Visual grading characteristics (VGC) analysis is a non-parametric rank-invariant method for analysis of visual grading data. In VGC analysis, image quality ratings for two different conditions are compared by producing a VGC curve, similar to how the ratings for normal and abnormal cases in receiver operating characteristic (ROC) analysis are used to create an ROC curve. The use of established ROC software for the analysis of VGC data has therefore previously been proposed. However, the ROC analysis is based on the assumption of independence between normal and abnormal cases. In VGC analysis, this independence cannot always be assumed, e.g. if the ratings are based on the same patients imaged under both conditions. A dedicated software intended for analysis of VGC studies, which takes possible dependencies between ratings into account in the statistical analysis of a VGC study, has therefore been developed. The software-VGC Analyzer-determines the area under the VGC curve and its uncertainty using non-parametric resampling techniques. This article gives an introduction to VGC Analyzer, describes the types of analyses that can be performed and instructs the user about the input and output data.

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The purpose of the present work was to investigate the validity of using single-reader-adapted receiver operating characteristics (ROC) software for analysis of visual grading characteristics (VGC) data. VGC data from four published VGC studies on optimisation of X-ray examinations, previously analysed using ROCFIT, were reanalysed using a recently developed software dedicated to VGC analysis (VGC Analyzer), and the outcomes [the mean and 95 % confidence interval (CI) of the area under the VGC curve (AUCVGC) and the p-value] were compared. The studies included both paired and non-paired data and were reanalysed both for the fixed-reader and the random-reader situations. The results showed good agreement between the softwares for the mean AUCVGC For non-paired data, wider CIs were obtained with VGC Analyzer than previously reported, whereas for paired data, the previously reported CIs were similar or even broader. Similar observations were made for the p-values. The results indicate that the use of single-reader-adapted ROC software such as ROCFIT for analysing non-paired VGC data may lead to an increased risk of committing Type I errors, especially in the random-reader situation. On the other hand, the use of ROC software for analysis of paired VGC data may lead to an increased risk of committing Type II errors, especially in the fixed-reader situation.