TY - JOUR
T1 - Unraveling biophysical interactions of radiation pneumonitis in non-small-cell lung cancer via Bayesian network analysis
AU - Luo, Yi
AU - El Naqa, Issam
AU - McShan, Daniel L.
AU - Ray, Dipankar
AU - Lohse, Ines
AU - Matuszak, Martha M.
AU - Owen, Dawn
AU - Jolly, Shruti
AU - Lawrence, Theodore S.
AU - Kong, Feng Ming (Spring)
AU - Ten Haken, Randall K.
N1 - Funding Information:
This work was supported by the National Institutes of Health [grant numbers P01 CA059827, R01 CA142840]. The authors wish to thank Paul Stanton, Nan Bi, MD, PhD, and Weili Wang MD, PhD for their work in processing the cytokine, miRNA and SNP data. This work was presented in part at ICTR-PHE 2016, 15-19 February 2016, CICG, Geneva, Switzerland.
Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2017/4/1
Y1 - 2017/4/1
N2 - 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.
AB - 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.
KW - Bayesian network analysis
KW - Biophysical interactions
KW - Lung cancer
KW - Radiation pneumonitis
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U2 - 10.1016/j.radonc.2017.02.004
DO - 10.1016/j.radonc.2017.02.004
M3 - Article
C2 - 28237401
AN - SCOPUS:85010761422
SN - 0167-8140
VL - 123
SP - 85
EP - 92
JO - Radiotherapy and Oncology
JF - Radiotherapy and Oncology
IS - 1
ER -