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

Objective To develop a prediction model for recurrence by incorporating radiological and clinicopathological prognostic factors in rectal cancer patients. Methods 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). Results 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). Conclusions 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.

Automated summary

A prediction model for recurrence in rectal cancer patients was developed by incorporating radiological and clinicopathological prognostic factors, showing superior risk stratification performance compared to traditional TNM staging. The model identified high-risk group effectively for more active surveillance.