RJafroc (1.2.0)

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Analyzing Diagnostic Observer Performance Studies.

https://dpc10ster.github.io/RJafroc/
http://cran.r-project.org/web/packages/RJafroc

Tools for quantitative assessment of medical imaging systems, radiologists or computer aided detection ('CAD') algorithms. Implements methods described in the book: 'Chakraborty' (2017) . Data collection paradigms include receiver operating characteristic ('ROC') and a location specific extension, namely free-response 'ROC' ('FROC'). 'ROC' data consists of a single rating per image, where the rating is the perceived confidence level the image is of a diseased patient. 'FROC' data consists of a variable number (including zero) of mark-rating pairs per image, where a mark is the location of a clinically relevant suspicious region and the rating is the corresponding confidence level that it is a true lesion. The name 'RJafroc' is derived from it being an enhanced R version of original Windows 'JAFROC' . Implemented are a number of figures of merit quantifying performance, functions for visualizing operating characteristics and three ROC ratings data curve-fitting algorithms: the 'binormal' model ('BM'), the contaminated 'binormal' model ('CBM') and the 'radiological' search model ('RSM') 'Chakraborty' (2006) . Also implemented is maximum likelihood fitting of paired ROC data, utilizing the correlated 'CBM' model ('CORCBM') model. Unlike the 'BM', which predicts 'improper' ROC curves, 'CBM', 'CORCBM' and the 'RSM' predict proper ROC curves that do not cross the chance diagonal. 'RSM' fitting yields measures of search and lesion-classification performances, in addition to the usual case-classification performance measured by the area under the 'ROC' curve. Search performance is the ability to find lesions while avoiding finding non-lesions. Lesion-classification performance is the ability to discriminate between found lesions and non-lesions. A number of significance testing algorithms are implement. For fully-crossed factorial study designs, termed multiple-reader multiple-case, significance testing of reader-averaged figure-of-merit differences between 'modalities' is implemented using either 'pseudovalue'-based or figure of merit-based methods. Single treatment analysis allows comparison of performance of a group of radiologists to a specified value, or comparison of 'CAD' performance to a group of radiologists interpreting the same cases. Sample size estimation tools are provided for 'ROC' and 'FROC' studies that allow estimation of relevant variances from a pilot study, in order to predict required numbers of readers and cases in a pivotal study. Utility and data file manipulation functions allow data to be read in any of the currently used input formats, including Excel, and the results of the analysis can be viewed in text or Excel output files.

Maintainer: Dev Chakraborty
Author(s): Dev Chakraborty [cre, aut, cph], Peter Philips [aut], Xuetong Zhai [aut], Lucy D'Agostino McGowan [ctb], Alejandro RodriguezRuiz [ctb]

License: GPL-3

Uses: bbmle, binom, dplyr, ggplot2, mvtnorm, numDeriv, openxlsx, Rcpp, stringr, testthat, knitr, rmarkdown

Released about 1 month ago.


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