A multilevel and multiscale approach for the prediction of oral cancer reoccurrence

Abstract

Oral cancer is the predominant neoplasm of the head and neck. Annually, more than 0.5 million new patients are diagnosed with oral cancer, worldwide. After the initial treatment and patient remission, reoccurrence rates still remain quite high. Early identification of such relapses is of crucial significance. Up to now, several approaches have been proposed for this purpose yielding however, unsatisfactory results. This is mainly attributed to the non-unified nature of these studies which focus only on a subset of the factors involved in the development and reoccurrence of oral cancer. Here we propose an orchestrated approach based on Dynamic Bayesian Networks (DBNs) for the prediction of a potential relapse after the disease has reached remission. A broad range of heterogeneous data sources featuring clinical, imaging and genomic information are assembled and analyzed during a predefined time-span, in order to decipher new and informative feature groups that correlate significantly with the progression of the disease and identify early potential relapses (local or metastatic) of the disease. © 2010 International Federation for Medical and Biological Engineering.