Using LASSO regression based SVM classification to improve the predictive performance of radiation-induced pneumonitis complication in breast cancer

Radiation-induced pneumonitis (RP) is a common complication in breast cancer patients after radiation therapy (RT). In the present study, least absolute shrinkage and selection operator (LASSO) and five classification algorithms were used to improve both the predictive ability of RP and the quality of patients' daily life. A total of 106 breast cancer patients were enrolled in this study. All of the patients were treated with volumetric modulated arc therapy (VMAT). A total of 19 risk factors were included in this study. The present study found that the area under receiver operating characteristics curve (AUC) and accuracy (ACC) of LASSO selected factors (F-LASSO) for each of the five classification algorithms were generally higher than those of all selected factors (F-all) and dose selected factors (F-dose). We propose to use LASSO with support vector machine (SVM) to assess the risk of complications, to improve the predictive ability for breast cancer patients with complications after RT, and to reduce the cost of assessing the risk of complications.