The application of image analysis and neural network technology to the study of large-cell liver-cell dysplasia and hepatocellular carcinoma

Liver cell dysplasia (LCD) is considered a preneoplastic lesion, whose characterization and differentiation from hepatocellular carcinoma (HCC) and from the reactive changes seen in cirrhosis has been controversial. We studied 12 cases of LCD (large cell type) with image analysis techniques (IA) and compared the findings with those of HCC (n = 40), and a spectrum of non-neoplastic hepatic lesions including normal liver and cirrhosis (n = 49). A minimum of 200 Feulgen-stained nuclei were measured from each lesion with the CAS 200 image analysis system. The data were collected with the aid of CellSheet software. Thirty-four variables were measured, including geometric, textural, and photometric nuclear features and DNA ploidy. The data were analyzed with multivariate statistics and a backpropagation neural network (NN). Stepwise statistical analysis selected 22 variables that were statistically significant in the three groups with P values