Unraveling Biophysical Interactions of Radiation Pneumonitis in Non-Small-Cell Lung Cancer via Bayesian Network Analysis

By Yi Luo, I. E. El Naqa, D. McShan, D. Ray, I. Lohse, M. Matuszak, D. Owen, S. Jolly, T. Lawrence, F. Kong and 1 more. 2017

57 citations recorded by the source corpus.

Background In non-small-cell lung cancer radiotherapy, radiation pneumonitis ≥ grade 2 (RP2) depends on patients' dosimetric, clinical, biological and genomic characteristics. Methods We developed a Bayesian network (BN) approach to explore its potential for interpreting biophysical signaling pathways influencing RP2 from a heterogeneous dataset including single nucleotide polymorphisms, micro RNAs, cytokines, clinical data, and radiation treatment plans before and during the course of radiotherapy. Model building utilized 79 patients (21 with RP2) with complete data, and model testing used 50 additional patients with incomplete data. A developed large-scale Markov blanket approach selected relevant predictors. Resampling by k-fold cross-validation determined the optimal BN structure. Area under the receiver-operating characteristics curve (AUC) measured performance. Results Pre- and during-treatment BNs identified biophysical signaling pathways from the patients' relevant variables to RP2 risk. Internal cross-validation for the pre-BN yielded an AUC=0.82 which improved to 0.87 by incorporating during treatment changes. In the testing dataset, the pre- and during AUCs were 0.78 and 0.82, respectively. Conclusions Our developed BN approach successfully handled a high number of heterogeneous variables in a small dataset, demonstrating potential for unraveling relevant biophysical features that could enhance prediction of RP2, although the current observations would require further independent validation.

Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Pulmonary and Respiratory Medicine

I. E. El Naqa

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Deep Reinforcement Learning for Automated Radiation Adaptation in Lung Cancer (2017, methodology): “Hence, to develop the transition probabilities, we selected 9 variables related to cytokines, SNPs, miRNA and PET radiomics features in order to predict LC and RP2 (see TABLE I) by using the Bayesian network approach developed in [13] to represent the RAE states.”
Radiogenomics and Radiotherapy Response Modeling (2017, methodology): “, decision trees and Bayesian networks [BNs]) have witnessed increased used in outcome modeling of cancer (Oh et al., 2011a; Lee et al., 2015; Jayasurya et al., 2010; Luo et al., 2017).”