QuBBD: Collaborative Research: SMART -- Spatial-Nonspatial Multidimensional Adaptive Radiotherapy Treatment

本课题为头颈部肿瘤的自适应放射治疗设计了一种新的方法。统计和计算方法应用于从患者队列中收集的大数据，为具有与特定队列相似特征的新患者量身定制精确治疗。该奖项支持启动四个互补领域之间的合作研究项目：临床、图像分析和可视化、高维数据管理和统计学习。在这个项目中开发的经验推导的治疗规则有可能提高护理标准。该项目定义了新的强化学习技术，该技术考虑了多维结果和患者偏好。该项目通过在制定治疗规则时考虑到空间数据（如医学图像）和非空间数据（如人口统计和毒性），扩展了目前的技术水平。该方法进一步考虑了患者对副作用的偏好。所开发的方法不仅可以用于各种癌症诊断，还可以用于其他需要在疗效和毒性之间权衡权衡的多重决策的慢性疾病，包括精神健康障碍、药物滥用疾病和糖尿病。该奖项由美国国立卫生研究院大数据到知识（BD2K）计划与美国国家科学基金会数学科学部合作支持。

This project designs a novel approach for adaptive radiotherapy treatment of head and neck cancer. Statistical and computational methods are applied to Big Data, collected from cohorts of patients, to tailor precise treatment for a new patient who has similar characteristics to a specific cohort. This award supports initiation of a collaborative research project between four complementary domains: clinical, image analysis and visualization, high-dimensional data management, and statistical learning. The empirically-derived treatment rules developed in this project have the potential to improve the standard of care. The project defines novel reinforcement learning techniques which account for multidimensional outcomes and patient preferences. The project extends the state of the art by taking into account both spatial data (such as medical images) and nonspatial data (such as demographics and toxicity) in the development of the treatment rules. The approach further takes into account the patient's preference with regard to side effects. The methods developed may be used to derive optimal treatment strategies across not only a variety of cancer diagnoses, but other chronic conditions that require making multiple decisions that must weigh the tradeoffs between efficacy and toxicity, including mental health disorders, substance abuse diseases, and diabetes. This award is supported by the National Institutes of Health Big Data to Knowledge (BD2K) Initiative in partnership with the National Science Foundation Division of Mathematical Sciences.