The precise
radiotherapy of
esophageal cancer may cause different degrees of radiation damage for lung tissues and cause radioactive
pneumonia. However, the occurrence of radioactive
pneumonia is related to many factors. To further clarify the correlation between the occurrence of radioactive
pneumonia and related factors, a random forest model was used to build a risk prediction model for patients with
esophageal cancer undergoing
radiotherapy. In this study, we retrospectively reviewed 118 patients with
esophageal cancer confirmed by pathology in our hospital. The health characteristics and related parameters of all patients were analyzed, and the predictive effect of
radiation pneumonia was discussed using the random forest algorithm.
After treatment, 71 patients developed radioactive
pneumonia (60.17%). In univariate analyses, age, planning target volume length, Karnofsky performance score (KPS),
pulmonary emphysema, with or without
chemotherapy, and the ratio of planning target volume to planning gross
tumor volume (PTV/PGTV) in mediastinum were significantly associated with radioactive
pneumonia (P < 0.05 for each comparison). Multivariate analysis revealed that with or without
pulmonary emphysema (OR = 7.491, P = 0.001), PTV/PGTV (OR = 0.205, P = 0.007), and KPS (OR = 0.251, P = 0.011) were independent predictors for
radiation pneumonia. The results concluded that the analysis of
radiation pneumonia-related factors based on the random forest algorithm could build a mathematical prediction model for the easily obtained data. This algorithm also could effectively analyze the risk factors of
radiation pneumonia and formulate the appropriate treatment plan for
esophageal cancer.