Histological heterogeneity of human glioblastomas investigated with an unsupervised neural network (SOM).

The histological variability of Glioblastomas (GB) precludes the modern assimilation of theses tumors into a single histological tumor group. As an alternative to statistical histological evaluation, we investigated 1489 human GB in order to discover whether they could be correctly classified using Self-Organizing Maps (SOM). In all tumors 50 histological features, as well as the age and sex of the patients, were examined. Four clusters of GB with a significance of 52 (maximal significance 60) were found. Cluster C1 contained 37.47% of all GB and 41.09% of all polymorphic glioblastomas (PG). Cluster C2 included 35.06% of all GB and 44.96% of all giant cell glioblastomas (GCG). Cluster C3 contained 16.45% of all GB with a significant component of astroblasts, glioblasts and oligodendroglia. Cluster C4 included 11.01% of all GB, 87.80% of the gliosarcomas (GS) and 36.72% of all GCG. Placing a series of component windows with their maps side by side allows the immediate recognition of the dependencies on variables and the determination of variables necessary to build the specific clusters. The SOM allow a realistic histological classification, comparable to the actual classification by the WHO. In addition, we found new, small subclusters of human GB which may have a clinical significance. With SOM one can learn to discriminate, discard and delete data, select histological and clinical or genetic variables that are meaningful, and consequently influence the result of patient management.