Precise prediction of the radiation pneumonitis in lung cancer: an explorative preliminary mathematical model using genotype information

Journal of Cancer
Lehui DuBaolin Qu

Abstract

Purpose: Radiation pneumonitis (RP) is the most significant dose-limiting toxicity and is one major obstacle for lung cancer radiotherapy. Grade ≥2 RP usually needs clinical interventions and serve RP could be life threatening. Clinically, tissue response could be strikingly different even two similar patients after identical radiotherapy. Previous methods for the RP prediction can hardly distinguish substantial variations among individuals. Reliable predictive factors or methods emphasizing the individual differences are strongly desired by clinical radiation oncologists. The purpose of this study is to develop an approach for the personalized RP risk prediction. Experimental Design: One hundred eighteen lung cancer patients who received radiotherapy were enrolled. Seven hundred thousand single-nucleotide polymorphism (SNP) sites were assessed 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's phenotypes and clinical interventional parameters were separately assessed by statistic test. Based on the results of the aforementioned analysis, a multiple linear regression model named Radiation Pneumo...Continue Reading

Citations

Jul 30, 2020·Journal of Personalized Medicine·Mariola Śliwińska-MossońIwona Bil-Lula

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Methods Mentioned

BETA
X-ray
genotyping
chip

Software Mentioned

Pinnacle
R
glmnet
PLINK

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