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Purpose:  Radiation pneumonitis (RP) frequently occurs during a treatment course of chest radiotherapy, which significantly reduces the clinical outcome and efficacy of radiotherapy. The ability to easily predict RP before radiotherapy would allow this disease to be avoided.   Methods and Materials:  This study recruited 48 lung cancer patients requiring chest radiotherapy. For each participant, RNA sequencing (RNA-Seq) was performed on a peripheral blood sample before radiotherapy. The RNA-Seq data was then integrated into a genome-scale flux analysis to develop an RP scoring system for predicting the probability of occurrence of RP. Meanwhile, the clinical information and radiation dosimetric parameters of this cohort were collected for analysis of any statistical associations between these parameters and RP. A non-parametric rank sum test showed no significant difference between the predicted results from the RP score system and the clinically observed occurrence of RP in this cohort.   Results:  The results of the univariant analysis suggested that the tumor stage, exposure dose, and bilateral lung dose of V5 and V20 were significantly associated with the occurrence of RP. The results of the multivariant analysis suggested that the exposure doses of V5 and V20 were independent risk factors associated with RP and a level of RP ≥ 2, respectively. Thus, our results indicate that our RP scoring system could be applied to accurately predict the risk of RP before radiotherapy because the scores were highly consistent with the clinically observed occurrence of RP.   Conclusion:  Compared with the standard statistical methods, this genome-scale flux-based scoring system is more accurate, straightforward, and economical, and could therefore be of great significance when making clinical decisions for chest radiotherapy.

Prediction of Radiation Pneumonitis Using Genome-Scale Flux Analysis of RNA-Seq Derived From Peripheral Blood