DocumentCode :
272614
Title :
Predicting xerostomia induced by IMRT treatments: A logistic regression approach
Author :
Soares, Inês ; Dias, Joana ; Rocha, Humberto ; do Carmo Lopes, Maria ; Ferreira, Briigida
Author_Institution :
Dept. of Comput. Eng., Univ. of Coimbra, Coimbra, Portugal
fYear :
2014
fDate :
2-5 Nov. 2014
Firstpage :
72
Lastpage :
77
Abstract :
Radiotherapy is one of the main treatments used against cancer. Radiotherapy uses radiation to destroy cancerous cells trying, at the same time, to minimize the damages in healthy tissues. The planning of a radiotherapy treatment is patient dependent, resulting in a lengthy trial and error procedure until a treatment complying as most as possible with the medical prescription is found. Intensity Modulated Radiation Therapy (IMRT) is one technique of radiation treatment that allows the achievement of a high degree of conformity between the area to be treated and the dose absorbed by healthy tissues. Nevertheless, it is still not possible to eliminate completely the potential treatments´ side-effects. In this retrospective study we use the clinical data from patients with head-and-neck cancer treated at the Portuguese Institute of Oncology of Coimbra and explore the possibility of classifying new and untreated patients according to the probability of xerostomia 12 months after the beginning of IMRT treatments by using a logistic regression approach. The results obtained show that the classifier presents a high discriminative ability in predicting the binary response “at risk for xerostomia at 12 months”.
Keywords :
cancer; dosimetry; electronic health records; pattern classification; probability; radiation therapy; regression analysis; sensitivity analysis; tumours; IMRT treatments; Portuguese Institute-of-Oncology-of-Coimbra; binary response; cancer treatments; cancerous cells; classifier; clinical data; dose absorption; electronic health information system; head-and-neck cancer treatment; healthy tissues; high degree-of-conformity; intensity modulated radiation therapy; logistic regression approach; medical prescription; patient dependence; radiotherapy treatment; time 12 month; trial-and-error procedure; xerostomia; Biomedical applications of radiation; Cancer; Classification algorithms; Computational modeling; Logistics; Predictive models; Tumors; AUC; IMRT; ROC curves; Radiotherapy; logistic regression predictors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Bioinformatics and Biomedicine (BIBM), 2014 IEEE International Conference on
Conference_Location :
Belfast
Type :
conf
DOI :
10.1109/BIBM.2014.6999271
Filename :
6999271
Link To Document :
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