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

癌症取决于疾病的空间位置，影响到所有种族和年龄段， 造成重大死亡率和与治疗相关的副作用。在一个例子中，超过50,000 美国每年都有新的头颈部鳞癌病例被诊断出来。 州，导致患者数据的大型、丰富的存储库。对于每一种情况，肿瘤学家 需要预测与治疗相关的生存期、肿瘤学和毒性结果 策略，以便选择一种平衡疗效和毒性的治疗方法。然而，尽管 现有的丰富数据，在临床上对癌症治疗的决策支持是初步的 只包含了少数患者的特征，这主要是由于缺乏计算能力 方法和工具。 我们建议构建一种新的统计和计算方法，用于纵向和 随着时间的推移，个性化的治疗决定，特别适用于头颈部癌症 治疗计划。同时合并复杂因素-例如辐射剂量 放射敏感器官的位置或患者报告的影响质量的副作用 生命-癌症治疗过程中的治疗决策需要发展 新的方法。这种方法是革命性的，因为它是该领域中第一个包括 成像和非成像数据，同时考虑大规模的生物和临床 相互关联。该方法通过利用大数据存储库和通过 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整合不同的数据类型并建立竞争模型 结果。 从临床角度来看，这种综合治疗方法在癌症治疗领域是新颖的。这个 由此产生的临床决策支持方法学将标志着生物医学的重大进步 计算，因为它将第一次能够识别可操作的治疗时间点 以及毒性修改，根据患者的特征和生活质量指标。这个 在该项目中开发的经验式治疗决策支持方法具有 有潜力直接提高存活者的护理标准和生活质量 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.