Optimal Decision Making in Radiotherapy Using Panomics Analytics

现代放射治疗 (RT) 的复杂环境包含来自患者的丰富组合的数据 具体信息包括：人口统计、高能剂量的身体特征、后续特征 重复应用图像引导（放射组学）和生物标记（基因组学、蛋白质组学等）， 在可能持续几天到几周的治疗期之前和/或期间产生。这些的快速增长 可用且未开发的“泛组学”数据为大数据分析带来了充足的机会来实现 RT 中个性化医疗的前景。对于有前景但高风险的 RT 手术尤其如此，例如 立体定向体放疗（SBRT），由于早期的临床成功而得到了巨大的发展 疾病阶段和缩短高剂量治疗的社会经济效益。这导致了人们的愿望 利用这些治疗方法治疗更晚期的癌症，然而，与这些治疗相关的未知风险 毒性增加阻碍了其潜力。因此，强大的临床决策支持系统（CDSS）能够 探索复杂的泛组学相互作用景观，目标是利用已知的原理 迫切需要分次放疗之前和期间的治疗反应。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， 目前正在限制为基于实时响应的适应做出个性化临床决策的能力 根据现有数据，放射治疗中。为了满足这一需求并克服当前的挑战，我们组建了 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 在 RT 之前和期间获得可靠的预测结果； (2)深度强化学习动态优化 治疗适应； (3) 用于 RT 决策支持的以用户为中心的软件原型，具有更广泛的目标 为 RT 中的结果建模和基于响应的适应构建全面的实时框架。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 摆脱当前受主观经验和启发法限制的基于人群的方法的潜力 将规则纳入稳健的、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 部位进行测试，以证明其多功能性并突出相关性 人机因素和癌症具体问题。 影响陈述：患者特定的大数据现在可以在 RT 课程之前和/或期间使用，提供新的 以及尚未开发的个性化治疗机会。本研究将克服目前的不足 通过调查和开发基于人群的方法和当前 RT 实践中未充分利用的数据 智能的、计算机辅助的、以用户为中心的、个性化的CDSS，并在有回报但高回报的方面测试其性能 风险 RT 场景。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.