algorithm; malignant breast neoplasm; mutation carrier; mutational status; novel; open source; patient screening; patient stratification; predictive modeling; programs; quantitative imaging; radiomics; standard of care; tool; treatment strategy; tumor

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.

项目摘要 仅临床决策中使用的传统准则通常不足以准确地对患者进行分层 例如，国家综合癌症网络（NCCN）指南 对乳腺癌的种系基因测试进行分层的患者无法识别几乎50％的患者 可能有BRCA突变。随着成像技术和黑箱机器学习的进步 算法，放射素学已成为在广泛的健康状况下进行预测的承诺工具 例如乳腺癌和卵巢癌，阿尔茨海默氏病以及冠心病。基于这些研究， 这项研究的基本假设是，从放射线学获得的成像表型一起 借助传统准则，可以筛查患者的潜在疾病（在这项研究中，生殖线BRCA 突变）比单独的传统准则具有更高的积极预测价值。在这里，我们指的是 NCCN指南作为传统准则。 但是，对于这些已删除的健康状况与 定量成像表型。加上缺乏量化和报告成像的标准 在多个机构的表型中，目前不可行将它们整合到临床决策中。 为此，这项研究将侧重于以下两个具体目的，以应对这些挑战和 随后验证该假设。特定目的1：通过放大器同步进行MRI协调 减轻扫描仪到扫描仪的可变性。特定目的2：因果推论和信息理论 发现BRCA突变与成像表型之间的因果关系，然后整合 他们参与临床决策。 而拟议的研究重点是根据磁性进行种系BRCA测试的患者进行分层 共振成像表型，方法和算法推广到其他健康状况，并且 成像方式。这项研究的结果将导致新的临床决策范式 医生将能够将成像表型与潜在的健康状况联系起来 - 金如何 综合代谢面板的异常水平是潜在健康问题的指标，并准备 进行适当的干预措施。