Unraveling biophysical interactions of radiation pneumonitis in non-small-cell lung cancer via Bayesian network analysis

Abstract

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.

Radiation-induced lung toxicity in non-small-cell lung cancer: Understanding the interactions of clinical factors and cytokines with the dose-toxicity relationship

“…Another approach is Bayesian network analysis, which explores probabilistic relationships among multiple variables by representing their interactions and dependences on a directed acyclic graph [42]. Recently, Bayesian network analysis has shown promise as a method to estimate the risk of radiation pneumonitis (RP) [43, 44]. …”

A multiobjective Bayesian networks approach for joint prediction of tumor local control and radiation pneumonitis in nonsmall‐cell lung cancer (NSCLC) for response‐adapted radiotherapy

“…Sevenfold cross‐validation was conducted to evaluate the pretreatment SO‐BN, and its AUC value is 0.81 with a 95% confidence interval (CI) of 0.74–0.88 based on 2000 stratified bootstrap replicates. Figure (b) shows a stable pretreatment SO‐BN with an edge strength ≥ 0.65 for RP2 prediction from our previous work . Based on sevenfold cross‐validation, the AUC value is 0.82 with a 95% CI of 0.72–0.87.…”

“…, where the inner family and the extended family of the radiation outcomes were obtained from the first and the second layers of their extended MB neighborhoods. An example to illustrate the extended MBs of RP2 can be found in our previous work …”

Radiogenomics and radiotherapy response modeling

“…In an effort to overcome the black box stigma of generic machine learning algorithms, approaches incorporating more engineering systems-like methods based on graphical techniques (e.g., 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). In particular, a BN provides a probabilistic graphical representation of the relationships between the variables represented as nodes in a directed acyclic graph (DAG), which encodes the presence and direction of relationship influence among the variables themselves and the clinical endpoint of interest.…”

“…The second row shows during-treatment BN modeling of RP. (d) Markov blanket, (e) BN structure, and (f) ROC analysis on cross-validation (Luo et al , 2017). …”