Optimal Decision Making in Radiotherapy Using Panomics Analytics

现代放射治疗（RT）的复杂环境包括来自患者的丰富组合的数据， 具体信息包括：人口统计学、高能剂量的物理特征、随后的特征 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， 在治疗期之前和/或期间产生，所述治疗期可以跨越几天到几周。这些快速增长 可用和未开发的“泛组学”数据，为大数据分析提供了充足的机会， RT中个性化医疗的承诺。这在有前途但高风险的RT手术中尤其如此，例如 立体定向体RT（SBRT），由于早期临床成功， 缩短高剂量治疗的疾病阶段和社会经济效益。这导致了人们的愿望， 将这些治疗方法用于更晚期的癌症，然而， 增加的毒性阻碍了其潜力。因此，强大的临床决策支持系统（CDSS）能够 探索复杂的泛组学互动景观的目标是利用已知的原则， 在分次RT之前和期间的治疗反应是迫切需要的。的长期目标 该项目旨在克服与预测不确定性和人机交互相关的障碍， 目前限制了为基于实时响应的适应做出个性化临床决策的能力 从现有的数据来看。为了满足这一需求并克服当前的挑战，我们组织了一个 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。 具体来说，我们将开发并定量评估：（1）基于图的监督机器学习算法 用于RT之前和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;（3）以用户为中心的RT决策支持软件原型，目标更广泛 建立一个全面的实时框架，用于RT中的结果建模和基于响应的自适应。 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 从目前基于人口的方法转变的潜力受到主观经验和启发的限制 将规则转化为强大的、患者特定的、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部位进行测试，以证明其多功能性并突出相关性 人机因素和癌症特定问题。 影响声明：患者特定的大数据现在可以在RT课程之前和/或期间使用， 和个性化治疗的未开发机会。这项研究将克服目前的缺点， 通过调查和开发一个基于人口的方法和数据在当前RT实践中未得到充分利用， 智能的，计算机辅助的，以用户为中心的，个性化的CDSS，并测试其在奖励，但高， 风险RT情景。该方法也适用于其他现代癌症治疗方案。

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.