datasetCrossedModality

This is a crossed treatment dataset, see book Section 18.5. There are two treatment factors. 
The first treatment factor <code>modalityID1</code> can be "F" or "I", which represent two CT reconstruction
algorithms. The second treatment factor <code>modalityID2</code> can be "20" "40" "60" "80", which 
represent the mAs values of the image acquisition. The factors are fully crossed. The function 
<code><a rd-options="" href="/link/StSignificanceTestingCrossedModalities?package=RJafroc&version=1.2.0" data-mini-rdoc="RJafroc::StSignificanceTestingCrossedModalities">StSignificanceTestingCrossedModalities</a></code> analyzes such datasets.

datasets

Tools for quantitative assessment of medical imaging systems, radiologists
or computer aided detection ('CAD') algorithms. Implements methods described in the book:
'Chakraborty' (2017) <ISBN:978-1482214840>. 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' <http://www.devchakraborty.com>. 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) <{doi:10.1088/0031-9155/51/14/012}> . 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.

Dev Chakraborty

RJafroc

Analyzing Diagnostic Observer Performance Studies

Peter Philips

Xuetong Zhai

Lucy D'Agostino McGowan

Alejandro RodriguezRuiz

datasetCrossedModality function

A list with 9 elements:<ul>
<li><code>NL</code> Ratings array [1:2, 1:4, 1:11, 1:68, 1:5], of non-lesion localizations, NLs</li>
<li><code>LL</code> Ratings array [1:2, 1:4, 1:11, 1:34, 1:3], of lesion localizations, LLs</li>
<li><code>lesionNum</code> array [1:34], number of lesions per diseased case, all set to 1</li>
<li><code>lesionID</code> array [1:34, 3], labels of lesions on diseased cases, all set to 1</li>
<li><code>lesionWeight</code> array [1:34, 3] weights (or clinical importance) of lesions, all set to 1</li>
<li><code>dataType</code> "FROC", the data type</li>
<li><code>modalityID1</code> [1:2] "F" "I", treatment labels</li>
<li><code>modalityID2</code> [1:4] "20" "40" "60" "80", treatment labels</li>
<li><code>readerID</code> [1:11] "1" "10" "11" ..., reader labels</li>
</ul>

Format

This is a crossed treatment dataset, see book Section 18.5. There are two treatment factors. 
The first treatment factor <code>modalityID1</code> can be "F" or "I", which represent two CT reconstruction
algorithms. The second treatment factor <code>modalityID2</code> can be "20" "40" "60" "80", which 
represent the mAs values of the image acquisition. The factors are fully crossed. The function 
<code><a rd-options='' href='StSignificanceTestingCrossedModalities'>StSignificanceTestingCrossedModalities</a></code> analyzes such datasets.

datasetCrossedModality: John Thompson crossed treatment FROC dataset

Description

Usage

Arguments

Format

References

Examples