dataset14

This is referred to in the book as the "FZR" dataset. It is a real ROC study, 
conducted on the same images and using the same radiologists, on treatments
"4" and "5" of dataset04. This was compared to highest rating inferred ROC
data from dataset04 to conclude, erroneously, that the highest rating assumption
is invalid. See book Section 13.6.2.

datasets

Implements software for assessing medical imaging systems, radiologists or computer aided detection algorithms.
Models of observer performance are implemented, including the binormal model (BM), the contaminated binormal model (CBM), the
correlated contaminated binormal model (CORCBM), and the radiological search model (RSM). The software and applications are
described in a book - Chakraborty DP: Observer Performance Methods for Diagnostic Imaging - Foundations, Modeling, and
Applications with R-Based Examples. Taylor-Francis LLC; 2017 - and its vignettes <https://dpc10ster.github.io/RJafroc/>.
Observer performance data collection paradigms are the receiver operating characteristic (ROC) and its location specific
extensions, primarily free-response ROC (FROC) and the location ROC (LROC). ROC data consists of single ratings per images.
A rating is the perceived confidence level that the image is that 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 reportable suspicious region
and the rating is the corresponding confidence level that it is a real lesion. LROC data consists of a rating and a forced
localization of the most suspicious region on every image. RJafroc supersedes the Windows version of JAFROC software
V4.2.1, <http://www.devchakraborty.com>:. Package functions are organized as follows. Data file related function names are
preceded by Df, curve fitting functions by Fit, included data sets by dataset, plotting functions by
Plot, significance testing functions by St, sample size related functions by Ss, data simulation functions
by Simulate and utility functions by Util. Implemented are figures of merit (FOMs) for quantifying performance,
functions for visualizing empirical operating characteristics: e.g., ROC, FROC, alternative FROC (AFROC) and weighted AFROC
(wAFROC) curves. Four maximum likelihood curve-fitting algorithms are implemented: the binormal model (BM), the contaminated
binormal model (CBM), the correlated contaminated binormal model (CORCBM) and the radiological search model (RSM). Unlike the
binormal model, CBM, CORCBM and RSM predict "proper" ROC curves that do not cross the chance diagonal. RSM fitting additionally
yields measures of search and lesion-classification performances. Search performance is the ability to find lesions while
avoiding finding non-lesions. Lesion-classification performance is the ability to correctly classify found lesions from found
non-lesions. For fully crossed study designs significance testing of reader-averaged FOM differences between modalities is
implemented via both Dorfman-Berbaum-Metz and the Obuchowski-Rockette methods, including Hillis' extensions. Also implemented are
single treatment analyses, which allow comparison of performance of a group of radiologists to a specified value, or comparison
to CAD to a group of radiologists interpreting the same cases. Crossed-modality analysis is implemented wherein there are two crossed
treatment factors and the desire is to determined performance in each treatment factor averaged over all levels of the other factor.
Sample size estimation tools are provided for ROC and FROC studies; these use estimates of the relevant variances from a pilot study
to predict required numbers of readers and cases in a pivotal study to achieve a desired power. 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. The methods are illustrated with several included datasets from the author's international
collaborations. This version corrects a few bugs noticed by users and extends the Excel file input format for
greater flexibility in handling non-crossed datasets and the sample size routines have been rewritten for ease of use.

Dev Chakraborty

RJafroc

Analyzing Diagnostic Observer Performance Studies

Peter Philips

Xuetong Zhai

Lucy D'Agostino McGowan

Alejandro RodriguezRuiz

dataset14 function

A list with 8 elements:<ul>
<li><code>NL</code> Ratings array [1:2, 1:4, 1:200, 1], of false positives, FPs</li>
<li><code>LL</code> Ratings array [1:2, 1:4, 1:100, 1], of true positives, TPs</li>
<li><code>lesionVector</code> array [1:100], number of lesions per diseased case, all set to 1</li>
<li><code>lesionID</code> array [1:100, 1], labels of lesions on diseased cases, all set to 1</li>
<li><code>lesionWeight</code> array [1:100, 1], weights (or clinical importance) of lesions, all set to 1</li>
<li><code>dataType</code> "ROC", the data type</li>
<li><code>modalityID</code> [1:2] "1" "2", treatment labels</li>
<li><code>readerID</code> [1:4] "1" "2" ..., reader labels</li>
</ul>

Format

Federica Zanca real (as opposed to inferred) ROC dataset — dataset14

Federica Zanca real (as opposed to inferred) ROC dataset

dataset14: Federica Zanca real (as opposed to inferred) ROC dataset

Description

Usage

Arguments

Format

References

Examples