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. ? ? ?

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
Project #
Application #
Study Section
Radiation Therapeutics and Biology Study Section (RTB)
Program Officer
Deye, James
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Duke University
Schools of Medicine
United States
Zip Code
Kelsey, Chris R; Jackson, Isabel L; Langdon, Scott et al. (2013) Analysis of single nucleotide polymorphisms and radiation sensitivity of the lung assessed with an objective radiologic endpoin. Clin Lung Cancer 14:267-74
Kelsey, Chris R; Jackson, Lauren; Langdon, Scott et al. (2012) A polymorphism within the promoter of the TGF?1 gene is associated with radiation sensitivity using an objective radiologic endpoint. Int J Radiat Oncol Biol Phys 82:e247-55
Zhang, Junan; Ma, Jinli; Zhou, Sumin et al. (2010) Radiation-induced reductions in regional lung perfusion: 0.1-12 year data from a prospective clinical study. Int J Radiat Oncol Biol Phys 76:425-32
Gayou, Olivier; Das, Shiva K; Zhou, Su-Min et al. (2008) A genetic algorithm for variable selection in logistic regression analysis of radiotherapy treatment outcomes. Med Phys 35:5426-33
Chen, Shifeng; Zhou, Sumin; Yin, Fang-Fang et al. (2008) Using patient data similarities to predict radiation pneumonitis via a self-organizing map. Phys Med Biol 53:203-16
Das, Shiva K; Chen, Shifeng; Deasy, Joseph O et al. (2008) Combining multiple models to generate consensus: application to radiation-induced pneumonitis prediction. Med Phys 35:5098-109
Chen, Shifeng; Zhou, Sumin; Yin, Fang-Fang et al. (2007) Investigation of the support vector machine algorithm to predict lung radiation-induced pneumonitis. Med Phys 34:3808-14
Chen, Shifeng; Zhou, Sumin; Zhang, Junan et al. (2007) A neural network model to predict lung radiation-induced pneumonitis. Med Phys 34:3420-7
Das, Shiva K; Zhou, Sumin; Zhang, Junan et al. (2007) Predicting lung radiotherapy-induced pneumonitis using a model combining parametric Lyman probit with nonparametric decision trees. Int J Radiat Oncol Biol Phys 68:1212-21