Outcome prediction for salivary gland cancer using multivariate adaptative regression splines (MARS) and self-organizing maps (SOM)
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Over the last decades, advances in diagnosis and tissue microsurgical reconstruction of soft tissues have modified the therapeutic approach to salivary gland cancers, but long-term survival rates have increased only marginally. Due to the relatively low frequency of these tumors together with their diverse histopathological types, it is not easy to perform a prognosis assessment. Multivariate adaptative regression splines (MARS) is a data mining technique with a well-known ability to describe a response starting from a large number of predictors. In this work, MARS was used for determining the prognosis of cancers of salivary glands using clinical and histological variables, as well as molecular markers. Here, we have generated four different models combining different sets of variables, with sensitivities and specificities ranging from 95.45 to 100%. Specifically, one of these models which combined five clinical variables (Tumor size –T–, neck node metastasis –N–, distant metastasis –M–, age and number of tumor recurrences) plus one molecular factor (gelatinase B -MMP-9-) showed a sensitivity and a specificity of 100%. Therefore, the MARS model was applied to the modeling of the influence of several clinical and molecular variables on the prognosis of salivary gland cancers with success. A self-organizing map (SOM) is a type of neural network what was used here to determine a prognostic model composed for four variables: N, M, number of recurrences and tumor type. The sensitivity of this model was that of 97%, and its specificity was that of 94.7%.
KeywordsSalivary gland cancer Prognosis Data mining Multivariate adaptative regression splines (MARS)
This work was supported by a grant for scientific research from the Ministry of Health, Spain (Instituto de Salud Carlos III, PI070675).
Compliance with ethical standards
Conflict of interest
The authors report no conflicts of interest in this work.
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