Analysis of related factors of radiation pneumonia caused by precise radiotherapy of esophageal cancer based on random forest algorithm - Na Li; Peng Luo; Chunyang Li; Yanyan Hong; Mingjun Zhang; Zhendong Chen (Mathematical Biosciences and Engineering, 2021)
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.