Note: [] Address for correspondence: Priv.-Doz. Dr. med. J.R.
Iglesias-Rozas Klinikum Stuttgart. Katharinenhospital Institut für
Pathologie (Neuropathologie) (Ärzt. Dir.: Prof. Dr. med. A. Bosse)
Kriegsbergstr. 60 D-70174 Stuttgart Tel: ++ 49 +711/278 4918 Fax: ++49 +711/278
4909 E-mail: jr.iglesias@katharinenhospital.de Iglesias@z.zgs.de
Note: []
Abstract: Using a variant of unsupervised neural networks (Self-Organizing
Maps, SOM), it is intended to examine the ability to reproduce a
clinical-pathological classification of patients with glioblastomas. This SOM
provides a powerful means to visualize and analyze complex data sets without
prior statistical knowledge. 58 consecutive cases were selected for evaluation.
The single criterion for the selection of the cases was the survival of the
patients. The SOM is realized by a two-dimensional hexagonal grid (a map) with
2047 nodes or neurons. The nodes on the grid with 6 clinical-pathological
variables of glioblastomas gradually adapt themselves to the intrinsic shape of
the data distribution. After the operation 25 patients were treated with
conventional radiotherapy and 17 patients with radiotherapy and chemotherapy,
16 Patients received neither radiation nor chemotherapy. The minimal survival
was < 1 month, the maximal survival 26 months. Viscovery SOM displays
colored map regions, in our study patients with glioblastomas, grouped in
clusters. Two clusters show the maximal significance. In a small cluster the
glioblastoma patients with the long survival are grouped. These patients have
been treated with radiotherapy and chemotherapy. The second cluster contains
all other patients. The clusters can be compared to clusters of sex, age,
survival and histological malignancy. The SOM allows a specific visual
evaluation of new treatments and a more effective comparison with established
tumor management.
