Flexible NLP toolkit for automatic curation of outcomes for breast cancer patients

项目概要/摘要 乳腺癌是世界上新发病例最多的疾病（11.7%）。虽然预后的 由于早期发现和综合治疗，乳腺癌患者通常是有利的， 20%-30%的患者仍会发生远处转移，仅进展期病例 平均存活时间为两年。乳腺癌被广泛认为是一种异质性肿瘤。 在原发性肿瘤转移能力和转移扩散时间的意义上， 疾病需要高质量的基于人群的癌症监测数据：（1）描述 癌症负担、模式和结果，以便（2）为癌症预防、检测和 控制活动;（3）根据过去和未来的趋势评估干预措施， 可以采取最佳的方法来减轻癌症的负担和痛苦。但 费力的人工策展过程使得人口明智的监测数据收集 挑战性研究表明，注册费用总额的很大一部分用于 即使在低收入国家，在这个项目中，我们的使命是 一个灵活的NLP工具集，可以在机构级别本地执行，并将策划临床 和以患者为中心的乳腺癌患者的结果， 笔记放射学和病理学报告为了测试这些工具的通用性， 启动他们的数据收集部署，我们将与格鲁吉亚SEER和加州合作 国家癌症登记处，并将策划过去10年乳腺癌患者的结果数据 来自美国两个代表不同患者人群的机构-埃默里大学 医院（格鲁吉亚）和斯坦福大学医学中心（加州）。我们将利用以前的 开发工具和技术，并将其扩展到自动管理临床和患者- 集中的结果数据-复发日期和复发部位、给予的治疗、精神 和身体的结果-从临床笔记，并将这些转化为结构化和查询 格式. NLP工具将被对接，并在医院注册处本地运行，以实现自动化 结果策展。最后，NLP提取的结果将与国家癌症登记处共享 进行评估。从方法论的角度来看，框架和开放源码软件 开发的工具可以用于癌症研究，超出了我们的项目范围， 无论问题领域如何，结果都是一样的。

Project summary/Abstract Breast cancer has the largest number of new cases in world (11.7%). Although the prognosis of breast cancer patients is generally favorable due to early detection and comprehensive treatment, 20%–30% of patients will still develop distant metastases and cases with progressive stage only have a median two-year survival time. Breast cancer is widely recognized as a heterogeneous disease in the sense of both primary tumor metastatic capacity and time to metastatic spread of disease. High-quality population-based cancer surveillance data are needed to: (1) describe cancer burden, patterns, and outcomes in order to (2) inform cancer prevention, detection and control activities; and (3) evaluate interventions on the basis of past and future trends so that optimal approaches to alleviate burden and suffering from cancer can be adopted. However, the laborious manual curation process makes the population wise surveillance data collection challenging. It has been shown in studies that a large percentage of total registry cost is devoted to labor for data curation, even in the low-income countries. In this project, our mission is to build a flexible NLP toolset that can be executed locally at the institution level and will curate the clinical and patient-centered outcomes of breast cancer patients by parsing longitudinally acquired clinic notes, radiology and pathology reports. In order to test the generalizability of the tools and to initiate their deployment for data collection, we will partner with both Georgia SEER and California state cancer registry and will curate the outcome data of past 10-years breast cancer patients from two institutions across US representing diverse patient populations - Emory University hospital (Georgia) and Stanford Medical Center (California). We will leverage the previously developed tools and technologies and extend them to automatically curate the clinical and patient- centered outcome data – recurrence date and site of recurrence, treatment administered, mental and physical outcomes – from clinic notes and convert these into structured and query-able format. The NLP tools will be dockerized and run locally at the hospital registry level for automated outcome curation. Finally, the NLP extracted outcomes will be shared with State Cancer registry for evaluation. From a methodological perspective, the framework and the open-source software tools developed can be employed for cancer research beyond the scope of our project for curating outcomes regardless of the problem domain.