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

取决于疾病空间位置的癌症影响所有种族和年龄组， 导致了严重的死亡率和治疗相关的副作用。有一次，超过5万人 在美国，每年都有新的头颈部鳞状细胞癌病例被诊断出来， 从而形成了庞大而丰富的患者数据库。对于每一个病例，肿瘤学家 需要预测与治疗相关的生存率、肿瘤学和毒性结局 为了选择一种平衡疗效和毒性的治疗方法，但尽管 在癌症治疗的临床决策支持中，可获得的大量数据是基本的 并且只结合了少数患者特征，主要是由于缺乏计算能力， 方法和工具。 我们建议建立一个新的统计和计算方法的纵向和 随着时间的推移，个性化的治疗决策，特别适用于头颈癌 治疗计划同时纳入复杂因素-如辐射剂量 相对于放射敏感器官的位置或患者报告的影响质量的副作用 在癌症治疗的过程中，将生命-纳入治疗决策需要发展 新颖的方法。这种方法是革命性的，因为它是该领域第一个包括 成像和非成像数据，同时考虑到大规模的生物和临床 相互关联该方法是创新的，通过利用大数据存储库， 它独特地融合了生物工程和计算机的计算建模原理， 科学这些方法使我们能够合并不同的数据类型和模型竞争 结果。 从临床角度来看，这种综合方法在癌症治疗领域是新颖的。的 由此产生的临床决策支持方法将标志着生物医学领域的重大进步。 计算，因为它将能够识别，第一次，可操作的治疗时间点， 和毒性改变。的 本项目开发的药物衍生治疗决策支持方法具有以下特点： 潜在的直接改善护理标准和生存患者的生活质量， 一种严重的，通常是致命的，使人衰弱的疾病。

Cancers that depend on the spatial location of the disease affect all ethnicities and age groups, accounting for significant mortality and therapy-related side effects. In one instance, over 50,000 new cases of head and neck squamous carcinomas are diagnosed each year in the United States, leading to large, rich repositories of patient data. For each of these cases, oncologists need to anticipate survival, oncologic, and toxicity outcomes associated with treatment strategies in order to select a treatment which balances efficacy and toxicity. However, despite the wealth of data available, in the clinic decision support for cancer treatment is rudimentary and incorporates only a handful of patient characteristics, largely due to a lack of computational methodology and tools. We propose to construct a novel statistical and computational methodology for longitudinal and personalized treatment decisions over time, with specific application to head and neck cancer therapy planning. Simultaneous incorporation of complex factors---such as radiation dose location with respect to radiosensitive organs or patient reported side effects affecting quality of life---into treatment decisions over the course of cancer therapy requires the development of novel methodology. This methodology is revolutionary in that it is the first in the field to include both imaging and nonimaging data, while taking into account large-scale biological and clinical correlates. The approach is innovative through its leverage of big data repositories and through its unique blend of computational modeling principles from bioengineering and computer science. These methods allow us to incorporate diverse data types and model competing outcomes. From a clinical perspective, this integrative approach is novel in the field of cancer therapy. The resulting clinical decision support methodology will mark a significant advance in biomedical computing because it will be able to identify, for the first time, actionable timepoints for therapy and toxicity modification, based on a patient’s characteristics and quality of life indicators. The empirically-derived treatment decision support methodology developed in this project has the potential to directly improve the standard of care and the quality of life of surviving patients with a grave, often fatal and debilitating illness.