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

项目摘要/摘要 头颈癌(HNC)患者在肿瘤治疗后存活数年，原因是 治疗效果，增加与人乳头瘤病毒相关的HNC的发生率，减少 吸烟和烟草相关的肿瘤。然而，大多数患者都受到长期的或长期的 永久性后遗症，其严重程度、发展速度和治疗后的消退情况各不相同 主要是在幸存者之间。与此同时，患者报告的结果(PRO)提供了重要的 可能对有效检测和解决长期影响至关重要的信息。然而， 对专业知识库的解释受到数据和分析问题的困扰，到目前为止，这些问题阻碍了 它们在临床护理中的实际应用，包括数据缺失或不完整、多个 症状，不同人群和不同时间的可变性，就HNC和其他空间- 依赖癌症，进一步的症状依赖于肿瘤的解剖位置及其邻近 处于危险中的器官。 我们建议开发经过验证的、特定于患者的模型来解释HNC PRO，以便 告知患者的个人治疗和护理决定。我们的数据科学方法 通过纵向核算前期症状集群，绕过了最新技术的限制 以及它们随时间的动态变化，同时通过结合患者特定的生物成像来处理不完整的数据 治疗前和治疗期间的标记物和空间剂量数据，通过校准患者间的变异性，并通过 基于以下队列预测新患者的症状发展和计算临床行动信号 相似的病人。 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资料库和类似的队列，其症状群 分析及其对异类数据源的集成，包括患者报告的结果和 定量生物成像数据。由此产生的方法学将标志着生物医学的重大进步 计算，因为它将能够识别早期特定的患者，这些患者有长期或 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 病人级别。 拟议的补编申请通过以下方式扩展了家长裁决的方法学方法 纳入辐射剂量数据，在职业发展计划内进行，旨在加强和 加快申请人的能力，因为他的背景在生物医学科学中的代表性不足， 过渡到有指导和独立研究人员的地位，从而加强癌症研究人员队伍的多样性 在这个项目下。

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.