Precise Prediction Of The Radiation Pneumonitis With Rpi: An Explorative Preliminary Mathematical Model Using Genotype Information

Radiation pneumonitis (RP) is the most significant dose-limiting toxicity and is one major obstacle for the radiotherapy of lung cancer. Reliable predictive factors or methods are strongly demanded by radiation oncologists. The purpose of this study is by determining the effectiveness of both genetic and non-genetic factors on their impact on the development of RP, to develop a clinically practicable approach for the risk assessment of RP. One hundred eighteen lung cancer patients who received radiotherapy were enrolled. Seven hundred thousand single-nucleotide polymorphism (SNP) sites of each patient were tested via Generalized Linear Models via Lasso and Elastic-Net Regularization (GLMNET) to determine their synergistic effects on the RP risk prediction. Non-genetic factors including patient characteristics and dosimetric parameters were separately assessed by statistic test for their association with the RP risk. Based on the results of the aforementioned analysis, a multiple linear regression model named Radiation Pneumonitis Index (RPI) was built, for the assessment of RP risk. No statistically significant association were found between the RP risk and any of the non-genetic factors. Twenty five effective SNPs for predicting the high-grade RP risk were discovered and their coefficients of the synergistic effect were determined. An RPI score defined only by the information about these 25 SNPs can successfully distinguish the high-grade RP population with 100% specificity and 97.8% accuracy. Non-genetic factors including important dosimetric parameters may not play dominant roles in the development of RP. Genotype information alone can effectively predict the risk of RP. The combination of genetics and mathematical algorithms can be a new direction for radiotherapy in the field of precision medicine.