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 language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher | Springer Verlag |
| Pages | 257-266 |
| Number of pages | 10 |
| Volume | 9105 |
| ISBN (Print) | 9783319195506 |
| DOIs | |
| Publication status | Published - 2015 |
| Event | 15th Conference on Artificial Intelligence in Medicine, AIME 2015 - Pavia, Italy Duration: Jun 17 2015 → Jun 20 2015 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 9105 |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 15th Conference on Artificial Intelligence in Medicine, AIME 2015 |
|---|---|
| Country | Italy |
| City | Pavia |
| Period | 6/17/15 → 6/20/15 |
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Keywords
- Fuzzy logic
- Parotid gland shrinkage
- Radiotherapy
- Statistics
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
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 proceeding › Conference contribution
}
TY - GEN
T1 - A composite model for classifying parotid shrinkage in radiotherapy patients using heterogeneous data
AU - Pota, Marco
AU - Scalco, Elisa
AU - Sanguineti, Giuseppe
AU - Belli, Maria Luisa
AU - Cattaneo, Giovanni Mauro
AU - Esposito, Massimo
AU - Rizzo, Giovanna
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Fuzzy logic
KW - Parotid gland shrinkage
KW - Radiotherapy
KW - Statistics
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U2 - 10.1007/978-3-319-19551-3_34
DO - 10.1007/978-3-319-19551-3_34
M3 - Conference contribution
SN - 9783319195506
VL - 9105
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 257
EP - 266
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
ER -