Survival model in oral squamous cell carcinoma based on clinicopathological parameters, molecular markers and support vector machines

Authors:
Pablo Rosado;Paloma Lequerica-FernáNdez;Lucas VillallaíN;Ignacio PeñA;Fernando Sanchez-Lasheras;Juan C. De Vicente
Affiliations:
Department of Oral and Maxillofacial Surgery, Hospital de Cabueñes, Gijón, Asturias, Spain;Department of Analisis and Biochemistry, Hospital San Agustín, Avilés, Spain;Department of Oral and Maxillofacial Surgery, Hospital Universitario Central de Asturias, Oviedo, Spain;Department of Oral and Maxillofacial Surgery, Hospital Universitario Central de Asturias, Oviedo, Spain;Research Department, Tecniproject, Oviedo, Spain;Department of Oral and Maxillofacial Surgery, Hospital Universitario Central de Asturias, Oviedo, Spain and Instituto Universitario de Oncología del Principado de Asturias (IUOPA), Oviedo, Sp ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

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Abstract

The aim of the present study is to find an intelligent and efficient model, based on Support Vector Machines (SVM), able to predict prognosis in patients with oral squamous cell carcinoma (OSCC). A total of 34 clinical and molecular variables were studied in 69 patients suffering from an OSCC. Variables were selected by means of two methods applied in parallel (Non-concave penalty and Newton's methods). The implementation of a predictive model was performed using the SVM as a classifier algorithm. Finally, its classification ability was evaluated by discriminant analysis. Recurrence, number of recurrences, and TNM stage have been identified as the most relevant prognosis factors with both used methods. Classification rates reached 97.56% and 100% for alive and dead patients, respectively (overall classification rate of 98.55%). SVM techniques build tools able to predict with high accuracy the survival of a patient with OSCC.