Influencing factors and prediction methods of radiotherapy and chemotherapy in patients with lung cancer based on logistic regression analysis - Yuxia Liu; Chang Xu; Chengyan Xing; Mingwei Chen (Scientific Reports, 2022)
Abstract Logistic regression analysis has widespread applications in clinical disease diagnosis, but it has not yet been applied to assess the acceptance of radiotherapy and chemotherapy in patients with lung cancer. A prediction model was established to investigate the influencing factors of radiotherapy and chemotherapy in lung cancer patients in order to provide useful information for clinicians to develop targeted and effective treatment. A sample was admitted of lung cancer patients to Binzhou Medical University Hospital stays from January 2020 to June 2021. After investigating doctors, nurses, patients, managers and conducting expert demonstration, the questionnaire was formed. The questionnaire was filled out by the patient or the patient's family members. The factors in the questionnaire data of patients accepting and not accepting radiotherapy and chemotherapy were compared for univariate analysis, and the significantly different single factor were analyzed by multifactor logistic regression analysis, explored the influencing factors of radiotherapy and chemotherapy in lung cancer patients established a predictive model and drew the receiver operating characteristic curve (ROC curve). The factors of two groups had statistically significant differences or no statistically significant differences. After multifactor logistic regression analysis was conducted, own personality, self-care ability, disease course classification, own attitude towards disease treatment, and family attitude towards disease treatment were included in the influencing factors of radiotherapy and chemotherapy in patients with lung cancer. Then, a predictive model was established. The area under the ROC curve of the predicted model was 0.973, the 95% confidence interval was 0.952–0.995, the optimal critical value was 0.832, the sensitivity was 91.84%, the specificity was 89.09%, and the accuracy was 90.85%. Based on logistic regression analysis, the prediction model could predict the extent of accepting radiotherapy and chemotherapy in patients with lung cancer. Understanding the factors related to patients with lung cancer accepting radiotherapy and chemotherapy could provide useful information for the targeted and effective treatment by clinicians.