An improved classification of foci for carcinogenicity testing by statistical descriptors

• Published in: Toxicology in vitro 2015-10, Vol.29 (7), p.1839-1850
• Main Authors: Callegaro, Giulia, Stefanini, Federico Mattia, Colacci, Annamaria, Vaccari, Monica, Urani, Chiara
• Format: Article
• Language: English
• Subjects: Animals; Area Under Curve; BALB 3T3 Cells; BALB/c 3T3 cell line; Biological Assay; Carcinogenicity Tests; Carcinogens - toxicity; Cell transformation assay; Cell Transformation, Neoplastic; foci; foci classification; Image Interpretation, Computer-Assisted; Logistic Models; Mice; ROC Curve; Statistical image descriptors
• ISSN: 0887-2333; 1879-3177
• DOI: 10.1016/j.tiv.2015.07.013

[Display omitted] •A tool based on image analysis to support the scoring of malignant foci in BALB/c 3T3 CTA is presented.•Descriptors of foci growth patterns were quantitatively measured through digital image analysis.•A binary logistic model exploiting measured features provided probability estimates of foci transformation.•The estimated sensitivity of the derived classifier (untransformed against Type III) was 0.9, with an AUC value equal to 0.90.•The proposed model may be casted in the wider perspective of Integrated Testing Strategies. 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.