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Diagnosis of human oligodendrogliomas with the help of the NeuroShell Easy Classifier neural network
J Iglesias1, J Esparza, M Scherf
1Laboratory of Neuropathology, Katharinenhospital, Stuttgart, Germany. jr.iglesias@katharinenhospital.de
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.
Study Design:
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.
Conclusion:
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.