The development and validation of a predictive model for recurrence in rectal cancer based on radiological and clinicopathological data.

Abstract

To develop a prediction model for recurrence by incorporating radiological and clinicopathological prognostic factors in rectal cancer patients. All radiologic and clinicopathologic data of 489 patients with rectal cancer, retrospectively collected from a single institution between 2009 and 2013, were used to develop a predictive model for recurrence using the Cox regression. The model performance was validated on an independent cohort between 2015 and 2017 (N = 168). Out of 489 derivative patients, 103 showed recurrence after surgery. The prediction model was constructed with the following four significant predictors: distance from anal verge, MR-based extramural venous invasion, pathologic nodal stage, and perineural invasion (HR: 1.69, 2.09, 2.59, 2.29, respectively). Each factor was assigned a risk score corresponding to HR. The derivation and validation cohort were classified by sum of risk scores into 3 groups: low, intermediate, and high risk. Each of these groups showed significantly different recurrence rates (derivation cohort: 13.4%, 35.3%, 61.5 %; validation cohort: 6.2%, 23.7%, 64.7%). Our new model showed better performance in risk stratification, compared to recurrence rates of tumor node metastasis (TNM) staging in the validation cohort (stage I: 3.6%, II: 12%, III: 30.2%). The area under the receiver operating characteristic curve of the new prediction model was higher than TNM staging at 3-year recurrence in the validation cohort (0.853 vs. 0.731; p = .009). The new risk prediction model was strongly correlated with a recurrence rate after rectal cancer surgery and excellent for selection of high-risk group, who needs more active surveillance. • Multivariate analysis revealed four significant risk factors to be MR-based extramural venous invasion, perineural invasion, nodal metastasis, and the short distance from anal verge among the radiologic and clinicopathologic data. • Our new recurrence prediction model including radiologic data as well as clinicopathologic data showed high predictive performance of disease recurrence. • This model can be used as a comprehensive approach to evaluate individual prognosis and helpful for the selection of highly recurrent group who needs more active surveillance.