• 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