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

本研究设计了一种头颈部肿瘤适应性放射治疗的新方法。将统计和计算方法应用于从患者队列收集的大数据，以针对与特定队列具有相似特征的新患者进行精确治疗。 该奖项支持四个互补领域之间的合作研究项目的启动：临床，图像分析和可视化，高维数据管理和统计学习。 本项目中开发的外科衍生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，这些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空间数据（如医学图像）和非空间数据（如人口统计学和毒性）来扩展最新技术水平。该方法还考虑了患者对副作用的偏好。 开发的方法可用于获得最佳治疗策略，不仅适用于各种癌症诊断，还适用于其他慢性疾病，这些疾病需要做出多项决定，必须权衡疗效和毒性之间的权衡，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.