BodyMapR: an R package and Shiny application designed to generate anatomical visualizations of cancer lesions

Miller, David M.; Shalhout, Sophia Z

doi:10.1093/jamiaopen/ooac013

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…StoryboardR is an R package with a Shiny application front-end that produces an interactive data visualization of patient-level data. When built around a core data capture system such as a cancer registry, R-based packages like GENETEX, 17 eLAB, 18 BodyMapR, 19 and StoryboardR can combine to form a powerful data informatics ecosystem to both augment data abstraction as well as facilitate data analysis with the goal of accelerating time-to-action for patients with rare tumors.…”

Section: Discussionmentioning

confidence: 99%

StoryboardR: an R package and Shiny application designed to visualize real-world data from clinical patient registries

Miller

Shalhout

2023

JAMIA Open

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Objectives Tumor registries are a rich source of real-world data which can be used to test important hypotheses that inform clinical care. Exploratory data analysis at the level of individual subjects, when enhanced by interactive data visualizations, has the potential to provide novel insights and generate new hypothesis. Materials and Methods We created StoryboardR: an R package and Shiny application designed to visualize real-word data from tumor registries. Results StoryboardR facilitates the data visualization of real-word data from tumor registries captured in REDCap®. The output is an interactive timeline that allows for a visual interpretation of the relationship between potential prognostic and/or predictive biomarkers and outcomes. Conclusions StoryboardR is freely available under the Massachusetts Institute of Technology license and can be obtained from GitHub. StoryboardR is executed in R and deployed as a Shiny application for non-R users. It produces data visualizations of patient journeys from tumor registries.

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Section: Discussionmentioning

confidence: 99%

StoryboardR: an R package and Shiny application designed to visualize real-world data from clinical patient registries

Miller

Shalhout

2023

JAMIA Open

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“…As the literature shows, these kinds of applications are increasing for all themes as well as cancer. Miller and Shalhout [30] designed and implemented an application to generate anatomical visualizations of cancer lesions. They concluded that data visualizations of the characteristics of clinical tumors could help to understand the natural history of malignancies.…”

Section: Principal Findingsmentioning

confidence: 99%

Exploring Cancer Incidence, Risk Factors, and Mortality in the Lleida Region: Interactive, Open-source R Shiny Application for Cancer Data Analysis

Florensa¹,

Mateo²,

Lopez³

et al. 2023

JMIR Cancer

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Background 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. Objective 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. Methods 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. Results 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. Conclusions 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.

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Exploring Cancer Incidence, Risk Factors, and Mortality in the Lleida Region: Interactive, Open-source R Shiny Application for Cancer Data Analysis (Preprint)

Florensa¹,

Mateo²,

Lopez³

et al. 2022

Preprint

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BACKGROUND The cancer incidence rate is essential in public health surveillance. The analysis of this information permits the authorities to know the cancer situation in their regions. OBJECTIVE This study aimed to present the design and implementation of a shiny app to assist cancer registers in conducting rapid descriptive and predictive analytics in a user-friendly, intuitive, portable and scalable way. Moreover, we want to describe the design and implementation roadmap to inspire other population registers to exploit their datasets and develop similar tools and models. METHODS The first step is consolidating the data into the population register cancer database. This data is cross-validated by ASEDAT software, checked later, and reviewed by experts. Next, we developed an online tool to visualize data and generate reports to assist decision-making under the R Shiny framework. Currently, the app can generate descriptive analytics using population variables such as age, sex and cancer type; cancer incidence in region-level geographical heatmaps; line plots to visualize temporal trends and typical risk factor plots. RESULTS The results provide a successful case study where the tool was applied to the cancer register of the Lleida region. The study illustrates how researchers and cancer registers can use the app to analyse cancer databases. Furthermore, the results highlight the analytics related to risk factors and second tumours. CONCLUSIONS This paper aims to show a successful methodology to exploit data in the population cancers register and propose guidelines for other similar records to develop similar tools. We intend to inspire other entities to build an app that can help decision-making and also make data more accessible and transparent for the community of users.

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BodyMapR: an R package and Shiny application designed to generate anatomical visualizations of cancer lesions

Cited by 3 publications

References 10 publications

StoryboardR: an R package and Shiny application designed to visualize real-world data from clinical patient registries

StoryboardR: an R package and Shiny application designed to visualize real-world data from clinical patient registries

Exploring Cancer Incidence, Risk Factors, and Mortality in the Lleida Region: Interactive, Open-source R Shiny Application for Cancer Data Analysis

Exploring Cancer Incidence, Risk Factors, and Mortality in the Lleida Region: Interactive, Open-source R Shiny Application for Cancer Data Analysis (Preprint)

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