Current tab, overview of application and additional information about development.
Here you can access the user guide and templates.
Here you can load your own local cohort for analysis in concordance with the methods described in the publication.
Here you can try out the app and explore the environment before you assemble your local dataset.
The sample subjects used are not based on real subject data and should not be used to assess MPM performance or recommend thresholds for real-world cases.
This application is included as supplemental material for the article - J. Uthoff, N. Koehn, J. Larson, S.K.N. Dilger, E. Hammond, A. Schwartz, B. Mullan, R. Sanchez, R.M. Hoffman, J.C. Sieren, ''Post-imaging Mathematical Prediction Models: Are They Clinically Relevant?'' published in European Radiology. PMID: XXXXXX
The authors would like to acknowledge Mark Escher and Patrick Thalken for their assistance in application deployment.
Following publication, the source code for this application will be available on GitHub (http://XXXX) under Creative Commons Attribution-ShareAlike (CC BY-SA) License. This license lets you remix, tweak, and build upon this work provided you cite the origin code and license new creations under identical terms. This license is similar to copyleft free and open source software licenses.
This application was developed using the R programming language version 3.5.1 (2018-07-02) -- 'Feather Spray'((C) 2018 The R Project for Statistical Computing).
R is free software environment for statistical computing and graphics. It complies and runs on a wide variety of UNIX platforms, Windows, and MacOS. It is available under the GNU General Public License. The authors of the associated publication and developers of this application would like to thank the R Development Core Team and all CRAN contributors for their work to make statistical and graphical libraries and tools available to all.
R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
R libraries: 'shiny', 'pROC','PRROC', and 'dplyr'.
Winston Chang, Joe Cheng, JJ Allaire, Yihui Xie and Jonathan McPherson (2018). shiny: Web Application Framework for R. R package version 1.1.0. https://CRAN.R-project.org/package=shiny
Xavier Robin, Natacha Turck, Alexandre Hainard, Natalia Tiberti, Frederique Lisacek, Jean-Charles Sanchez and Markus Muller (2011). pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 12, p. 77. DOI: 10.1186/1471-2105-12-77 <http://www.biomedcentral.com/1471-2105/12/77/>
Jan Grau, Ivo Grosse, and Jens Keilwagen (2015). PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R. Bioinformatics (31) 15, pp. 2595-2597.R package version 1.3.1.
Hadley Wickham, Romain Francois, Lionel Henry and Kirill Muller (2018). dplyr: A Grammar of Data Manipulation. R package version 0.7.6. https://CRAN.R-project.org/package=dplyr
The user guide indicates (1) how variables should be coded and (2) how columns should be named. It also provides an overview of which variables/columns are used in each of the four MPMs. If you wish to only receive results from a single MPM, please feel free to use the templates provided on the 'Local Single MPM File Templates' tab above.Save user guide as ''MPM_UserGuide.pdf'' to your computer's Downloads folder
The template file is a comma separated file or '.csv' which can open in Excel or a text editor. It includes the appropriate column names and a single default subject. To use the template, simply add local subjects as a new row per the directions in the user guide.Save template as ''MPM_localData_template.csv'' to your computer's Downloads folder
Similar to Figure 1 of Uthoff et al. (PMID: XXXXX)- histograms of MPM predictions split based on true nodule classification. Solid lines indicate MPM-associated thresholds. Note vertical axis autoscales to optimal for visualizatoin of MPM.
File will save as ''MPM_youdenSummary_localDataset.csv'' to your computer's Downloads folderYouden Summary Statistics
File will save as ''MPM_customSummary_localDataset.csv'' to your computer's Downloads folderCustom Summary Statistics
File will save as ''MPM_predictions_localDataset.csv'' to your computer's Downloads folderAll four MPM risk predictions
Similar to Figure 1 of Uthoff et al. (PMID: XXXX) - histograms of MPM predictions split based on true nodule classification. Solid black lines indicate MPM-associated thresholds. The gray dashed line is the Youden threshold. Note vertical axis autoscales to optimal for visualizatoin of MPM.