Accurate Models for Predicting Radiation-Induced Injury

Summary

Principal Investigator: Shiva Das
Affiliation: Duke University Medical Center
Country: USA
Abstract: Radiotherapy-induced cardiac and lung injury is a serious problem that is growing more critical with continuing efforts to escalate radiation dose to thoracic tumors. It is imperative that the mathematical tools be available to accurately predict such risk prior to treatment. Accurate prediction can help to mitigate the risk by appropriately altering the radiotherapy treatment plan. Inaccurate prediction, on the other hand, could have dangerous consequences by underestimating the probability of injury. Currently, popularly used parametric models can be inaccurate predictors due to certain assumptions. Parametric models assume that the mathematical behavior underlying radiation-response is dependent only on dose and is the same for all organs and injury endpoints. The hypothesis driving this project is that novel non-parametric models can more accurately predict radiotherapy-induced cardiac and lung injury than existing, popularly used, parametric alternatives. Injury data for this project will consist of dose data, SPECT-based functional maps, and relevant patient characteristics, from Duke University and the Netherlands Cancer Institute (NKI). Specific aims supporting the hypothesis are: (1) Optimally fit commonly used parametric models to the input injury data. The model parameters will be fitted using maximum likelihood estimation, and parameter uncertainty will be estimated using Markov chain Monte Carlo simulation. (2) Develop novel non-parametric predictive models. These are powerful ensemble models that will combine the strengths of three component methods: linear discriminant, trees, and neural constructs. Construction of the component models will use novel techniques incorporating receiver operating characteristics (ROC) analysis to explicitly safeguard against overfitting to the input data. (3) Assess the robustness of the non-parametric models and compare their predictive accuracy to parametric models. A more robust model is less sensitive to the input dataset. Robustness assessment and model comparison will use novel techniques incorporating ROC analysis. ROC analysis will also be used to assess the predictive importance of using SPECT functional inputs. It is anticipated that the non-parametric models developed here will be significantly superior to existing parametric alternatives. They will be made publicly available via the Internet. The overall impact of this work will be to facilitate significant reduction in thoracic radiotherapy-induced injury via accurate prediction.
Funding Period: 2006-05-10 - 2010-04-30
more information: NIH RePORT

Top Publications

  1. pmc A genetic algorithm for variable selection in logistic regression analysis of radiotherapy treatment outcomes
    Olivier Gayou
    Department of Radiation Oncology, Allegheny General Hospital, Pittsburgh, Pennsylvania 15212, USA
    Med Phys 35:5426-33. 2008
  2. pmc Predicting lung radiotherapy-induced pneumonitis using a model combining parametric Lyman probit with nonparametric decision trees
    Shiva K Das
    Department of Radiation Oncology, Duke University Medical Center, Durham, NC 27710, USA
    Int J Radiat Oncol Biol Phys 68:1212-21. 2007
  3. pmc A neural network model to predict lung radiation-induced pneumonitis
    Shifeng Chen
    Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA
    Med Phys 34:3420-7. 2007
  4. pmc Investigation of the support vector machine algorithm to predict lung radiation-induced pneumonitis
    Shifeng Chen
    Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA
    Med Phys 34:3808-14. 2007
  5. pmc Using patient data similarities to predict radiation pneumonitis via a self-organizing map
    Shifeng Chen
    Department of Radiation Oncology, Duke University Medical Center, Durham, NC 27710, USA
    Phys Med Biol 53:203-16. 2008
  6. pmc Combining multiple models to generate consensus: application to radiation-induced pneumonitis prediction
    Shiva K Das
    Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA
    Med Phys 35:5098-109. 2008
  7. doi Radiation-induced reductions in regional lung perfusion: 0.1-12 year data from a prospective clinical study
    Junan Zhang
    Department of Radiation Oncology, Duke University Medical Center, Durham, NC 27710, USA
    Int J Radiat Oncol Biol Phys 76:425-32. 2010

Detail Information

Publications7

  1. pmc A genetic algorithm for variable selection in logistic regression analysis of radiotherapy treatment outcomes
    Olivier Gayou
    Department of Radiation Oncology, Allegheny General Hospital, Pittsburgh, Pennsylvania 15212, USA
    Med Phys 35:5426-33. 2008
    ..In conclusion, genetic algorithms provide a reliable and fast way to select significant factors in logistic regression analysis of large clinical studies...
  2. pmc Predicting lung radiotherapy-induced pneumonitis using a model combining parametric Lyman probit with nonparametric decision trees
    Shiva K Das
    Department of Radiation Oncology, Duke University Medical Center, Durham, NC 27710, USA
    Int J Radiat Oncol Biol Phys 68:1212-21. 2007
    ..To develop and test a model to predict for lung radiation-induced Grade 2+ pneumonitis...
  3. pmc A neural network model to predict lung radiation-induced pneumonitis
    Shifeng Chen
    Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA
    Med Phys 34:3420-7. 2007
    ..05). The network for prospective testing is publicly available via internet access...
  4. pmc Investigation of the support vector machine algorithm to predict lung radiation-induced pneumonitis
    Shifeng Chen
    Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA
    Med Phys 34:3808-14. 2007
    ..The model SVM(all) is publicly available via internet access...
  5. pmc Using patient data similarities to predict radiation pneumonitis via a self-organizing map
    Shifeng Chen
    Department of Radiation Oncology, Duke University Medical Center, Durham, NC 27710, USA
    Phys Med Biol 53:203-16. 2008
    ..The SOM model developed here may not be extrapolated to treatment techniques outside that used in our database, such as several-field lung intensity modulated radiation therapy or gated radiation therapy...
  6. pmc Combining multiple models to generate consensus: application to radiation-induced pneumonitis prediction
    Shiva K Das
    Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA
    Med Phys 35:5098-109. 2008
    ..2, lung volume receiving >20-30 Gy; (4) female sex; and (5) squamous cell histology. To facilitate ease of interpretation and prospective use, the fused outcome results for the patients were fitted to a logistic probability function...
  7. doi Radiation-induced reductions in regional lung perfusion: 0.1-12 year data from a prospective clinical study
    Junan Zhang
    Department of Radiation Oncology, Duke University Medical Center, Durham, NC 27710, USA
    Int J Radiat Oncol Biol Phys 76:425-32. 2010
    ..To assess the time and regional dependence of radiation therapy (RT)-induced reductions in regional lung perfusion 0.1-12 years post-RT, as measured by single photon emission computed tomography (SPECT) lung perfusion...