A Bayesian network approach for modeling local failure in lung cancer

Jung Hun Oh, Jeffrey Craft, Rawan Al Lozi, Manushka Vaidya, Yifan Meng, Joseph O. Deasy, Jeffrey D. Bradley, Issam El Naqa

Research output: Contribution to journalArticle

27 Scopus citations

Abstract

Locally advanced non-small cell lung cancer (NSCLC) patients suffer from a high local failure rate following radiotherapy. Despite many efforts to develop new dose-volume models for early detection of tumor local failure, there was no reported significant improvement in their application prospectively. Based on recent studies of biomarker proteins' role in hypoxia and inflammation in predicting tumor 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 local failure in lung cancer. The proposed approach was tested using two different datasets of locally advanced NSCLC patients 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 patients 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 patients 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 NSCLC patients.

Original languageEnglish
Pages (from-to)1635-1651
Number of pages17
JournalPhysics in medicine and biology
Volume56
Issue number6
DOIs
StatePublished - Mar 21 2011

Fingerprint Dive into the research topics of 'A Bayesian network approach for modeling local failure in lung cancer'. Together they form a unique fingerprint.

  • Cite this

    Oh, J. H., Craft, J., Al Lozi, R., Vaidya, M., Meng, Y., Deasy, J. O., Bradley, J. D., & Naqa, I. E. (2011). A Bayesian network approach for modeling local failure in lung cancer. Physics in medicine and biology, 56(6), 1635-1651. https://doi.org/10.1088/0031-9155/56/6/008