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The invasive front of carcinomas may vary in complexity from smooth to highly complex when the front splits up into small cell clusters or even single cancer cells. The degree of complexity is usually estimated visually and semiquantitatively by a pathologist, although more objective methods based on computer-assisted image analysis are available. In this study, we compared the visual estimation of the irregularity of the tumour invasion front of colon carcinomas to different quantitative image analytical techniques and defined a complexity index for the invasive margin. Sections from 29 archived colon carcinomas were stained immunohistochemically for cytokeratin 8. Images of the tumour invasion front were read into a computer and thresholded so that the tumour tissue became black and the background white or so that the tumour front was outlined by a single pixel line. The invasive front was visually classified into four degrees of irregularity by a pathologist. The complexity of the front was then assessed using four different image analysis techniques, i.e. the estimation of fractal dimension, tumour front length, number of tumour cell clusters and lacunarity. Fractal dimension and tumour cell clusters together gave the best correlation to visual grading using a discriminant analysis. A cluster analysis and a tree diagram analysis were then performed and were found to be superior to visual estimation. The clusters represent different degrees of complexity and the result of the tree diagram analysis can be used to assign complexity indices to colon tumours. The fractal dimension separated tumours up to a certain level (1.5-1.6) of complexity. When the tumour front split up into small cell clusters, the counting of tumour cell clusters separated the cells over and above the fractal dimension. This new technique can be used to objectively and quantitatively describe the complexity of the invasive front of tumours.

Characterization of colon carcinoma growth pattern by computerized morphometry: Definition of a complexity index