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Open Access Research article

A multiscale and multiparametric approach for modeling the progression of oral cancer

Konstantinos P Exarchos12, Yorgos Goletsis3 and Dimitrios I Fotiadis1*

Author Affiliations

1 Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR45110, Ioannina, Greece

2 Foundation for Research and Technology - Hellas, Institute of Molecular Biology and Biotechnology, Department of Biomedical Research, GR45110, Ioannina, Greece

3 Department of Economics, University of Ioannina, GR45110, Ioannina, Greece

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BMC Medical Informatics and Decision Making 2012, 12:136  doi:10.1186/1472-6947-12-136

Published: 22 November 2012



In this work, we propose a multilevel and multiparametric approach in order to model the growth and progression of oral squamous cell carcinoma (OSCC) after remission. OSCC constitutes the major neoplasm of the head and neck region, exhibiting a quite aggressive nature, often leading to unfavorable prognosis.


We formulate a Decision Support System assembling a multitude of heterogeneous data sources (clinical, imaging tissue and blood genomic), aiming to capture all manifestations of the disease. Our primary aim is to identify the factors that dictate OSCC progression and subsequently predict potential relapses of the disease. The discrimination potential of each source of data is initially explored separately, and afterwards the individual predictions are combined to yield a consensus decision achieving complete discrimination between patients with and without a disease relapse. Moreover, we collect and analyze gene expression data from circulating blood cells throughout the follow-up period in consecutive time-slices, in order to model the temporal dimension of the disease. For this purpose a Dynamic Bayesian Network (DBN) is employed which is able to capture in a transparent manner the underlying mechanism dictating the disease evolvement, and employ it for monitoring the status and prognosis of the patients after remission.


By feeding as input to the DBN data from the baseline visit we achieve accuracy of 86%, which is further improved to complete discrimination when data from the first follow-up visit are also employed.


Knowing in advance the progression of the disease, i.e. identifying groups of patients with higher/lower risk of reoccurrence, we are able to determine the subsequent treatment protocol in a more personalized manner.