Early classification of parotid glands shrinkage in radiotherapy patients: A comparative study

• Published in: Biosystems engineering Vol. 138; pp. 77 - 89
• Main Authors: Pota, Marco, Scalco, Elisa, Sanguineti, Giuseppe, Cattaneo, Giovanni Mauro, Esposito, Massimo, Rizzo, Giovanna
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2015
• Subjects: CT based features; Fuzzy logic; Knowledge extraction; Parotid gland shrinkage; Radiotherapy; Statistics; Fuzzy logic; Parotid gland shrinkage; Radiotherapy; Knowledge extraction; Statistics; CT based features
• ISSN: 1537-5110; 1537-5129
• DOI: 10.1016/j.biosystemseng.2015.06.007

During radiotherapy treatment of patients with head-and-neck cancer, the possibility that parotid glands shrink was evidenced, connected with increasing risk of acute toxicity. In this ambit, the early identification of patients in danger is of primary importance, in order to treat them with adaptive therapy. This work studies different approaches for classifying parotid gland samples, taking into account textural features extracted from computed tomography (CT) images of monitored patients. A real dataset is used, and accuracy, sensitivity and specificity are counted as classification performances. Therefore, firstly, different procedures to define classes are compared in terms of their physical meaning and classification performances. Then, different methods for extracting knowledge from the dataset are implemented and compared in terms of performances and model interpretability. First-rate performance was obtained by using Likelihood-Fuzzy Analysis (LFA), which is a recently developing method based on the use of statistical information by means of Fuzzy Logic. The interpretable models extracted with LFA also allow identifying among textural features those able to predict parotid shrinkage. Some of these features are already known and are confirmed here, others are new, and some of them are very early predictors. Finally, an example of textural feature monitoring and classification of a patient is presented, through a reasoning scheme similar to human reasoning, based on the interpretation of simple rule-based models using linguistic variables. •Textural features extracted from computed tomography images to classify RT patients.•Physical meaning and performances of different procedures to define classes.•Performances and interpretability of different methods for extracting knowledge.•First-rate: statistical/fuzzy method named Likelihood-Fuzzy Analysis (LFA).•Known predictors confirmed, new early predictors observed, model interpretation.