Abstract
OBJECTIVE: To develop an individualized prediction model for myelosuppression risk in lung cancer patients undergoing platinum-based doublet chemotherapy and validate its predictive efficacy. METHODS: A retrospective analysis was conducted on the clinical data of 584 lung cancer patients who received platinum-based doublet chemotherapy at The Affiliated Hospital of Qingdao University between January 2016 and December 2020. Patients were randomly assigned to a training cohort (n=391) and a validation cohort (n=193). Myelosuppression occurred in 280 (71.6%) patients in the training cohort and 132 (68.4%) in the validation cohort. Univariate analysis and LASSO regression were used to identify independent risk factors for myelosuppression. Prediction models were developed using Support Vector Machine (SVM), Random Forest, Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (Adaboost). Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and Decision Curve Analysis (DCA). The SHAP algorithm was employed to evaluate feature importance, and a nomogram was developed for individual risk prediction. RESULTS: LASSO regression identified 10 independent risk factors for myelosuppression: age, body mass index (BMI), white blood cell count, neutrophil count, platelet count, total protein, gender, treatment regimen, targeted therapy, and first chemotherapy cycle. In the training cohort, the XGBoost model exhibited the best performance, with an area under the curve (AUC) of 0.855 (95% CI: 0.813-0.897), while the AUC in the validation cohort was 0.793. SHAP analysis identified white blood cell count, platelet count, neutrophil count, BMI, and age as the most influential predictors. The SHAP analysis based on the XGBoost model demonstrated substantial value. CONCLUSION: This study successfully developed an individualized prediction model for myelosuppression risk in lung cancer patients following platinum-based doublet chemotherapy, with the XGBoost model achieving high predictive accuracy and clinical utility. The model provides a valuable tool for guiding precision medicine.