Development and external validation of a predictive scoring system associated with metastasis of T1‐2 colorectal tumors to lymph nodes

BACKGROUND: It is critical for determining the optimum therapeutic solutions for T1‐2 colorectal cancer (CRC) to accurately predict lymph node metastasis (LNM) status. The purpose of the present study is to establish and verify a nomogram to predict LNM status in T1‐2 CRCs. METHODS: A total of 16 600 T1‐2 CRC patients were enrolled and classified into the training, internal validation, and external validation cohorts. The independent predictive parameters were determined by univariate and multivariate analyses to develop a nomogram to predict the probability of LNM status. The calibration curve, the area under the receiver operating characteristic curve (AUROC), and decision curve analysis (DCA) were used to evaluate the performance of the nomogram, and an external verification cohort was to verify the applicability of the nomogram. RESULTS: Seven independent predictors of LNM in T1‐2 CRC were identified in the multivariable analysis, including age, tumor site, tumor grade, perineural invasion, preoperative carcinoembryonic antigen, clinical assessment of LNM, and T stage. A nomogram incorporating the seven predictors was constructed. The nomogram yielded good discrimination and calibration, with AUROCs of 0.72 (95% confidence interval [CI]: 0.70‐0.75), 0.70 (95% CI: 0.67‐0.74), and 0.74 (95% CI: 0.71‐0.79) in the training, internal validation, and external validation cohorts, respectively. DCA showed that the predictive scoring system had high clinical application value. CONCLUSIONS: We proposed a novel predictive model for LNM in T1‐2 CRC patients to assist physicians in making treatment decisions. The nomogram is advantageous for tailoring therapy in T1‐2 CRC patients.