Abstract
BACKGROUND: Colon cancer remains a major cause of cancer-related deaths worldwide, with recurrence post-surgery, posing a significant challenge. Accurate lymph node (LN) staging is critical for prognosis and treatment decisions, but traditional systems, such as the AJCC TNM, often fail to predict recurrence. This study compares the prognostic performance of three LN staging systems Lymph Node Ratio (LNR), Log Odds of Metastatic Lymph Nodes (LODDS), and pN in colon cancer. METHODS: We retrospectively analyzed data from 812 colon cancer patients who underwent radical surgery at two tertiary hospitals (2010-2019). LNR, LODDS, and pN were calculated, and their ability to predict postoperative recurrence was assessed using C-index, AIC, BIC, and ROC curves. Machine learning models (LASSO, Random Forest, XGBoost) identified the most predictive staging system. A nomogram was developed integrating the best staging system with clinical factors to predict postoperative recurrence. RESULTS: The study identified LNR as the most predictive staging system for colon cancer. The nomogram based on LNR, along with other variables such as T stage and tumor grade, demonstrated superior predictive performance compared to individual staging systems. In the training cohort, the nomogram achieved an AUC of 0.791 at 1 year, 0.815 at 3 years, and 0.789 at 5 years. The C-index for the nomogram was 0.788, higher than that of LNR (C-index = 0.694) and tumor stage (C-index = 0.665). The nomogram successfully stratified patients into high- and low-risk groups, with higher risk scores correlating with poorer survival outcomes. The validation cohort confirmed the robustness of the model, showing that patients with lower risk scores had better prognoses. CONCLUSIONS: LNR is an effective predictor of recurrence and prognosis in colon cancer. The nomogram developed from LNR and other clinical factors offers superior prognostication and can aid in personalized treatment strategies.