VanDyke: Multi-reader multi-case dataset

In MRMCbinary: Multi-Reader Multi-Case Analysis of Binary Diagnostic Tests

Multi-reader multi-case dataset

Description

Example data from a study comparing the relative performance of cinematic presentation of magnetic resonance imaging (CINE MRI) to single spin-echo magnetic resonance imaging (SE MRI) for the detection of thoracic aortic dissection (Van Dyke et al., 1993).

Usage

VanDyke

Format

A data frame with 1140 rows and 7 variables:

reader

Reader identifier for the five radiologists

treatment

Treatment identifier for the two imaging modalities

case

Case identifiers for 114 cases

case2

Case identifier (cases nested within readers)

case3

Case identifier (cases nested within treatments)

truth

Indicator for thoracic aortic dissection (i.e., true disease status): 1 = performed (i.e., patients with aortic dissection imaged with both SE MRI and CINE MRI) or 0 = not performed (i.e., patients without dissection imaged with both SE MRI and CINE MRI)

rating

Five-point ratings given to case images by the radiologists (i.e., diagnostic test result): 1 = definitely no aortic dissection, 2 = probably no aortic dissection, 3 = unsure about aortic dissection, 4 = probably aortic dissection, or 5 = definitely aortic dissection

Details

This example compares the relative performance of SE MRI with the CINE MRI in detecting thoracic aortic dissection. There are 45 patients with an aortic dissection and 69 patients without a dissection imaged with both SE MRI and CINE MRI. One can directly use this data from MRMCaov package. See Source.

Source

This data are available at https://perception.lab.uiowa.edu and https://github.com/brian-j-smith/MRMCaov/tree/master/data.

References

Van Dyke, C. W., White, R. D., Obuchowski, N. A., Geisinger, M. A., Lorig, R. J., & Meziane, M. A. (1993). Cine MRI in the diagnosis of thoracic aortic dissection. 79th RSNA Meetings. Chicago, IL, 28.

Examples

## Load example data
data(VanDyke)

## Return the first parts of an object
head(VanDyke)

## Extract unique modalities
unique(VanDyke$treatment)

## Extract Unique readers
unique(VanDyke$reader)

## Create binary test results (Y_ijk)
VanDyke$Y <- as.numeric(VanDyke$rating >= 3)

MRMCbinary documentation built on April 3, 2025, 9:17 p.m.