A hybrid prognostic model for oral cancer based on clinicopathologic and genomic markers

• Main Authors: Siow, Wee Chang, Sameem Abdul Kareem, Amir Feisal Merican Aljunid Merican, Rosnah Zain
• Format: Article
• Language: English
• Published: Universiti Kebangsaan Malaysia 2014
• Online Access: http://journalarticle.ukm.my/7051/1/10_Siow_Wee_Chang.pdf; http://journalarticle.ukm.my/7051/; http://www.ukm.my/jsm/
• Institution: Universiti Kebangsaan Malaysia

There are very few prognostic studies that combine both clinicopathologic and genomic data. Most of the studies use only clinicopathologic factors without taking into consideration the tumour biology and molecular information, while some studies use genomic markers or microarray information only without the clinicopathologic parameters. Thus, these studies may not be able to prognoses a patient effectively. Previous studies have shown that prognosis results are more accurate when using both clinicopathologic and genomic data. The objectives of this research were to apply hybrid artificial intelligent techniques in the prognosis of oral cancer based on the correlation of clinicopathologic and genomic markers and to prove that the prognosis is better with both markers. The proposed hybrid model consisting of two stages, where stage one with ReliefF-GA feature selection method to find an optimal feature of subset and stage two with ANFIS classification to classify either the patients alive or dead after certain years of diagnosis. The proposed prognostic model was experimented on two groups of oral cancer dataset collected locally here in Malaysia, Group 1 with clinicopathologic markers only and Group 2 with both clinicopathologic and genomic markers. The results proved that the proposed model with optimum features selected is more accurate with the use of both clinicopathologic and genomic markers and outperformed the other methods of artificial neural network, support vector machine and logistic regression. This prognostic model is feasible to aid the clinicians in the decision support stage and to identify the high risk markers to better predict the survival rate for each oral cancer patient.