A Bayesian network approach for modeling XXXX .

@entity1 @entity32 @entity5 @entity1149 @entity2 @entity519
Locally advanced @entity1149 ( @entity1149 ) @entity1 suffer from a high local failure rate following radiotherapy. Despite many efforts to develop new dose-volume models for early detection of @entity5 local failure, there was no reported significant improvement in their application prospectively. Based on recent studies of biomarker proteins' role in @entity519 and @entity32 in predicting @entity5 response to radiotherapy, we hypothesize that combining physical and biological factors with a suitable framework could improve the overall prediction. To test this hypothesis, we propose a graphical Bayesian network framework for predicting @entity2 . The proposed approach was tested using two different datasets of locally advanced @entity1149 @entity1 treated with radiotherapy. The first dataset was collected retrospectively, which comprises clinical and dosimetric variables only. The second dataset was collected prospectively in which in addition to clinical and dosimetric information, blood was drawn from the @entity1 at various time points to extract candidate biomarkers as well. Our preliminary results show that the proposed method can be used as an efficient method to develop predictive models of local failure in these @entity1 and to interpret relationships among the different variables in the models. We also demonstrate the potential use of heterogeneous physical and biological variables to improve the model prediction. With the first dataset, we achieved better performance compared with competing Bayesian-based classifiers. With the second dataset, the combined model had a slightly higher performance compared to individual physical and biological models, with the biological variables making the largest contribution. Our preliminary results highlight the potential of the proposed integrated approach for predicting post-radiotherapy local failure in @entity1149 @entity1 .
