The clinical value of a nomogram constructed from CEA, CA199, PT, FIB, tumor differentiation and TNM stage in colorectal cancer

BACKGROUND: The accurate Tumor-Node-Metastasis (TNM) staging of colorectal cancer (CRC) is of great guiding significance for the judgment of tumor progression and prognosis, and the formulation of treatment strategies. OBJECTIVE: The aim of this study was to construct a recurrence risk scoring (RRS) system and prognostic prediction model to improve the accuracy of staging, prognosis prediction, and clinical decision making in resectable CRC. METHODS: CRC patients who underwent radical resection were retrospectively enrolled into study. Multivariable Cox regression model was applied to screen for independent prognostic factors. The RRS system is composed of independent prognostic factors which was awarded 1point each. A prognostic model composed of RRS and TNM staging system (RRS-TNM model) was applied to predict postoperative recurrence. RESULTS: TNM stage, tumor differentiation, preoperative elevated Carcinoembryonic Antigen, Carbohydrate Antigen 199, Prothrombin Time and Fibrinogen were the independent prognostic biomarkers. 173 of 540 patients had recurrence. The 5-year cumulative recurrence rate (5-y CRR) and disease-free survival (DFS) of postoperative p-TNM stage I, II, and III were 12.7% and 104.8 months, 26.5% and 89.3 months, and 55.5% and 57.3 months, respectively. The 5-y CRR and DFS of preoperative Low-risk (RRS 0-1score), Middle-risk (RRS 2-3scores), and High-risk (RRS 4-5scores) groups were 13.9% and 101.1 months, 40.9% and 75.5 months, and 70.2% and 41.1 months. The AUC (area under ROC curve) of RRS system was not inferior to that of TNM staging system (0.713 vs. 0.666; P= 0.093). The AUC (0.770) and C-index value (0.721) of RRS-TNM model were significantly better than both RRS and TNM staging system (P< 0.001). CONCLUSIONS: The RRS system accurately identifies CRC patients with high-risk recurrence preoperatively. Constructing a nomogram using the RRS system and TNM staging significantly improves the accuracy of staging and prognosis prediction, which is of great clinical significance for individualized clinical treatment and follow-up of CRC.