Longitudinal Spatial-Nonspatial Decision Support for Competing Outcomes in Head and Neck Cancer Therapy

头颈癌治疗竞争结果的纵向空间-非空间决策支持

• 批准号: 10381044
• 负责人: GUADALUPE CANAHUATE
• 金额: $ 7.16万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10381044
• 关键词: Accounting; Address; Aftercare; Age; American Joint Committee on Cancer; Anatomy; Award; Biomedical Computing; Cancer Center; Caring; Characteristics; Chronic; Clinical; Clinical Management; Clinical Research; Computing Methodologies; Country; Data; Data Analyses; Data Science; Data Set; Data Sources; Decision Support Model; Decision Support Systems; Dependence; Detection; Development; Development Plans; Diabetes Mellitus; Disease; Dose; Epidemic; Equilibrium; Evolution; Extramural Activities; Funding; Head and Neck Cancer; Human Papillomavirus; Hybrids; Incidence; Individual; Learning; Left; Location; Long-Term Effects; Malignant Neoplasms; Malignant neoplasm of brain; Malignant neoplasm of lung; Mental disorders; Mentors; Methodology; Methods; Modeling; Modification; Morbidity - disease rate; Nausea; Operative Surgical Procedures; Organ; Outcome; Parents; Patient Outcomes Assessments; Patients; Population; Probability; Process; Psychological reinforcement; Public Health; Quality of life; Radiation Dose Unit; Radiation therapy; Radiometry; Reporting; Research Personnel; Residual state; Resolution; Risk; Science; Selection for Treatments; Severities; Signal Transduction; Smoking; Staging; Substance abuse problem; Survivors; Symptoms; Time; Tobacco; Toxic effect; Training; Treatment Efficacy; Treatment outcome; Update; Validation; Xerostomia; anticancer research; base; bioimaging; cancer diagnosis; cancer therapy; career development; chemotherapy; clinical care; cohort; design; head and neck cancer patient; heterogenous data; high dimensionality; improved; in silico; individual patient; insight; interpatient variability; mortality; novel; nutrition; optimal treatments; parent project; patient stratification; personalized medicine; predictive modeling; prevent; programs; prospective; prototype; repository; response; risk prediction; risk prediction model; serial imaging; standard of care; survival outcome; symptom cluster; treatment planning; treatment strategy; tumor

Project Summary/Abstract Head and neck cancer (HNC) patients survive years after oncologic therapy due to increased efficacy of therapy, increased incidences of human papilloma virus related HNC, and decreased numbers of smoking and tobacco related tumors. However, the majority of patients are plagued with long lasting or permanent residual effects, whose severity, rate of development and resolution after treatment vary largely between survivors. At the same time, patient reported outcomes (PROs) offer important information that could be critical for the efficient detection and resolution of long term effects. However, the interpretation of PRO repositories is plagued by data and analysis issues which so far have prevented their practical use in clinical care, including missing or incomplete data, co-occurence of multiple symptoms, variability across populations and across time, and, in the case of HNC and other spatially- dependent cancers, further symptom dependency on the anatomical location of the tumors and their proximity to organs at risk. We propose to develop validated, patient-specific models to interpret HNC PROs in order to inform individual treatment and care decisions for patients. Our data science approach circumvents limitations in the state of the art by accounting longitudinally for PRO symptom clusters and their dynamics over time, while handling incomplete data, by incorporating patient- specific bioimaging markers and spatial dose data pre- and during therapy, by calibrating for inter-patient variability, and by predicting symptom development and computing clinical action signals for a new patient based on cohorts of similar patients. From a clinical perspective, our integrative data science approach is novel in the field of cancer therapy, through its leveraging of existing patient repositories and similar cohorts, its symptom- cluster analytics, and its integration of heterogeneous data sources, including patient reported outcomes and quantitative bioimaging data. The resulting methodology will mark a significant advance in biomedical computing because it will be able to identify early specific patients who are at risk for long lasting or permanent treatment-induced residual effects, and will thus enable clinicians to adapt care to the individual patient level. The proposed supplement application extends the methodological approach of the parent award by incorporating radiation dosimetry data, undertaken within a career development plan designed to enhance and accelerate the capacity of the applicant, who is from a background underrepresented in biomedical sciences, transition to mentored and independent investigator status, thus enhancing cancer research workforce diversity under this program.

项目概要/摘要 头颈癌 (HNC) 患者在肿瘤治疗后仍能存活数年，因为 治疗效果，人乳头瘤病毒相关 HNC 发病率增加，以及 HNC 数量减少 吸烟和烟草相关的肿瘤。然而，大多数患者都受到长期或长期的困扰 永久性残留效应，其严重程度、发展速度和治疗后的消退各不相同 主要是在幸存者之间。同时，患者报告结果 (PRO) 提供了重要的信息 对于有效检测和解决长期影响至关重要的信息。然而， PRO 存储库的解释受到数据和分析问题的困扰，迄今为止，这些问题一直无法实现 它们在临床护理中的实际用途，包括数据缺失或不完整、多种疾病同时出现 症状、不同人群和不同时间的变异性，以及就 HNC 和其他空间而言 依赖性癌症，进一步症状依赖于肿瘤的解剖位置及其接近程度 对处于危险中的器官。 我们建议开发经过验证的、针对患者的模型来解释 HNC PRO，以便 为患者提供个体化治疗和护理决策的信息。我们的数据科学方法 通过纵向考虑 PRO 症状群来规避现有技术的限制 及其随时间的动态变化，同时处理不完整的数据，通过结合患者特定的生物成像 通过校准患者间的变异性，以及治疗前和治疗期间的标记物和空间剂量数据 根据队列预测新患者的症状发展并计算临床行动信号 类似的患者。 从临床角度来看，我们的综合数据科学方法在癌症领域是新颖的 治疗，通过利用现有的患者存储库和类似的队列，其症状群 分析及其异构数据源的集成，包括患者报告的结果和 定量生物成像数据。由此产生的方法将标志着生物医学的重大进步 计算，因为它将能够识别出有长期或长期患病风险的早期特定患者 永久性治疗引起的残留效应，从而使临床医生能够根据个人情况调整护理 患者级别。 拟议的补充申请通过以下方式扩展了家长奖励的方法方法： 纳入辐射剂量测定数据，在职业发展计划中进行，旨在增强和 提高来自生物医学科学领域代表性不足的申请人的能力， 过渡到指导和独立研究者身份，从而增强癌症研究人员的多样性 在此计划下。