QuBBD: Precision E –Radiomics for Dynamic Big Head & Neck Cancer Data

QuBBD：Precision E – 动态大头放射组学

• 批准号: 9762879
• 负责人: GUADALUPE CANAHUATE
• 金额: $ 23.51万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9762879
• 关键词: Address; Big Data; Chronic; Collaborations; Complex; Country; Data; Diabetes Mellitus; Disease; Head and Neck Cancer; Health; Human; Hybrids; Image; Laboratories; Learning; Location; Medical Imaging; Mental disorders; Methodology; Methods; Modeling; Organ; Outcome; Patient Preferences; Patients; Probability; Psychological reinforcement; Quality of life; Radiation therapy; Science; Substance abuse problem; Techniques; Time; Toxic effect; Treatment outcome; cancer diagnosis; cancer radiation therapy; cancer therapy; chemotherapy; cohort; design; head and neck cancer patient; improved; novel; optimal treatments; personalized medicine; radiomics; repository; side effect; spatial relationship; standard of care; treatment planning; treatment response; treatment risk; treatment strategy; tumor

Cancer treatment courses which rely on imaging and spatially-dependent therapy involve making multiple treatment decisions (e.g., radiotherapy alone, radiotherapy plus chemotherapy, induction chemotherapy) over time. These decisions depend on complex factors, including the tumor location with respect to sensitive organs and its response to treatment, laboratory data, toxicity, anticipated side effects and survival probability. In this project, we design and develop novel statistical methodology for dynamic and personalized treatment decisions with specific application to head and neck cancer radiotherapy planning. The empirically-derived treatment rules developed in this project have the potential to improve the standard of care (i.e., treatment plans chosen by the tumor treatment board) and the quality of life of surviving patients. The methods developed in the proposal may be used to derive optimal treatment strategies across not only a variety of spatially-dependent cancer diagnoses, but also other chronic conditions including mental health disorders, substance abuse diseases, or diabetes, that require making multiple decisions that must weigh the tradeoffs between efficacy and toxicity. This Big Data collaboration effectively bridges the quantitative sciences with biomedicine, and provides quantitative techniques for leveraging the largest existing repository of head and neck cancer data in the country.

依赖于成像和空间依赖治疗的癌症治疗过程涉及到多次 随着时间的推移作出治疗决定(例如，单纯放疗、放疗加化疗、诱导化疗)。这些决定取决于复杂的因素，包括肿瘤相对于敏感器官的位置及其对治疗的反应、实验室数据、毒性、预期副作用和生存概率。在这个项目中，我们设计和开发了新的统计方法，用于动态和个性化的治疗决策，并具体应用于头颈癌放射治疗计划。 在该项目中开发的基于经验的治疗规则有可能提高护理标准(即肿瘤治疗委员会选择的治疗计划)和存活患者的生活质量。提案中开发的方法不仅可以用于得出各种空间依赖的癌症诊断的最佳治疗策略，还可以用于包括精神健康障碍、物质滥用疾病或糖尿病在内的其他慢性疾病，这些疾病需要做出必须权衡疗效和毒性之间权衡的多重决定。这一大数据协作有效地将量化科学与生物医学联系在一起，并提供了量化技术，以利用该国现有的最大头颈癌数据库。

项目成果

Highly conformal reirradiation in patients with prior oropharyngeal radiation: Clinical efficacy and toxicity outcomes.

• DOI: 10.1002/hed.26384
• 发表时间: 2020-11
• 期刊: Head & neck
• 作者: Bagley AF;Garden AS;Reddy JP;Moreno AC;Frank SJ;Rosenthal DI;Morrison WH;Gunn GB;Fuller CD;Shah SJ;Ferrarotto R;Sturgis EM;Gross ND;Phan J
• 通讯作者: Phan J