A composite model for classifying parotid shrinkage in radiotherapy patients using heterogeneous data

Marco Pota, Elisa Scalco, Giuseppe Sanguineti, Maria Luisa Belli, Giovanni Mauro Cattaneo, Massimo Esposito, Giovanna Rizzo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages257-266
Number of pages10
Volume9105
ISBN (Print)9783319195506
DOIs
Publication statusPublished - 2015
Event15th Conference on Artificial Intelligence in Medicine, AIME 2015 - Pavia, Italy
Duration: Jun 17 2015Jun 20 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9105
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other15th Conference on Artificial Intelligence in Medicine, AIME 2015
CountryItaly
CityPavia
Period6/17/156/20/15

Fingerprint

Radiotherapy
Shrinkage
Composite
Composite materials
Interpretability
Missing Values
Computed Tomography
Predictive Model
Decision Support Systems
Discriminant Analysis
Model
Fuzzy Logic
Therapy
Specificity
Discriminant analysis
Predictors
Likelihood
Decision support systems
Classify
Fuzzy logic

Keywords

  • Fuzzy logic
  • Parotid gland shrinkage
  • Radiotherapy
  • Statistics

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Pota, M., Scalco, E., Sanguineti, G., Belli, M. L., Cattaneo, G. M., Esposito, M., & Rizzo, G. (2015). A composite model for classifying parotid shrinkage in radiotherapy patients using heterogeneous data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9105, pp. 257-266). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9105). Springer Verlag. https://doi.org/10.1007/978-3-319-19551-3_34

A composite model for classifying parotid shrinkage in radiotherapy patients using heterogeneous data. / Pota, Marco; Scalco, Elisa; Sanguineti, Giuseppe; Belli, Maria Luisa; Cattaneo, Giovanni Mauro; Esposito, Massimo; Rizzo, Giovanna.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9105 Springer Verlag, 2015. p. 257-266 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9105).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Pota, M, Scalco, E, Sanguineti, G, Belli, ML, Cattaneo, GM, Esposito, M & Rizzo, G 2015, A composite model for classifying parotid shrinkage in radiotherapy patients using heterogeneous data. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 9105, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9105, Springer Verlag, pp. 257-266, 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, 6/17/15. https://doi.org/10.1007/978-3-319-19551-3_34
Pota M, Scalco E, Sanguineti G, Belli ML, Cattaneo GM, Esposito M et al. A composite model for classifying parotid shrinkage in radiotherapy patients using heterogeneous data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9105. Springer Verlag. 2015. p. 257-266. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-19551-3_34
Pota, Marco ; Scalco, Elisa ; Sanguineti, Giuseppe ; Belli, Maria Luisa ; Cattaneo, Giovanni Mauro ; Esposito, Massimo ; Rizzo, Giovanna. / A composite model for classifying parotid shrinkage in radiotherapy patients using heterogeneous data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9105 Springer Verlag, 2015. pp. 257-266 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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