Introduction of a neuronal network as a tool for diagnostic analysis and classification based on experimental pathologic data.

A neuronal network, as well as uni- and multivariate statistics and a discriminant analysis were applied to a morphometric database of 58 cases with thyroid neoplasms and normal thyroid tissue. The ability to classify cases correctly according to their diagnosis was compared between the neuronal network and discriminant analysis. For all pairwise comparisons, classification by neuronal network was as least as good as classification by discriminant analysis. For some comparisons, the neuronal network provided more correct diagnoses than discriminant analysis. On the contrary, in a comparison between tumors which are not significantly different according to multivariate statistics, the network reclassifies only half of the cases correctly, whereas discriminant analysis falsely suggests the possibility of classifying cases with either diagnosis. Our results confirm a higher sensitivity of the neuronal network to the diagnostic information contained in the present morphometric database, and we will therefore use this concept for analysis and diagnostic classification in further morphometric studies.