Generating prior probabilities for classifiers of brain tumours using belief networks

The method of "belief networks" is introduced as a means of generating probabilities that a tumour is any given type. The belief networks are constructed using a database of paediatric tumour cases consisting of data collected over five decades; the problems associated with using this data are discussed. To verify the usefulness of the networks, an application of the method is presented in which prior probabilities were generated and combined with a classification of tumours based solely on MRS data.Belief networks were constructed from a database of over 1300 cases. These can be used to generate a probability that a tumour is any given type. Networks are presented for astrocytoma grades I and II, astrocytoma grades III and IV, ependymoma, pineoblastoma, primitive neuroectodermal tumour (PNET), germinoma, medulloblastoma, craniopharyngioma and a group representing rare tumours, "other". Using the network to generate prior probabilities for classification improves the accuracy when compared with generating prior probabilities based on class prevalence.Bayesian belief networks are a simple way of using discrete clinical information to generate probabilities usable in classification. The belief network method can be robust to incomplete datasets. Inclusion of a priori knowledge is an effective way of improving classification of brain tumours by non-invasive methods.The current "gold standard" for brain tumour diagnosis is histopathology which requires a sample of tumour obtained at operation. These operations have an inherent risk of morbidity and mortality. Magnetic Resonance Imaging (MRI), Magnetic Resonance Spectroscopy (MRS) and other imaging modalities may offer a non-invasive way of making a diagnosis, but no method has yet attained sufficient accuracy to replace histopathology. MRS in particular has been shown to provide useful information about the biochemical content of a brain tumours [1] and numerous methods for classifying brain tumours based on magnetic re