BodyMapR: an R package and Shiny application designed to generate anatomical visualizations of cancer lesions. Issue 1 (4th March 2022)

• Record Type: Journal Article
• Authors: Miller, David M; Shalhout, Sophia Z
• Is Part Of: JAMIA open. Volume 5:Issue 1(2022)
• Publication Date: 2022-03-04
• Subjects: data visualization -- cancer -- Shiny app -- REDCap, Merkel cell carcinoma; Medical informatics -- Periodicals; 610.285
• DOI: 10.1093/jamiaopen/ooac013
• Languages: English
• ISSNs: 2574-2531

Abstract

Abstract: Objectives: Structured real-world data (RWD), such as those found in cancer registries, provide a rich source of information regarding the natural history of cancer. Interactive data visualizations of cancer lesions can provide insights into certain clinical tumor characteristics (CTC). Software that can be integrated into an oncological data collection effort and generate anatomical data visualizations of CTC are limited. Materials and Methods: We created BodyMapR: an R package and Shiny application that generates anatomical visualizations of cancer lesions from structured data. Results: BodyMapR is a Shiny application that transposes structured data from REDCap ® onto an anatomical map to yield an interactive data visualization. Conclusions: BodyMapR is freely available under the MIT license and can be obtained from GitHub. BodyMapR is executed in R and deployed as a Shiny application. It can be integrated into an existing cancer research platform and produces an interactive data visualization of CTC. Lay Summary: Large-scale data collection efforts in rare cancers are challenging and uncommon. Consequently, we lack a comprehensive understanding of clinical tumor characteristics (CTC), such as patterns of metastatic spread and biomarkers predictive of treatment response, for most rare tumors. Data collection efforts that incorporate data captured during real-world practice (a.k.a real-world data) can improve our understanding of CTC. Depicting RWD, for example, from a cancer registry, onto graphical representations of anatomical structures can provide a user-friendly technique to process information regarding CTC. However, displaying large amounts of RWD onto anatomical data visualizations is labor-intensive and time consuming. Currently, there is a dearth of software that can facilitate this process. Here, we present BodyMapR, a novel software that generates an interactive visualization of CTC from RWD. The package is freely available and modifiable by end users.