Computer-assisted analysis of medulloblastoma. A cytologic study

To explore data from a set of cases of medulloblastoma to see whether quantitative image analysis might suggest evidence for the existence of lower and higher grade lesions.

Fourteen consecutive cases of medulloblastoma were obtained. Smears were stained with toluidine blue. For each case, 50 nuclei were measured and a number of densitometric features extracted.

The existence of two subgroups of cases, identified as lower and higher grade groups, was suggested by a plot of the total optical density versus nuclear area. Two nuclear texture features--the number of pixels with the same optical density value occurring consecutively in the nucleus and the proportion of pixels in the high optical density range--divided the cases into the same subgroups. The use of a clustering algorithm established two clusters that corresponded to that subgrouping except for one case. Discriminant analysis gave an identical classification, with the misplaced case having a borderline discriminant function score. An unsupervised learning algorithm based on an adaptive distance metric formed two clusters and assigned the borderline case to the low grade subgroup. The grouping obtained by quantitative analysis was only partly related to the grade of nuclear atypia subjectively evaluated.

In our series of medulloblastomas, quantitative analysis provided a means of detecting differences in the nuclear size and texture that allowed the classification of cases into two subgroups.