Radiation pneumonitis (RP) is a radiotherapy dose-limiting toxicity for non-small-cell lung cancer. Prior studies have proposed clinical and dosimetric factors related to RP. This review paper describes about a radiomics-based predictive model for RP after lung cancer stereotactic body radiation therapy (SBRT) on pretreatment planning computed tomography (CT) images. For the RP prediction models, 275 non-small-cell lung cancer patients consisted of 245 training (22 with grade ≥2 RP) and 30 test cases (8 with grade ≥2 RP) were selected. A total of 486 radiomic features were calculated to quantify texture patterns within lung volumes. Ten subsets consisting of all 22 RP cases and 22 or 23 randomly selected non-RP cases were created from the imbalanced dataset of 245 training patients. For each subset, signatures were constructed, and predictive models were built using the least absolute shrinkage and selection operator (LASSO) logistic regression. An ensemble averaging model was built by averaging the RP probabilities of the 10 models. The best model areas under the receiver operating characteristic curves (AUCs) calculated on the training and test cohorts were 0.871 and 0.756, respectively. The radiomic features calculated on pretreatment planning CT images could be predictive imaging biomarkers for RP after lung cancer SBRT.