A Composite Model for Classifying Parotid Shrinkage in Radiotherapy Patients Using Heterogeneous Data

Abstract

The identification of head-and-neck radiotherapy patients who will probably undergo the parotid gland shrinkage would help to plan adaptive therapy for them. The goal of this paper is to build predictive models to be included in a Decision Support System, able to operate with a wide set of heterogeneous data and classify parotid shrinkage. The main idea is to combine a set of models, each of them working distinctly with a group of features regarding clinical data, dosimetric data, or information extracted from Computed Tomography images, into one or more composite models using the most informative variables, in order to obtain more accurate and reliable decisions. Each of these models is built by using Likelihood-Fuzzy Analysis, which is based on both statistics and fuzzy logic, in order to grant semantic interpretability. This solution presents good accuracy, sensitivity and specificity, and compared with the wellknown Fisher’s Linear Discriminant Analysis results more effective in parotids classification, even in case of missing values. The best models operating with available features are achieved, and the advantages of acquiring data from different sources are outlined. Other interesting findings regard the confirmation of already known predictors, and the individuation of others still undisclosed.