Florensa Didac, Mateo-Fornes Jordi, Lopez Sorribes Sergi, Torres Tuca Anna, Solsona Francesc, Godoy Pere
Department of Computer Engineering, University of Lleida, Lleida, Spain.
Population-based Cancer Registry, Santa Maria University Hospital, Lleida, Spain.
JMIR Cancer. 2023 Apr 20;9:e44695. doi: 10.2196/44695.
The cancer incidence rate is essential to public health surveillance. The analysis of this information allows authorities to know the cancer situation in their regions, especially to determine cancer patterns, monitor cancer trends, and help prioritize the allocation of health resource.
This study aimed to present the design and implementation of an R Shiny application to assist cancer registries conduct rapid descriptive and predictive analytics in a user-friendly, intuitive, portable, and scalable way. Moreover, we wanted to describe the design and implementation road map to inspire other population registries to exploit their data sets and develop similar tools and models.
The first step was to consolidate the data into the population registry cancer database. These data were cross validated by ASEDAT software, checked later, and reviewed by experts. Next, we developed an online tool to visualize the data and generate reports to assist decision-making under the R Shiny framework. Currently, the application can generate descriptive analytics using population variables, such as age, sex, and cancer type; cancer incidence in region-level geographical heat maps; line plots to visualize temporal trends; and typical risk factor plots. The application also showed descriptive plots about cancer mortality in the Lleida region. This web platform was built as a microservices cloud platform. The web back end consists of an application programming interface and a database, which NodeJS and MongoDB have implemented. All these parts were encapsulated and deployed by Docker and Docker Compose.
The results provide a successful case study in which the tool was applied to the cancer registry of the Lleida region. The study illustrates how researchers and cancer registries can use the application to analyze cancer databases. Furthermore, the results highlight the analytics related to risk factors, second tumors, and cancer mortality. The application shows the incidence and evolution of each cancer during a specific period for gender, age groups, and cancer location, among other functionalities. The risk factors view permitted us to detect that approximately 60% of cancer patients were diagnosed with excess weight at diagnosis. Regarding mortality, the application showed that lung cancer registered the highest number of deaths for both genders. Breast cancer was the lethal cancer in women. Finally, a customization guide was included as a result of this implementation to deploy the architecture presented.
This paper aimed to document a successful methodology for exploiting the data in population cancer registries and propose guidelines for other similar records to develop similar tools. We intend to inspire other entities to build an application that can help decision-making and make data more accessible and transparent for the community of users.
癌症发病率对公共卫生监测至关重要。对这些信息的分析使当局能够了解其所在地区的癌症情况，特别是确定癌症模式、监测癌症趋势，并有助于确定卫生资源分配的优先次序。
本研究旨在介绍一个R Shiny应用程序的设计与实现，以帮助癌症登记处以用户友好、直观、便携且可扩展的方式进行快速描述性和预测性分析。此外，我们希望描述设计与实施路线图，以激励其他人口登记处利用其数据集并开发类似的工具和模型。
第一步是将数据整合到人口登记癌症数据库中。这些数据由ASEDAT软件进行交叉验证，随后进行检查，并由专家进行审核。接下来，我们开发了一个在线工具，用于可视化数据并生成报告，以协助在R Shiny框架下进行决策。目前，该应用程序可以使用人口变量（如年龄、性别和癌症类型）生成描述性分析；区域级地理热图中的癌症发病率；用于可视化时间趋势的折线图；以及典型风险因素图。该应用程序还展示了莱里达地区癌症死亡率的描述性图表。这个网络平台被构建为一个微服务云平台。网络后端由一个应用程序编程接口和一个数据库组成，分别由NodeJS和MongoDB实现。所有这些部分都通过Docker和Docker Compose进行封装和部署。
结果提供了一个成功的案例研究，该工具应用于莱里达地区的癌症登记处。该研究说明了研究人员和癌症登记处如何使用该应用程序分析癌症数据库。此外，结果突出了与风险因素、二次肿瘤和癌症死亡率相关的分析。该应用程序展示了特定时期内每种癌症在性别、年龄组和癌症位置方面的发病率和演变情况等功能。风险因素视图使我们能够检测到大约60%的癌症患者在确诊时被诊断为超重。关于死亡率，该应用程序显示肺癌在男女中死亡人数最多。乳腺癌是女性中的致命癌症。最后，作为此次实施的结果，包含了一个定制指南，用于部署所呈现的架构。
本文旨在记录一种利用人口癌症登记处数据的成功方法，并为其他类似记录制定开发类似工具的指南。我们希望激励其他实体构建一个有助于决策的应用程序，并使数据对用户社区更易于获取和透明。
