Diagnosis of human oligodendrogliomas with the help of the NeuroShell Easy Classifier neural network.
OBJECTIVE
To examine whether a suitable solution can be found concerning the ability to reproduce the histologic classification of human oligodendrogliomas with the assistance of the NeuroShell Easy Classifier neural network.


METHODS
Histologic sections of 449 human oligodendrogliomas were selected. The diagnostic task was given by differentiation of three oligodendroglioma types: 121 low grade oligodendrogliomas, World Health Organization grade 2; 180 low grade oligoastrocytomas; and 148 anaplastic oligodendrogliomas, grade 3. Age, sex and 50 histologic characteristics were examined in each case, describing the presence of a specific histologic feature on a scale of four (zero, absence of the feature; three, abundant presence). From each group, two-thirds of randomly selected tumors were available for the training set and one-third for the testing set.


RESULTS
In the three-class problem, 98.88% of the tumors were correctly classified (testing set). Ninety-nine percent of new testing tumors were correctly classified with Easy Classifier as low grade and anaplastic oligodendrogliomas. In the case of low grade oligodendrogliomas versus low grade oligoastrocytomas, 99% of new tumors were correctly classified.


CONCLUSIONS
The main conclusion from this study is that Easy Classifier was able to differentiate, with high accuracy, sensitivity and specificity, among the three types of oligodendrogliomas.
Iglesias
J
J
Laboratory of Neuropathology, Katharinenhospital, Stuttgart, Germany. jr.iglesias@katharinenhospital.de
Esparza
J
J
Scherf
M
M
D001932:Brain Neoplasms / Q000145:classification / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D009367:Neoplasm Staging; D016571:Neural Networks, Computer; D009837:Oligodendroglioma / Q000145:classification / Q000175:diagnosis*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity