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

QuBBD：合作研究：SMART——空间-非空间多维适应性放射治疗

• 批准号: 1557559
• 负责人: Georgeta-Elisab Marai
• 金额: $ 2.5万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1557559&HistoricalAwards=false
• 关键词: QuBBD; Collaborative; Research; SMART; Spatial

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.

本项目设计了一种新的头颈癌自适应放射治疗方法。统计和计算方法被应用于从患者队列中收集的大数据，以便为具有与特定队列相似特征的新患者量身定做精确的治疗。该奖项支持在临床、图像分析和可视化、高维数据管理和统计学习四个互补领域之间启动一个合作研究项目。在该项目中开发的基于经验的治疗规则具有提高护理标准的潜力。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。该项目在制定治疗规则时同时考虑了空间数据(如医学图像)和非空间数据(如人口统计学和毒性)，从而扩展了最新技术。该方法进一步考虑了患者对副作用的偏好。开发的方法不仅可以用于得出各种癌症诊断的最佳治疗策略，还可以用于其他需要做出多项决定的慢性疾病，这些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。该奖项由国家卫生研究院大数据向知识转化(BD2K)倡议与国家科学基金会数学科学部合作支持。