Abstract
During the progression of lung cancer, cancer pain is a common complication. Currently, there are no accurate tools or methods to predict the occurrence of cancer pain in lung cancer. Our study aims to construct a predictive model for lung cancer pain to assist in the early diagnosis of cancer pain and improve prognosis. We retrospectively collected clinical data from 300 lung cancer patients between March 2013 and March 2023. First, we compared the clinical data of the groups with and without cancer pain. Significant factors were further screened using random forest analysis (IncMSE% > 2) to identify those with significant differences. Finally, these factors were incorporated into a multifactorial logistic regression model to develop a predictive model for lung cancer pain. The predictive accuracy and performance of the model were assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) analysis. Our study collected data from 300 lung cancer patients, including 100 in the pain-free group and 200 in the pain group. Subsequently, we conducted univariate analysis on 22 factors and selected statistically significant factors using random forest methods. Ultimately, lymphocytes(LYM) percentage, bone metastasis, tumor necrosis factor alpha (TNFα), and interleukin-6 (IL6) were identified as key factors. These 4 factors were included in a multivariate logistic regression analysis to construct a predictive model for lung cancer pain. The model demonstrated good predictive ability, with an area under the curve (AUC) of 0.852 (95% CI: 0.806-0.899). The calibration curve indicated that the model has good accuracy in predicting the risk of lung cancer pain. DCA further emphasized the model's high accuracy. The model was finally validated using 5-fold cross-validation. We developed a reliable predictive model for cancer pain in lung cancer. This can provide a theoretical basis for future large-sample, multi-center studies and may also assist in the early prevention and intervention of cancer pain in lung cancer.