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

BMC Medical Informatics and Decision Making

Research Article

Yorgos Goletsis¹  Dimitrios I Fotiadis²  Konstantinos P Exarchos³ 

[1] Department of Economics, University of Ioannina, GR45110, Ioannina, Greece;Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR45110, Ioannina, Greece;Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR45110, Ioannina, Greece;Foundation for Research and Technology - Hellas, Institute of Molecular Biology and Biotechnology, Department of Biomedical Research, GR45110, Ioannina, Greece;

关键词: Feature Selection;    Random Forest;    Oral Cancer;    Oral Squamous Cell Carcinoma;    Feature Selection Algorithm;   

DOI  :  10.1186/1472-6947-12-136

 received in 2012-04-25, accepted in 2012-11-01,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundIn 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.MethodsWe 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.ResultsBy 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.ConclusionsKnowing 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.

【 授权许可】

Unknown   
© Exarchos et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.