Predicting local failure in lung cancer using Bayesian networks

Despite various efforts to develop new predictive models for early detection of tumor local failure in locally advanced non-small cell lung cancer (NSCLC), many patients still suffer from a high local failure rate after radiotherapy. Based on recent studies of biomarker proteins' role in predicting tumor response following radiotherapy, we hypothesize that incorporation of physical and biological factors with a suitable framework could improve the overall prediction. To this end, we propose a graphical Bayesian network framework for predicting local failure in lung cancer. The proposed approach was tested using a dataset of locally advanced NSCLC patients treated with radiotherapy. This dataset was collected prospectively, which consisted of physical variables and blood-based biomarkers. Our experimental results demonstrate 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. The combined model of physical and biological factors outperformed individual physical and biological models, achieving an accuracy (acc) of 87.78%, Matthew's correlation coefficient (r) of 0.74, and Spearman's rank correlation coefficient (rs) of 0.75 on leave-one-out cross-validation analysis.