An improved classification of foci for carcinogenicity testing by statistical descriptors

Carcinogenesis is a multi-step process involving genetic alterations and non-genotoxic mechanisms. The in vitro cell transformation assay (CTA) is a promising tool for both genotoxic and non-genotoxic carcinogenesis. CTA relies on the ability of cells (e.g. BALB/c 3T3 mouse embryo fibroblasts) to develop a transformed phenotype after the treatment with suspected carcinogens. The classification of the transformed phenotype is based on coded morphological features, which are scored under a light microscope by trained experts. This procedure is time-consuming and somewhat prone to subjectivity. Herewith we provide a promising approach based on image analysis to support the scoring of malignant foci in BALB/c 3T3 CTA. The image analysis system is a quantitative approach, based on measuring features of malignant foci: dimension, multilayered growth, and invasivity into the surrounding monolayer of non-transformed cells. A logistic regression model was developed to estimate the probability for each focus to be transformed as a function of three statistical image descriptors. The estimated sensitivity of the derived classifier (untransformed against Type III) was 0.9, with an Area Under the Curve (AUC) value equal to 0.90 under the Receiver Operating Characteristics (ROC) curve.