Risk Prediction of Myelosuppression Following First-line Chemotherapy in Colorectal Cancer

Background: Colorectal cancer (CRC) is a leading cause of cancer-related deaths, with over 1.9 million new cases and 904,000 deaths in 2022. Chemotherapy is a primary treatment for CRC but often leads to myelosuppression, significantly affecting treatment efficacy and patient outcomes. Predictive tools for chemotherapy-induced myelosuppression are currently lacking. Methods: This retrospective study analyzed 855 CRC patients from Guang'anmen Hospital who received first-line chemotherapy (CapeOx, FOLFOX, FOLFIRI) between April 2020 and July 2024. Patients were divided into training (684) and validation (171) groups. Univariate analysis, LASSO regression, and multivariable logistic regression identified risk factors for myelosuppression, and a predictive nomogram was developed and validated using ROC curves, calibration curves, and decision curve analysis. Propensity score matching (PSM) was employed to minimize baseline differences between groups, followed by multivariate logistic regression analysis on the post-PSM data. Results: The incidence of myelosuppression was similar in both groups (33.04% vs. 32.16%). Significant predictors included age, smoking, diabetes, BMI, tumor location, lung metastasis, albumin (ALB) levels, and carcinoembryonic antigen (CEA) levels. The nomogram demonstrated good predictive performance with AUC values of 0.78 and 0.80 for the training and validation groups, respectively, showing consistent and clinically useful predictions. PSM further validated the robustness of the model, confirming BMI as a consistently significant predictor of myelosuppression. Conclusions: The study identified key risk factors for chemotherapy-induced myelosuppression in CRC patients and developed a nomogram for prediction. This tool can help clinicians assess risk and guide treatment decisions. Limitations include potential selection bias and the need for external validation in diverse populations. Future studies should further refine and validate this predictive model.