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A Routine Laboratory Data–Based Model for Predicting Recurrence After Curative Resection of Stage II Colorectal Cancer

Zhong Ye, Chun Wang, Limin Guo, Juan P. Palazzo, Zhixing Han, Yinzhi Lai, Jing Jiang, James A. Posey, Atrayee Basu Mallick, Bingshan Li, Li Jiang, and Hushan Yang

Background: Use of chemotherapy in stage II colorectal cancer (CRC) is controversial because it improves survival only in some patients. We aimed to develop a statistical model using routine and readily available blood tests to predict the prognosis of patients with stage II CRC and to identify which patients are likely to benefit from chemotherapy. Methods: We divided 422 patients with stage II CRC into a training and a testing set. The association of routine laboratory variables and disease-free survival (DFS) was analyzed. A prognostic model was developed incorporating clinically relevant laboratory variables with demographic and tumor characteristics. A prognostic score was derived by calculating the sum of each variable weighted by its regression coefficient in the model. Model performance was evaluated by constructing receiver operating characteristic curves and calculating the area under the curve (AUC). Results: Significant associations were seen between 5 laboratory variables and patient DFS in univariate analyses. After stepwise selection, 3 variables (carcinoembryonic antigen, hemoglobin, creatinine) were retained in the multivariate model with an AUC of 0.75. Compared with patients with a low prognostic score, those with a medium and high prognostic score had a 1.99- and 4.78-fold increased risk of recurrence, respectively. The results from the training set were validated in the testing set. Moreover, chemotherapy significantly improved DFS in high-risk patients, but not in low- and medium-risk patients. Conclusions: A routine laboratory variable–based model may help predict DFS of patients with stage II CRC and identify high-risk patients more likely to benefit from chemotherapy.