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In a recent series of papers written by Jaworska with different coauthors, compelling reasons for adopting a probabilistic approach to Integrated Testing Strategies were detailed. In a case study on skin sensitization, a Bayesian Network proved to be effective in adapting testing strategies to the available evidence. There is no doubt that probabilistic Integrated Testing Strategies are one way to pursue the goals of 3Rs effectively; nevertheless, some issues deserve further comment to pinpoint statistical criticalities and to widen the methodological perspective towards Bayesian graphical models.

The human carcinogenicity evaluation of chemicals has a great impact on public health. In vitro methods, such as the cell transformation assay (CTA), allow for a fast and reliable assessment of the carcinogenic potential of a chemical compound in comparison with the standard two-year bioassay. The scoring and classification of foci in selected cell lines is performed, after staining, by light microscopy. Foci can be separated into three classes: type I, which are scored as non-transformed, and types II and III that are considered to include fully transformed foci. However, in a number of cases, even an expert is uncertain about the attribution of a focus to a given class, due to its mixed or intermediate nature. Here, we suggest a simple approach to classifying mixed or intermediate foci by exploiting the quantitative information available from images, which is captured by statistical descriptors. A quantitative index is proposed, to describe the degree of dissimilarity of mixed and intermediate images to the three well-distinguished classes., (2011 FRAME.)