Optimal Decision Making in Radiotherapy Using Panomics Analytics

现代放射治疗(RT)的复杂环境包括来自大量患者的数据- 具体信息包括：人口统计、高能剂量的物理特征、后续特征 由于图像引导(放射组学)和生物标记(基因组学、蛋白质组学等)的重复应用， 在可跨越数天至数周的治疗期之前和/或之后产生。这些业务的快速增长 可用的和未开发的泛OMICS数据，为大数据分析带来了大量机会 在RT中承诺个体化用药。在前景看好但风险很高的RT程序中尤其如此，例如 立体定向体部RT(SBRT)，由于早期的临床成功，它已经经历了巨大的扩张 疾病分期和缩短大剂量治疗的社会经济效益。这导致了人们希望 然而，将这些治疗方法应用到更晚期的癌症中，与 增加的毒性阻碍了它的潜力。因此，强大的临床决策支持系统(CDSS)能够 探索复杂的泛Omics交互格局，目标是利用已知的 分割放疗前和放疗过程中的治疗反应是迫切需要的。的长期目标是 该项目旨在克服与预测不确定性和人机交互相关的障碍，这些障碍 目前限制了为基于实时响应的适应而做出个性化临床决策的能力 在放射治疗方面，从现有的数据。为了满足这一需求和克服当前的挑战，我们组建了一个 多学科团队包括：临床医生、医学物理学家、数据科学家和人为因素专家。 具体地说，我们将开发和量化评估：(1)基于图的监督机器学习算法 用于RT前和RT期间的稳健预测结果；(2)深度强化学习以动态优化 治疗适应；以及(3)以用户为中心的RT决策支持软件原型，目标更广泛 为RT中的结果建模和基于响应的适应建立一个全面的实时框架。我们 假设使用先进的机器学习技术和以用户为中心的工具将解锁 摆脱受主观经验和启发式限制的当前基于人群的方法的潜力 规则转换成健壮的、针对患者的、用户友好的CDSS。这种方法及其相应的软件工具将 在肺癌和肝癌的两个临床RT站点进行测试，以展示其多功能性并突出相关性 人机因素和癌症特有的问题。 影响陈述：特定于患者的大数据现在可以在RT疗程之前和/或期间使用，提供新的 以及尚未开发的个性化治疗机会。这项研究将克服目前 基于人口的方法和数据在当前RT实践中的使用不足，通过调查和开发 智能的、计算机辅助的、以用户为中心的、个性化的CDSS，并测试其在回报高的 风险RT情景。该方法也适用于其他现代癌症方案。

The complex environment of modern radiation therapy (RT) comprises data from a rich combination of patient- specific information including: demographics, physical characteristics of high-energy dose, features subsequent to repeated application of image-guidance (radiomics), and biological markers (genomics, proteomics, etc.), generated before and/or over a treatment period that can span few days to several weeks. Rapid growth of these available and untapped “pan-Omics” data, invites ample opportunities for Big data analytics to deliver on the promise of personalized medicine in RT. This particularly true in promising but high-risk RT procedures such as stereotactic body RT (SBRT), which have witnessed tremendous expansion due to clinical successes in early disease stages and socio-economic benefits of shortened high dose treatments. This has led to the desire to exploit these treatments into more advanced stages of cancer, however, the unknown risks associated with increased toxicities hamper its potential. Therefore, robust clinical decision support systems (CDSSs) capable of exploring the complex pan-Omics interaction landscape with the goal of exploiting known principles of treatment response before and during the course of fractionated RT are urgently needed. The long-term goal of this project is to overcome barriers related to prediction uncertainties and human-computer interactions, which are currently limiting the ability to make personalized clinical decisions for real-time response-based adaptation in radiotherapy from available data. To meet this need and overcome current challenges, we have assembled a multidisciplinary team including: clinicians, medical physicists, data scientists, and human factor experts. Specifically, we will develop and quantitively evaluate: (1) graph-based supervised machine learning algorithms for robust prediction outcomes before and during RT; (2) deep reinforcement learning to dynamically optimize treatment adaptation; and (3) a user-centered software prototype for RT decision support, with the broader goal of building a comprehensive real-time framework for outcome modeling and response-based adaption in RT. We hypothesize that the use of advanced machine learning techniques and user-centered tools will unlock the potentials to move from current population-based approaches limited by subjective experiences and heuristic rules into robust, patient-specific, user-friendly CDSSs. This approach and its corresponding software tool will be tested within two clinical RT sites of lung and liver cancers, to demonstrate its versatility and highlight pertinent human-computer factors and cancer specific issues. Impact statement: Patient-specific big data are now available before and/or during RT courses, offering new and untapped opportunities for personalized treatment. This study will overcome current shortcomings of population-based approaches and data underuse in current RT practice by investigating and developing an intelligent, computer-aided, user-centered, personalized CDSS and test its performance in rewarding but high- risk RT scenarios. The approach is also applicable to other modern cancer regimens.