Van Dyke ROC dataset

This is referred to in the book as the "VD" dataset. It consists of 114 cases, 45 of which are diseased, interpreted in two treatments ("0" = single spin echo MRI, "1" = cine-MRI) by five radiologists using the ROC paradigm. Each diseased cases had an aortic dissection; the ROC paradigm generates one rating per case. Often referred to in the ROC literature as the Van Dyke dataset, which, along with the Franken dataset, has been widely used to illustrate advances in ROC methodology. The example below displays the ROC plot for the first modality and first reader.

dataset02

Format

A list with 3 elements: $ratings, $lesions and $descriptions; $ratings contain 3 elements, $NL, $LL and $LL_IL as sub-lists; $lesions contain 3 elements, $perCase, $IDs and $weights as sub-lists; $descriptions contain 7 elements, $fileName, $type, $name, $truthTableStr, $design, $modalityID and $readerID as sub-lists;

  • rating$NL num Array[1:2, 1:5, 1:114, 1] ratings of non-lesion localizations NLs

  • rating$LL num Array[1:2, 1:5, 1:45, 1] ratings of lesion localizations LLs

  • rating$LL_IL NA This placeholder is used only for LROC data

  • lesions$perCase int Array[1:45], number of lesions per diseased case

  • lesions$IDs num Array[1:45, 1], numeric labels of lesions on diseased cases

  • lesions$weights num Array[1:45, 1], weights (or clinical importance) of lesions

  • descriptions$fileName chr "dataset02" base name of dataset in `data` folder

  • descriptions$type chr "ROC" the data type

  • descriptions$name chr "VAN-DYKE", the name of the dataset

  • descriptions$truthTableStr num Array[1:2, 1:5, 1:114, 1:2] Truth table structure

  • descriptions$design chr "FCTRL", study design factorial dataset

  • descriptions$modalityID chr Array[1:2] "0" "1", modality labels

  • descriptions$readerID chr Array[1:5] the reader labels

References

Van Dyke CW, et al. Cine MRI in the diagnosis of thoracic aortic dissection. 79th RSNA Meetings. 1993.

Examples

res <- str(dataset02)
#> List of 3
#>  $ ratings     :List of 3
#>   ..$ NL   : num [1:2, 1:5, 1:114, 1] 1 3 2 3 2 2 1 2 3 2 ...
#>   ..$ LL   : num [1:2, 1:5, 1:45, 1] 5 5 5 5 5 5 5 5 5 5 ...
#>   ..$ LL_IL: logi NA
#>  $ lesions     :List of 3
#>   ..$ perCase: int [1:45] 1 1 1 1 1 1 1 1 1 1 ...
#>   ..$ IDs    : num [1:45, 1] 1 1 1 1 1 1 1 1 1 1 ...
#>   ..$ weights: num [1:45, 1] 1 1 1 1 1 1 1 1 1 1 ...
#>  $ descriptions:List of 7
#>   ..$ fileName     : chr "dataset02"
#>   ..$ type         : chr "ROC"
#>   ..$ name         : chr "VAN-DYKE"
#>   ..$ truthTableStr: num [1:2, 1:5, 1:114, 1:2] 1 1 1 1 1 1 1 1 1 1 ...
#>   ..$ design       : chr "FCTRL"
#>   ..$ modalityID   : Named chr [1:2] "0" "1"
#>   .. ..- attr(*, "names")= chr [1:2] "0" "1"
#>   ..$ readerID     : Named chr [1:5] "0" "1" "2" "3" ...
#>   .. ..- attr(*, "names")= chr [1:5] "0" "1" "2" "3" ...
## PlotEmpOpChr(dataset = dataset02, opChType = "ROC")$Plot