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.

项目概要 仅用于临床决策的传统指南通常不足以对患者进行准确分层 例如，国家综合癌症网络 (NCCN) 指南 对患者进行乳腺癌种系基因检测的分层未能识别出近 50% 的患者 随着成像技术和黑盒机器学习的进步，可能存在 BRCA 突变。 算法，放射组学已成为一种有前景的工具，可用于对各种健康状况进行预测 例如乳腺癌和卵巢癌、阿尔茨海默病和冠心病。 这项研究的基本假设是，从放射组学获得的成像表型一起 遵循传统指南可以筛查患者的基础疾病（在本研究中，种系 BRCA 突变）比传统的单独检测具有更高的阳性预测值。 NCCN 指南与传统指南一样。 然而，人们对这些有害健康状况与疾病之间的因果关系知之甚少。 定量成像表型以及缺乏量化和报告成像的标准。 跨多个机构的表型，目前不可能将它们整合到临床决策中。 为此，本研究将重点关注以下两个具体目标来应对这些挑战和 随后验证假设：具体目标 1：通过幅度同步实现 MRI 协调。 减少扫描仪之间的差异。具体目标 2：因果推理和信息论。 发现 BRCA 突变与影像表型之间的因果关系并随后整合 将他们纳入临床决策。 虽然拟议的研究重点是基于磁力对患者进行生殖系 BRCA 检测分层 共振成像表型、方法和算法可推广到其他健康状况和 这项研究的结果将带来临床决策的新范式。 医生将能够将影像表型与潜在的健康状况联系起来——类似于如何 综合代谢组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。