Prognostic Prediction Models for Colorectal Cancer Patients After Curative Resection

Abstract

To develop a prediction tool for recurrence and survival in colorectal cancer (CRC) patients following surgically curative resections. We developed a reliable prediction model for CRC patients after surgically curative resections. Using clinicopathologic factors, novel prediction models were constructed with the area under the curve (AUC) of 0.841 and 0.876 for DFS and CSS, respectively. Between January 2004 and December 2007, 376 CRC patients were investigated at the Osaka Medical Center for Cancer and Cardiovascular Diseases. Patients with at least 1 of the following criteria were excluded: preoperative treatment, synchronous distant metastasis, noncurative resection, and incomplete follow-up after operation. All patients were retrospectively analyzed. A Cox proportional hazards model was used to develop a prediction model for disease-free survival (DFS) and cancer-specific survival (CSS). In univariate and multivariate analyses of clinicopathologic factors, the following factors had significant correlation with DFS and CSS: tumor location, preoperative serum carcinoembryonic antigen (CEA), pathologically defined tumor invasion, and lymph node metastasis. Using these variables, novel prediction models were constructed by the logistic regression model with AUC of 0.840 and 0.876 for DFS and CSS, respectively. The prediction models were validated by external datasets in an independent patient group. This study showed novel and reliable personalized prognostic models, integrating not only TNM factors but also tumor location and preoperative serum CEA to predict patient prognosis. These individualized prediction models could help clinicians in the treatment of postoperative CRC patients.