Influencing factors and prediction methods of radiotherapy and chemotherapy in patients with lung cancer based on logistic regression analysis

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.

Relationship between self-psychological adjustment and post-traumatic growth in patients with lung cancer undergoing chemotherapy: a cross-sectional study

“…According to ecosystem theory, social and personal factors impact selfpsychological adjustment. 63 Residence in a city and time to first diagnosis ≥6 months and <1 year were important social and personal factors; thus, both became unique demographic characteristics of patients in the high-PCER group, which is similar to the findings of Liu et al 64 Patients with lung cancer undergoing chemotherapy living in urban areas were categorised into the high-PCER group for three reasons. First, they have a better economic status and lower financial burden of treatment, which is favourable for PCER modelling.…”

Using Logistic Regression and Support Vector Classification to Predict Cancer

“…Additionally, the class imbalance present in the dataset could introduce biases that might affect the models' performance in real-world scenarios. [6] Second, the interpretability of the models is an essential consideration. While logistic regression is inherently interpretable, the SVC model, especially with non-linear kernels, can become a 'black box', making clinical adoption challenging.…”